Diff of the two buildlogs:

--
--- b1/build.log	2025-09-24 06:45:52.816686689 +0000
+++ b2/build.log	2025-09-23 12:45:46.465768287 +0000
@@ -1,8 +1,2556 @@
 I: pbuilder: network access will be disabled during build
-I: Current time: Tue Sep 23 00:45:52 -12 2025
-I: pbuilder-time-stamp: 1758631552
+I: Current time: Tue Oct 27 07:57:42 +14 2026
+I: pbuilder-time-stamp: 1793037462
 I: Building the build Environment
 I: extracting base tarball [/var/cache/pbuilder/unstable-reproducible-base.tgz]
+tar: ./build: time stamp 2027-11-29 10:28:53 is 34396270.624960928 s in the future
+tar: ./dev/pts: time stamp 2027-11-29 10:28:25 is 34396242.624801424 s in the future
+tar: ./dev/full: time stamp 2027-11-29 10:28:25 is 34396242.624754208 s in the future
+tar: ./dev/zero: time stamp 2027-11-29 10:28:25 is 34396242.624656847 s in the future
+tar: ./dev/tty: time stamp 2027-11-29 10:28:25 is 34396242.624606757 s in the future
+tar: ./dev/random: time stamp 2027-11-29 10:28:25 is 34396242.624490243 s in the future
+tar: ./dev/ptmx: time stamp 2027-11-29 10:28:25 is 34396242.624405594 s in the future
+tar: ./dev/urandom: time stamp 2027-11-29 10:28:25 is 34396242.62432763 s in the future
+tar: ./dev/console: time stamp 2027-11-29 10:28:25 is 34396242.624281599 s in the future
+tar: ./dev/null: time stamp 2027-11-29 10:28:25 is 34396242.62423825 s in the future
+tar: ./etc/group: time stamp 2027-11-29 10:28:55 is 34396272.624065011 s in the future
+tar: ./etc/security/opasswd: time stamp 2027-11-29 10:28:30 is 34396247.623710544 s in the future
+tar: ./etc/security: time stamp 2027-11-29 10:28:30 is 34396247.623242979 s in the future
+tar: ./etc/shadow: time stamp 2027-11-29 10:28:55 is 34396272.621628202 s in the future
+tar: ./etc/apt/preferences.d/reproducible: time stamp 2027-11-29 10:29:50 is 34396327.62130258 s in the future
+tar: ./etc/apt/preferences.d: time stamp 2027-11-29 10:29:50 is 34396327.621270408 s in the future
+tar: ./etc/apt/trusted.gpg.d/reproducible.asc: time stamp 2027-11-29 10:29:50 is 34396327.621078911 s in the future
+tar: ./etc/apt/trusted.gpg.d: time stamp 2027-11-29 10:29:50 is 34396327.620580006 s in the future
+tar: ./etc/apt/sources.list: time stamp 2027-11-29 10:29:23 is 34396300.620524044 s in the future
+tar: ./etc/apt/apt.conf.d/15pbuilder: time stamp 2027-11-29 10:28:53 is 34396270.620350317 s in the future
+tar: ./etc/apt/apt.conf.d/80proxy: time stamp 2027-11-29 10:29:22 is 34396299.620276162 s in the future
+tar: ./etc/apt/apt.conf.d: time stamp 2027-11-29 10:29:22 is 34396299.620248126 s in the future
+tar: ./etc/apt/sources.list.d/reproducible.list: time stamp 2027-11-29 10:29:50 is 34396327.620146497 s in the future
+tar: ./etc/apt/sources.list.d: time stamp 2027-11-29 10:29:50 is 34396327.620113296 s in the future
+tar: ./etc/apt: time stamp 2027-11-29 10:29:23 is 34396300.620024698 s in the future
+tar: ./etc/fstab: time stamp 2027-11-29 10:28:24 is 34396241.619875864 s in the future
+tar: ./etc/ld.so.cache: time stamp 2027-11-29 10:29:00 is 34396277.619807649 s in the future
+tar: ./etc/subuid: time stamp 2027-11-29 10:28:25 is 34396242.619727279 s in the future
+tar: ./etc/pam.d/common-session-noninteractive: time stamp 2027-11-29 10:28:30 is 34396247.619538547 s in the future
+tar: ./etc/pam.d/common-auth: time stamp 2027-11-29 10:28:30 is 34396247.619138907 s in the future
+tar: ./etc/pam.d/common-session: time stamp 2027-11-29 10:28:30 is 34396247.619019738 s in the future
+tar: ./etc/pam.d/common-account: time stamp 2027-11-29 10:28:30 is 34396247.618950456 s in the future
+tar: ./etc/pam.d/common-password: time stamp 2027-11-29 10:28:30 is 34396247.618802733 s in the future
+tar: ./etc/pam.d: time stamp 2027-11-29 10:28:55 is 34396272.618779231 s in the future
+tar: ./etc/dpkg/dpkg.cfg.d/02speedup: time stamp 2027-11-29 10:29:23 is 34396300.61856502 s in the future
+tar: ./etc/dpkg/dpkg.cfg.d: time stamp 2027-11-29 10:29:23 is 34396300.618532457 s in the future
+tar: ./etc/dpkg/origins/default: time stamp 2027-11-29 10:28:25 is 34396242.618449649 s in the future
+tar: ./etc/dpkg/origins: time stamp 2027-11-29 10:28:29 is 34396246.618370852 s in the future
+tar: ./etc/dpkg: time stamp 2027-11-29 10:28:49 is 34396266.618202784 s in the future
+tar: ./etc/logrotate.d: time stamp 2027-11-29 10:29:00 is 34396277.617971835 s in the future
+tar: ./etc/environment: time stamp 2027-11-29 10:28:30 is 34396247.617918484 s in the future
+tar: ./etc/default: time stamp 2027-11-29 10:28:55 is 34396272.617694871 s in the future
+tar: ./etc/ld.so.conf.d: time stamp 2027-11-29 10:29:00 is 34396277.617385427 s in the future
+tar: ./etc/passwd: time stamp 2027-11-29 10:28:55 is 34396272.617220579 s in the future
+tar: ./etc/group-: time stamp 2027-11-29 10:28:25 is 34396242.617100304 s in the future
+tar: ./etc/gshadow: time stamp 2027-11-29 10:28:55 is 34396272.616962904 s in the future
+tar: ./etc/shells: time stamp 2027-11-29 10:28:29 is 34396246.616866151 s in the future
+tar: ./etc/passwd-: time stamp 2027-11-29 10:28:25 is 34396242.616689595 s in the future
+tar: ./etc/.pwd.lock: time stamp 2027-11-29 10:28:55 is 34396272.616636703 s in the future
+tar: ./etc/cron.daily: time stamp 2027-11-29 10:29:00 is 34396277.616333235 s in the future
+tar: ./etc/selinux: time stamp 2027-11-29 10:28:55 is 34396272.616220869 s in the future
+tar: ./etc/skel: time stamp 2027-11-29 10:28:29 is 34396246.615882803 s in the future
+tar: ./etc/update-motd.d: time stamp 2027-11-29 10:28:29 is 34396246.615758706 s in the future
+tar: ./etc/opt: time stamp 2027-11-29 10:28:25 is 34396242.615358475 s in the future
+tar: ./etc/subgid: time stamp 2027-11-29 10:28:25 is 34396242.615299038 s in the future
+tar: ./etc/perl/Net: time stamp 2027-11-29 10:28:49 is 34396266.615006184 s in the future
+tar: ./etc/perl: time stamp 2027-11-29 10:28:48 is 34396265.614980148 s in the future
+tar: ./etc/kernel: time stamp 2027-11-29 10:28:31 is 34396248.614878413 s in the future
+tar: ./etc/terminfo: time stamp 2027-11-29 10:28:29 is 34396246.614735623 s in the future
+tar: ./media: time stamp 2027-11-29 10:28:25 is 34396242.614580407 s in the future
+tar: ./mnt: time stamp 2027-11-29 10:28:25 is 34396242.614495794 s in the future
+tar: ./opt: time stamp 2027-11-29 10:28:25 is 34396242.614429907 s in the future
+tar: ./root: time stamp 2027-11-29 10:28:25 is 34396242.614216308 s in the future
+tar: ./run/lock: time stamp 2027-11-29 10:28:25 is 34396242.614112105 s in the future
+tar: ./srv: time stamp 2027-11-29 10:28:25 is 34396242.613977813 s in the future
+tar: ./usr/libexec/gcc/x86_64-linux-gnu/15: time stamp 2027-11-29 10:28:45 is 34396252.80326538 s in the future
+tar: ./usr/libexec/gcc/x86_64-linux-gnu: time stamp 2027-11-29 10:28:31 is 34396238.803219975 s in the future
+tar: ./usr/libexec/gcc: time stamp 2027-11-29 10:28:31 is 34396238.803200099 s in the future
+tar: ./usr/libexec/dpkg: time stamp 2027-11-29 10:28:27 is 34396234.803038816 s in the future
+tar: ./usr/libexec/coreutils: time stamp 2027-11-29 10:28:26 is 34396233.802911829 s in the future
+tar: ./usr/libexec: time stamp 2027-11-29 10:28:31 is 34396238.802886665 s in the future
+tar: ./usr/lib/gcc/x86_64-linux-gnu/15/include/sanitizer: time stamp 2027-11-29 10:28:46 is 34396253.743767958 s in the future
+tar: ./usr/lib/gcc/x86_64-linux-gnu/15/include: time stamp 2027-11-29 10:28:46 is 34396253.73477338 s in the future
+tar: ./usr/lib/gcc/x86_64-linux-gnu/15/plugin: time stamp 2027-11-29 10:28:45 is 34396252.652001445 s in the future
+tar: ./usr/lib/tmpfiles.d: time stamp 2027-11-29 10:28:55 is 34396262.624278626 s in the future
+tar: ./usr/lib/apt/solvers: time stamp 2027-11-29 10:28:31 is 34396238.623950427 s in the future
+tar: ./usr/lib/apt/methods: time stamp 2027-11-29 10:28:31 is 34396238.615669475 s in the future
+tar: ./usr/lib/mime/packages: time stamp 2027-11-29 10:28:29 is 34396236.614744712 s in the future
+tar: ./usr/lib/init: time stamp 2027-11-29 10:28:29 is 34396236.614593905 s in the future
+tar: ./usr/lib/dpkg/methods/apt: time stamp 2027-11-29 10:28:31 is 34396238.614303653 s in the future
+tar: ./usr/lib/dpkg/methods: time stamp 2027-11-29 10:28:31 is 34396238.614280589 s in the future
+tar: ./usr/lib/dpkg: time stamp 2027-11-29 10:28:31 is 34396238.614263336 s in the future
+tar: ./usr/lib/linux/uapi/sh/asm: time stamp 2027-11-29 10:28:48 is 34396255.612778596 s in the future
+tar: ./usr/lib/linux/uapi/sh: time stamp 2027-11-29 10:28:48 is 34396255.612751375 s in the future
+tar: ./usr/lib/linux/uapi/s390/asm: time stamp 2027-11-29 10:28:48 is 34396255.609638579 s in the future
+tar: ./usr/lib/linux/uapi/s390: time stamp 2027-11-29 10:28:48 is 34396255.609609768 s in the future
+tar: ./usr/lib/linux/uapi/mips/asm: time stamp 2027-11-29 10:28:48 is 34396255.607304627 s in the future
+tar: ./usr/lib/linux/uapi/mips: time stamp 2027-11-29 10:28:48 is 34396255.607270215 s in the future
+tar: ./usr/lib/linux/uapi/powerpc/asm: time stamp 2027-11-29 10:28:48 is 34396255.604676407 s in the future
+tar: ./usr/lib/linux/uapi/powerpc: time stamp 2027-11-29 10:28:48 is 34396255.604642754 s in the future
+tar: ./usr/lib/linux/uapi/riscv/asm/vendor: time stamp 2027-11-29 10:28:48 is 34396255.603408432 s in the future
+tar: ./usr/lib/linux/uapi/riscv/asm: time stamp 2027-11-29 10:28:48 is 34396255.602793645 s in the future
+tar: ./usr/lib/linux/uapi/riscv: time stamp 2027-11-29 10:28:48 is 34396255.602766576 s in the future
+tar: ./usr/lib/linux/uapi/x86/asm: time stamp 2027-11-29 10:28:48 is 34396255.599552885 s in the future
+tar: ./usr/lib/linux/uapi/x86: time stamp 2027-11-29 10:28:48 is 34396255.599520184 s in the future
+tar: ./usr/lib/linux/uapi/arm64/asm: time stamp 2027-11-29 10:28:48 is 34396255.597763517 s in the future
+tar: ./usr/lib/linux/uapi/arm64: time stamp 2027-11-29 10:28:48 is 34396255.59773396 s in the future
+tar: ./usr/lib/linux/uapi/parisc/asm: time stamp 2027-11-29 10:28:48 is 34396255.595965027 s in the future
+tar: ./usr/lib/linux/uapi/parisc: time stamp 2027-11-29 10:28:48 is 34396255.595936062 s in the future
+tar: ./usr/lib/linux/uapi/sparc/asm: time stamp 2027-11-29 10:28:48 is 34396255.593505881 s in the future
+tar: ./usr/lib/linux/uapi/sparc: time stamp 2027-11-29 10:28:48 is 34396255.593470328 s in the future
+tar: ./usr/lib/linux/uapi/loongarch/asm: time stamp 2027-11-29 10:28:48 is 34396255.591806868 s in the future
+tar: ./usr/lib/linux/uapi/loongarch: time stamp 2027-11-29 10:28:48 is 34396255.591779201 s in the future
+tar: ./usr/lib/linux/uapi/alpha/asm: time stamp 2027-11-29 10:28:48 is 34396255.589947124 s in the future
+tar: ./usr/lib/linux/uapi/alpha: time stamp 2027-11-29 10:28:48 is 34396255.589919976 s in the future
+tar: ./usr/lib/linux/uapi/arm/asm: time stamp 2027-11-29 10:28:48 is 34396255.588292154 s in the future
+tar: ./usr/lib/linux/uapi/arm: time stamp 2027-11-29 10:28:48 is 34396255.588268788 s in the future
+tar: ./usr/lib/linux/uapi/m68k/asm: time stamp 2027-11-29 10:28:48 is 34396255.58647344 s in the future
+tar: ./usr/lib/linux/uapi/m68k: time stamp 2027-11-29 10:28:48 is 34396255.586444975 s in the future
+tar: ./usr/lib/linux/uapi: time stamp 2027-11-29 10:28:48 is 34396255.58642869 s in the future
+tar: ./usr/lib/linux: time stamp 2027-11-29 10:28:48 is 34396255.586413447 s in the future
+tar: ./usr/lib/lsb/init-functions.d: time stamp 2027-11-29 10:28:29 is 34396236.586137674 s in the future
+tar: ./usr/lib/lsb: time stamp 2027-11-29 10:28:29 is 34396236.586112038 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/gconv/gconv-modules.d: time stamp 2027-11-29 10:28:27 is 34396234.525624519 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/gconv: time stamp 2027-11-29 10:28:27 is 34396234.4662384 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/pkgconfig: time stamp 2027-11-29 10:28:46 is 34396253.397383776 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/audit: time stamp 2027-11-29 10:28:46 is 34396253.265995315 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/security: time stamp 2027-11-29 10:28:28 is 34396235.208577697 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/gprofng: time stamp 2027-11-29 10:28:46 is 34396253.174540616 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/ossl-modules: time stamp 2027-11-29 10:28:28 is 34396235.162402886 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/libfakeroot: time stamp 2027-11-29 10:28:56 is 34396263.161247543 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/Carp: time stamp 2027-11-29 10:28:29 is 34396235.790387212 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/warnings: time stamp 2027-11-29 10:28:29 is 34396235.790193365 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/Getopt/Long: time stamp 2027-11-29 10:28:29 is 34396235.789926637 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/Getopt: time stamp 2027-11-29 10:28:29 is 34396235.789614211 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/IPC: time stamp 2027-11-29 10:28:29 is 34396235.789154042 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/Text: time stamp 2027-11-29 10:28:29 is 34396235.788914399 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/Scalar: time stamp 2027-11-29 10:28:29 is 34396235.788635632 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/NFCQC: time stamp 2027-11-29 10:28:29 is 34396235.788425635 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/ExtPict: time stamp 2027-11-29 10:28:29 is 34396235.788344755 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/EPres: time stamp 2027-11-29 10:28:29 is 34396235.788251673 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/PCM: time stamp 2027-11-29 10:28:29 is 34396235.788153852 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Bc: time stamp 2027-11-29 10:28:29 is 34396235.787609106 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Hex: time stamp 2027-11-29 10:28:29 is 34396235.787519699 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Bpt: time stamp 2027-11-29 10:28:29 is 34396235.787344143 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/IdStatus: time stamp 2027-11-29 10:28:29 is 34396235.787180187 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/SB: time stamp 2027-11-29 10:28:29 is 34396235.786545618 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/CI: time stamp 2027-11-29 10:28:29 is 34396235.78643334 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/GCB: time stamp 2027-11-29 10:28:29 is 34396235.786011545 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Nv: time stamp 2027-11-29 10:28:29 is 34396235.783334607 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Perl: time stamp 2027-11-29 10:28:29 is 34396235.782192755 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Ccc: time stamp 2027-11-29 10:28:29 is 34396235.781690938 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Lower: time stamp 2027-11-29 10:28:29 is 34396235.781584429 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Jg: time stamp 2027-11-29 10:28:29 is 34396235.780655505 s in the future
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+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Hyphen: time stamp 2027-11-29 10:28:29 is 34396235.779911156 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Dash: time stamp 2027-11-29 10:28:29 is 34396235.779799959 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Math: time stamp 2027-11-29 10:28:29 is 34396235.77971497 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/BidiC: time stamp 2027-11-29 10:28:29 is 34396235.779633537 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/CWCM: time stamp 2027-11-29 10:28:29 is 34396235.779541539 s in the future
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+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Lb: time stamp 2027-11-29 10:28:29 is 34396235.773978055 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/IDC: time stamp 2027-11-29 10:28:29 is 34396235.773824723 s in the future
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+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/Alpha: time stamp 2027-11-29 10:28:29 is 34396235.773382254 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/WB: time stamp 2027-11-29 10:28:29 is 34396235.772794208 s in the future
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+tar: ./usr/lib/x86_64-linux-gnu/perl-base/unicore/lib/SD: time stamp 2027-11-29 10:28:29 is 34396235.772609742 s in the future
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+tar: ./usr/lib/locale/bi_VU: time stamp 2027-11-29 10:28:59 is 34396262.035948082 s in the future
+tar: ./usr/lib/locale/be_BY.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035924149 s in the future
+tar: ./usr/lib/locale/es_US/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035900604 s in the future
+tar: ./usr/lib/locale/es_US: time stamp 2027-11-29 10:28:59 is 34396262.035877458 s in the future
+tar: ./usr/lib/locale/ro_RO.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035851974 s in the future
+tar: ./usr/lib/locale/et_EE.iso885915: time stamp 2027-11-29 10:28:59 is 34396262.035828127 s in the future
+tar: ./usr/lib/locale/en_HK.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035804502 s in the future
+tar: ./usr/lib/locale/en_HK.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035781704 s in the future
+tar: ./usr/lib/locale/es_ES@euro/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035758163 s in the future
+tar: ./usr/lib/locale/es_ES@euro: time stamp 2027-11-29 10:28:59 is 34396262.035735359 s in the future
+tar: ./usr/lib/locale/it_IT: time stamp 2027-11-29 10:28:59 is 34396262.035711979 s in the future
+tar: ./usr/lib/locale/ps_AF: time stamp 2027-11-29 10:28:59 is 34396262.035688083 s in the future
+tar: ./usr/lib/locale/en_ZW/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035664436 s in the future
+tar: ./usr/lib/locale/en_ZW: time stamp 2027-11-29 10:28:59 is 34396262.035641396 s in the future
+tar: ./usr/lib/locale/ks_IN: time stamp 2027-11-29 10:28:59 is 34396262.03561749 s in the future
+tar: ./usr/lib/locale/wa_BE: time stamp 2027-11-29 10:28:59 is 34396262.035594034 s in the future
+tar: ./usr/lib/locale/ka_GE.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035569486 s in the future
+tar: ./usr/lib/locale/bg_BG.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035550592 s in the future
+tar: ./usr/lib/locale/doi_IN: time stamp 2027-11-29 10:28:59 is 34396262.03553083 s in the future
+tar: ./usr/lib/locale/sa_IN: time stamp 2027-11-29 10:28:59 is 34396262.035510648 s in the future
+tar: ./usr/lib/locale/gez_ET@abegede/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035468766 s in the future
+tar: ./usr/lib/locale/gez_ET@abegede: time stamp 2027-11-29 10:28:59 is 34396262.035438574 s in the future
+tar: ./usr/lib/locale/ru_UA.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035408683 s in the future
+tar: ./usr/lib/locale/ru_UA.utf8: time stamp 2027-11-29 10:28:59 is 34396262.03536284 s in the future
+tar: ./usr/lib/locale/hak_TW: time stamp 2027-11-29 10:28:59 is 34396262.035341179 s in the future
+tar: ./usr/lib/locale/es_CL/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035323019 s in the future
+tar: ./usr/lib/locale/es_CL: time stamp 2027-11-29 10:28:59 is 34396262.035304654 s in the future
+tar: ./usr/lib/locale/sv_SE/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035286804 s in the future
+tar: ./usr/lib/locale/sv_SE: time stamp 2027-11-29 10:28:59 is 34396262.035269366 s in the future
+tar: ./usr/lib/locale/gez_ER@abegede/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035251452 s in the future
+tar: ./usr/lib/locale/gez_ER@abegede: time stamp 2027-11-29 10:28:59 is 34396262.035234037 s in the future
+tar: ./usr/lib/locale/ar_KW/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035215822 s in the future
+tar: ./usr/lib/locale/ar_KW: time stamp 2027-11-29 10:28:59 is 34396262.035198552 s in the future
+tar: ./usr/lib/locale/mg_MG: time stamp 2027-11-29 10:28:59 is 34396262.035180645 s in the future
+tar: ./usr/lib/locale/lij_IT: time stamp 2027-11-29 10:28:59 is 34396262.035162969 s in the future
+tar: ./usr/lib/locale/de_BE.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035144504 s in the future
+tar: ./usr/lib/locale/de_BE.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035126761 s in the future
+tar: ./usr/lib/locale/oc_FR: time stamp 2027-11-29 10:28:59 is 34396262.035108263 s in the future
+tar: ./usr/lib/locale/rif_MA: time stamp 2027-11-29 10:28:59 is 34396262.03509026 s in the future
+tar: ./usr/lib/locale/zu_ZA.utf8: time stamp 2027-11-29 10:28:59 is 34396262.035071995 s in the future
+tar: ./usr/lib/locale/raj_IN/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035055155 s in the future
+tar: ./usr/lib/locale/raj_IN: time stamp 2027-11-29 10:28:59 is 34396262.03503938 s in the future
+tar: ./usr/lib/locale/ig_NG: time stamp 2027-11-29 10:28:59 is 34396262.035022628 s in the future
+tar: ./usr/lib/locale/it_CH.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.035006208 s in the future
+tar: ./usr/lib/locale/it_CH.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034990008 s in the future
+tar: ./usr/lib/locale/ar_OM/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.03497293 s in the future
+tar: ./usr/lib/locale/ar_OM: time stamp 2027-11-29 10:28:59 is 34396262.034956262 s in the future
+tar: ./usr/lib/locale/en_AU: time stamp 2027-11-29 10:28:59 is 34396262.034939932 s in the future
+tar: ./usr/lib/locale/mjw_IN/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034922719 s in the future
+tar: ./usr/lib/locale/mjw_IN: time stamp 2027-11-29 10:28:59 is 34396262.034905863 s in the future
+tar: ./usr/lib/locale/nl_BE.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034889633 s in the future
+tar: ./usr/lib/locale/nl_BE.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034872524 s in the future
+tar: ./usr/lib/locale/aa_ET/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034855998 s in the future
+tar: ./usr/lib/locale/aa_ET: time stamp 2027-11-29 10:28:59 is 34396262.034839383 s in the future
+tar: ./usr/lib/locale/gv_GB.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034822702 s in the future
+tar: ./usr/lib/locale/gv_GB.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034806461 s in the future
+tar: ./usr/lib/locale/ml_IN: time stamp 2027-11-29 10:28:59 is 34396262.034790061 s in the future
+tar: ./usr/lib/locale/id_ID.utf8: time stamp 2027-11-29 10:28:59 is 34396262.03477349 s in the future
+tar: ./usr/lib/locale/gv_GB: time stamp 2027-11-29 10:28:59 is 34396262.034756564 s in the future
+tar: ./usr/lib/locale/en_NZ/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034740271 s in the future
+tar: ./usr/lib/locale/en_NZ: time stamp 2027-11-29 10:28:59 is 34396262.03472289 s in the future
+tar: ./usr/lib/locale/zh_HK.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034705841 s in the future
+tar: ./usr/lib/locale/wo_SN: time stamp 2027-11-29 10:28:59 is 34396262.034686361 s in the future
+tar: ./usr/lib/locale/tl_PH.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034668616 s in the future
+tar: ./usr/lib/locale/tl_PH.utf8: time stamp 2027-11-29 10:28:59 is 34396262.0346523 s in the future
+tar: ./usr/lib/locale/ar_LB.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034635769 s in the future
+tar: ./usr/lib/locale/ar_LB.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034619657 s in the future
+tar: ./usr/lib/locale/gl_ES: time stamp 2027-11-29 10:28:59 is 34396262.034603415 s in the future
+tar: ./usr/lib/locale/ms_MY.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034585732 s in the future
+tar: ./usr/lib/locale/ms_MY.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034568884 s in the future
+tar: ./usr/lib/locale/mg_MG.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034552447 s in the future
+tar: ./usr/lib/locale/en_IE@euro/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034535989 s in the future
+tar: ./usr/lib/locale/en_IE@euro: time stamp 2027-11-29 10:28:59 is 34396262.034519105 s in the future
+tar: ./usr/lib/locale/ar_SD/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034503271 s in the future
+tar: ./usr/lib/locale/ar_SD: time stamp 2027-11-29 10:28:59 is 34396262.034486995 s in the future
+tar: ./usr/lib/locale/ar_JO/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034470462 s in the future
+tar: ./usr/lib/locale/ar_JO: time stamp 2027-11-29 10:28:59 is 34396262.034454478 s in the future
+tar: ./usr/lib/locale/so_KE: time stamp 2027-11-29 10:28:59 is 34396262.034438052 s in the future
+tar: ./usr/lib/locale/cmn_TW: time stamp 2027-11-29 10:28:59 is 34396262.034421494 s in the future
+tar: ./usr/lib/locale/ms_MY/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034404853 s in the future
+tar: ./usr/lib/locale/ms_MY: time stamp 2027-11-29 10:28:59 is 34396262.034388516 s in the future
+tar: ./usr/lib/locale/da_DK: time stamp 2027-11-29 10:28:59 is 34396262.034372068 s in the future
+tar: ./usr/lib/locale/pa_IN: time stamp 2027-11-29 10:28:59 is 34396262.034355728 s in the future
+tar: ./usr/lib/locale/ar_IN/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034339713 s in the future
+tar: ./usr/lib/locale/ar_IN: time stamp 2027-11-29 10:28:59 is 34396262.034323177 s in the future
+tar: ./usr/lib/locale/pap_AW: time stamp 2027-11-29 10:28:59 is 34396262.034306606 s in the future
+tar: ./usr/lib/locale/ha_NG: time stamp 2027-11-29 10:28:59 is 34396262.034290154 s in the future
+tar: ./usr/lib/locale/ru_RU.cp1251: time stamp 2027-11-29 10:28:59 is 34396262.034273825 s in the future
+tar: ./usr/lib/locale/gl_ES.utf8: time stamp 2027-11-29 10:28:59 is 34396262.03425753 s in the future
+tar: ./usr/lib/locale/fr_LU@euro/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034240942 s in the future
+tar: ./usr/lib/locale/fr_LU@euro: time stamp 2027-11-29 10:28:59 is 34396262.034224782 s in the future
+tar: ./usr/lib/locale/yi_US.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034208099 s in the future
+tar: ./usr/lib/locale/ru_RU.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034191508 s in the future
+tar: ./usr/lib/locale/yuw_PG: time stamp 2027-11-29 10:28:59 is 34396262.034174847 s in the future
+tar: ./usr/lib/locale/uz_UZ: time stamp 2027-11-29 10:28:59 is 34396262.034158514 s in the future
+tar: ./usr/lib/locale/nl_AW/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034142162 s in the future
+tar: ./usr/lib/locale/nl_AW: time stamp 2027-11-29 10:28:59 is 34396262.034126204 s in the future
+tar: ./usr/lib/locale/ar_SD.utf8/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.034109643 s in the future
+tar: ./usr/lib/locale/ar_SD.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034093852 s in the future
+tar: ./usr/lib/locale/crh_RU: time stamp 2027-11-29 10:28:59 is 34396262.034076893 s in the future
+tar: ./usr/lib/locale/fr_CA: time stamp 2027-11-29 10:28:59 is 34396262.034059207 s in the future
+tar: ./usr/lib/locale/kl_GL.utf8: time stamp 2027-11-29 10:28:59 is 34396262.034036577 s in the future
+tar: ./usr/lib/locale/cs_CZ: time stamp 2027-11-29 10:28:59 is 34396262.034016328 s in the future
+tar: ./usr/lib/locale/eu_FR/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.033968744 s in the future
+tar: ./usr/lib/locale/eu_FR: time stamp 2027-11-29 10:28:59 is 34396262.033948194 s in the future
+tar: ./usr/lib/locale/am_ET: time stamp 2027-11-29 10:28:59 is 34396262.033929751 s in the future
+tar: ./usr/lib/locale/ru_RU: time stamp 2027-11-29 10:28:59 is 34396262.033906842 s in the future
+tar: ./usr/lib/locale/en_IE/LC_MESSAGES: time stamp 2027-11-29 10:28:59 is 34396262.033886328 s in the future
+tar: ./usr/lib/locale/en_IE: time stamp 2027-11-29 10:28:59 is 34396262.033844915 s in the future
+tar: ./usr/lib/locale/hsb_DE: time stamp 2027-11-29 10:28:59 is 34396262.033821173 s in the future
+tar: ./usr/lib/locale: time stamp 2027-11-29 10:28:59 is 34396262.03379982 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl/cross-config-5.40.1: time stamp 2027-11-29 10:28:47 is 34396250.033769629 s in the future
+tar: ./usr/lib/x86_64-linux-gnu/perl: time stamp 2027-11-29 10:28:47 is 34396250.033746189 s in the future
+tar: ./usr/lib/x86_64-linux-gnu: time stamp 2027-11-29 10:28:56 is 34396259.033715545 s in the future
+tar: ./usr/lib/apt/planners: time stamp 2027-11-29 10:28:31 is 34396234.033694228 s in the future
+tar: ./usr/lib/apt: time stamp 2027-11-29 10:28:31 is 34396234.033663037 s in the future
+tar: ./usr/lib/gcc/x86_64-linux-gnu/15: time stamp 2027-11-29 10:28:47 is 34396250.033641147 s in the future
+tar: ./usr/lib/gcc/x86_64-linux-gnu: time stamp 2027-11-29 10:28:41 is 34396244.033612202 s in the future
+tar: ./usr/lib/gcc: time stamp 2027-11-29 10:28:41 is 34396244.033591595 s in the future
+tar: ./usr/lib/compat-ld: time stamp 2027-11-29 10:28:31 is 34396234.033562535 s in the future
+tar: ./usr/lib: time stamp 2027-11-29 10:28:49 is 34396252.033543596 s in the future
+tar: ./usr/bin: time stamp 2027-11-29 10:29:00 is 34396263.033515183 s in the future
+tar: ./usr: time stamp 2027-11-29 10:28:25 is 34396228.033494736 s in the future
+tar: ./etc/systemd/system/timers.target.wants: time stamp 2027-11-29 10:28:49 is 34396252.033451149 s in the future
+tar: ./etc/systemd/system: time stamp 2027-11-29 10:28:25 is 34396228.033429103 s in the future
+tar: ./etc/alternatives: time stamp 2027-11-29 10:29:00 is 34396263.033385098 s in the future
 I: copying local configuration
 W: --override-config is not set; not updating apt.conf Read the manpage for details.
 I: mounting /proc filesystem
@@ -48,53 +2596,85 @@
 dpkg-source: info: applying 1079789_ignore_arm64_nonconvergence.patch
 I: Not using root during the build.
 I: Installing the build-deps
-I: user script /srv/workspace/pbuilder/974014/tmp/hooks/D02_print_environment starting
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/D01_modify_environment starting
+debug: Running on ionos5-amd64.
+I: Changing host+domainname to test build reproducibility
+I: Adding a custom variable just for the fun of it...
+I: Changing /bin/sh to bash
+'/bin/sh' -> '/bin/bash'
+lrwxrwxrwx 1 root root 9 Oct 26 17:57 /bin/sh -> /bin/bash
+I: Setting pbuilder2's login shell to /bin/bash
+I: Setting pbuilder2's GECOS to second user,second room,second work-phone,second home-phone,second other
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/D01_modify_environment finished
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/D02_print_environment starting
 I: set
-  BUILDDIR='/build/reproducible-path'
-  BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other'
-  BUILDUSERNAME='pbuilder1'
-  BUILD_ARCH='amd64'
-  DEBIAN_FRONTEND='noninteractive'
-  DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=40 '
-  DISTRIBUTION='unstable'
-  HOME='/root'
-  HOST_ARCH='amd64'
+  BASH=/bin/sh
+  BASHOPTS=checkwinsize:cmdhist:complete_fullquote:extquote:force_fignore:globasciiranges:globskipdots:hostcomplete:interactive_comments:patsub_replacement:progcomp:promptvars:sourcepath
+  BASH_ALIASES=()
+  BASH_ARGC=()
+  BASH_ARGV=()
+  BASH_CMDS=()
+  BASH_LINENO=([0]="12" [1]="0")
+  BASH_LOADABLES_PATH=/usr/local/lib/bash:/usr/lib/bash:/opt/local/lib/bash:/usr/pkg/lib/bash:/opt/pkg/lib/bash:.
+  BASH_SOURCE=([0]="/tmp/hooks/D02_print_environment" [1]="/tmp/hooks/D02_print_environment")
+  BASH_VERSINFO=([0]="5" [1]="3" [2]="3" [3]="1" [4]="release" [5]="x86_64-pc-linux-gnu")
+  BASH_VERSION='5.3.3(1)-release'
+  BUILDDIR=/build/reproducible-path
+  BUILDUSERGECOS='second user,second room,second work-phone,second home-phone,second other'
+  BUILDUSERNAME=pbuilder2
+  BUILD_ARCH=amd64
+  DEBIAN_FRONTEND=noninteractive
+  DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=42 '
+  DIRSTACK=()
+  DISTRIBUTION=unstable
+  EUID=0
+  FUNCNAME=([0]="Echo" [1]="main")
+  GROUPS=()
+  HOME=/root
+  HOSTNAME=i-capture-the-hostname
+  HOSTTYPE=x86_64
+  HOST_ARCH=amd64
   IFS=' 	
   '
-  INVOCATION_ID='7e3d23b9b1e149e58726e574b54012ea'
-  LANG='C'
-  LANGUAGE='en_US:en'
-  LC_ALL='C'
-  MAIL='/var/mail/root'
-  OPTIND='1'
-  PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games'
-  PBCURRENTCOMMANDLINEOPERATION='build'
-  PBUILDER_OPERATION='build'
-  PBUILDER_PKGDATADIR='/usr/share/pbuilder'
-  PBUILDER_PKGLIBDIR='/usr/lib/pbuilder'
-  PBUILDER_SYSCONFDIR='/etc'
-  PPID='974014'
-  PS1='# '
-  PS2='> '
+  INVOCATION_ID=28d68d33aca2429dbd44ba698b230cab
+  LANG=C
+  LANGUAGE=et_EE:et
+  LC_ALL=C
+  MACHTYPE=x86_64-pc-linux-gnu
+  MAIL=/var/mail/root
+  OPTERR=1
+  OPTIND=1
+  OSTYPE=linux-gnu
+  PATH=/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/i/capture/the/path
+  PBCURRENTCOMMANDLINEOPERATION=build
+  PBUILDER_OPERATION=build
+  PBUILDER_PKGDATADIR=/usr/share/pbuilder
+  PBUILDER_PKGLIBDIR=/usr/lib/pbuilder
+  PBUILDER_SYSCONFDIR=/etc
+  PIPESTATUS=([0]="0")
+  POSIXLY_CORRECT=y
+  PPID=468671
   PS4='+ '
-  PWD='/'
-  SHELL='/bin/bash'
-  SHLVL='2'
-  SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.WhI8rddP/pbuilderrc_GBBI --distribution unstable --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/unstable-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.WhI8rddP/b1 --logfile b1/build.log statsmodels_0.14.5+dfsg-1.dsc'
-  SUDO_GID='111'
-  SUDO_HOME='/var/lib/jenkins'
-  SUDO_UID='106'
-  SUDO_USER='jenkins'
-  TERM='unknown'
-  TZ='/usr/share/zoneinfo/Etc/GMT+12'
-  USER='root'
-  _='/usr/bin/systemd-run'
-  http_proxy='http://46.16.76.132:3128'
+  PWD=/
+  SHELL=/bin/bash
+  SHELLOPTS=braceexpand:errexit:hashall:interactive-comments:posix
+  SHLVL=3
+  SUDO_COMMAND='/usr/bin/timeout -k 24.1h 24h /usr/bin/ionice -c 3 /usr/bin/nice -n 11 /usr/bin/unshare --uts -- /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.WhI8rddP/pbuilderrc_G5PM --distribution unstable --hookdir /etc/pbuilder/rebuild-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/unstable-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.WhI8rddP/b2 --logfile b2/build.log statsmodels_0.14.5+dfsg-1.dsc'
+  SUDO_GID=110
+  SUDO_HOME=/var/lib/jenkins
+  SUDO_UID=105
+  SUDO_USER=jenkins
+  TERM=unknown
+  TZ=/usr/share/zoneinfo/Etc/GMT-14
+  UID=0
+  USER=root
+  _='I: set'
+  http_proxy=http://213.165.73.152:3128
 I: uname -a
-  Linux ionos11-amd64 6.12.48+deb13-amd64 #1 SMP PREEMPT_DYNAMIC Debian 6.12.48-1 (2025-09-20) x86_64 GNU/Linux
+  Linux i-capture-the-hostname 6.12.48+deb13-amd64 #1 SMP PREEMPT_DYNAMIC Debian 6.12.48-1 (2025-09-20) x86_64 GNU/Linux
 I: ls -l /bin
-  lrwxrwxrwx 1 root root 7 Aug 10 12:30 /bin -> usr/bin
-I: user script /srv/workspace/pbuilder/974014/tmp/hooks/D02_print_environment finished
+  lrwxrwxrwx 1 root root 7 Aug 10  2025 /bin -> usr/bin
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/D02_print_environment finished
  -> Attempting to satisfy build-dependencies
  -> Creating pbuilder-satisfydepends-dummy package
 Package: pbuilder-satisfydepends-dummy
@@ -647,7 +3227,7 @@
 Get: 417 http://deb.debian.org/debian unstable/main amd64 r-cran-lmtest amd64 0.9.40-1 [399 kB]
 Get: 418 http://deb.debian.org/debian unstable/main amd64 r-cran-robustbase amd64 0.99-6-1 [3092 kB]
 Get: 419 http://deb.debian.org/debian unstable/main amd64 r-cran-vcd all 1:1.4-13-1 [1287 kB]
-Fetched 524 MB in 16s (32.0 MB/s)
+Fetched 524 MB in 7s (73.2 MB/s)
 Preconfiguring packages ...
 Selecting previously unselected package libtext-charwidth-perl:amd64.
 (Reading database ... 
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(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 19917 files and directories currently installed.)
@@ -1987,8 +4567,8 @@
 Setting up tzdata (2025b-5) ...
 
 Current default time zone: 'Etc/UTC'
-Local time is now:      Tue Sep 23 12:48:49 UTC 2025.
-Universal Time is now:  Tue Sep 23 12:48:49 UTC 2025.
+Local time is now:      Mon Oct 26 17:59:22 UTC 2026.
+Universal Time is now:  Mon Oct 26 17:59:22 UTC 2026.
 Run 'dpkg-reconfigure tzdata' if you wish to change it.
 
 Setting up unicode-data (16.0.0-1) ...
@@ -2373,7 +4953,11 @@
 Building tag database...
  -> Finished parsing the build-deps
 I: Building the package
-I: Running cd /build/reproducible-path/statsmodels-0.14.5+dfsg/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S  > ../statsmodels_0.14.5+dfsg-1_source.changes
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/A99_set_merged_usr starting
+Not re-configuring usrmerge for unstable
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/A99_set_merged_usr finished
+hostname: Name or service not known
+I: Running cd /build/reproducible-path/statsmodels-0.14.5+dfsg/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/i/capture/the/path" HOME="/nonexistent/second-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/i/capture/the/path" HOME="/nonexistent/second-build" dpkg-genchanges -S  > ../statsmodels_0.14.5+dfsg-1_source.changes
 dpkg-buildpackage: info: source package statsmodels
 dpkg-buildpackage: info: source version 0.14.5+dfsg-1
 dpkg-buildpackage: info: source distribution unstable
@@ -2500,1543 +5084,1543 @@
 running build
 running build_py
 creating build/lib.linux-x86_64-cpython-313/statsmodels
-copying statsmodels/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels
+copying statsmodels/_version.py -> build/lib.linux-x86_64-cpython-313/statsmodels
 copying statsmodels/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels
+copying statsmodels/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels
 copying statsmodels/conftest.py -> build/lib.linux-x86_64-cpython-313/statsmodels
-copying statsmodels/_version.py -> build/lib.linux-x86_64-cpython-313/statsmodels
-creating build/lib.linux-x86_64-cpython-313/statsmodels/gam
-copying statsmodels/gam/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
-copying statsmodels/gam/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
-copying statsmodels/gam/gam_penalties.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
-copying statsmodels/gam/generalized_additive_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
-copying statsmodels/gam/smooth_basis.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/x13.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/ar_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/descriptivestats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/varma_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/arima_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/arima_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/stattools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/mlemodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/deterministic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/arma_mle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/seasonal.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/tsatools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/adfvalues.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/_bds.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-copying statsmodels/tsa/coint_tables.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
-creating build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/l1_solvers_common.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/l1_slsqp.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/_screening.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/_parameter_inference.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/wrapper.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/_constraints.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/l1_cvxopt.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/covtype.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/_prediction_inference.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/transform.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/optimizer.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/distributed_estimation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/_penalties.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/elastic_net.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-copying statsmodels/base/_penalized.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
-creating build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/kernel_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/smoothers_lowess_old.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/kernel_density.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/_kernel_base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/kernels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/kernels_asymmetric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/kde.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/smoothers_lowess.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/bandwidths.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-copying statsmodels/nonparametric/kdetools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
-creating build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/aft_el.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/elanova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/originregress.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/descriptive.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-copying statsmodels/emplike/elregress.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
-creating build/lib.linux-x86_64-cpython-313/statsmodels/imputation
-copying statsmodels/imputation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
-copying statsmodels/imputation/ros.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
-copying statsmodels/imputation/mice.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
-copying statsmodels/imputation/bayes_mi.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
-creating build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/boxplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/tsaplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/agreement.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/regressionplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/correlation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/mosaicplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/_regressionplots_doc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/plot_grids.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/gofplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/dotplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/plottools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/tukeyplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-copying statsmodels/graphics/factorplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
-creating build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/try_mlecov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/count.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/nonlinls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/tmodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-copying statsmodels/miscmodels/ordinal_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tests
-copying statsmodels/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tests
-copying statsmodels/tests/test_package.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tests
-copying statsmodels/tests/test_x13.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/othermod
-copying statsmodels/othermod/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod
-copying statsmodels/othermod/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod
-copying statsmodels/othermod/betareg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod
-creating build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/conditional_models.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/_diagnostics_count.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/discrete_margins.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/discrete_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/count_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-copying statsmodels/discrete/truncated_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets
-copying statsmodels/datasets/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets
-copying statsmodels/datasets/template_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets
-copying statsmodels/datasets/utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets
-creating build/lib.linux-x86_64-cpython-313/statsmodels/formula
-copying statsmodels/formula/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula
-copying statsmodels/formula/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula
-copying statsmodels/formula/formulatools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/sequences.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/typing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/sm_exceptions.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/_testing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/_test_runner.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/transform_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/linalg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/numdiff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/catadd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/grouputils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/docstring.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/testing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/print_version.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/parallel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/web.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/decorators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/rng_qrng.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/rootfinding.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
+copying statsmodels/tools/eval_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
 creating build/lib.linux-x86_64-cpython-313/statsmodels/robust
-copying statsmodels/robust/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust
 copying statsmodels/robust/robust_linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust
+copying statsmodels/robust/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust
 copying statsmodels/robust/norms.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust
 copying statsmodels/robust/scale.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust
-creating build/lib.linux-x86_64-cpython-313/statsmodels/src
-copying statsmodels/src/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/src
-creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/generalized_linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/qif.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/bayes_mixed_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/cov_struct.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/generalized_estimating_equations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
-copying statsmodels/genmod/_tweedie_compound_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
 creating build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/nonparametric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/_inference_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/contingency_tables.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/tabledist.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/mediation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/dist_dependence_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/multicomp.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/proportion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/descriptivestats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/multitest.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/effect_size.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/_lilliefors.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/contingency_tables.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/_inference_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/stattools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/gof.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/power.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/meta_analysis.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/inter_rater.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/_knockoff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/_adnorm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/knockoff_regeffects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/effect_size.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/oneway.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/_delta_method.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/moment_helpers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/multivariate_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/regularized_covariance.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/contrast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/_diagnostic_other.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/_adnorm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/diagnostic_gen.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/descriptivestats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/outliers_influence.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/power.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/_diagnostic_other.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/multicomp.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/meta_analysis.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/oaxaca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/robust_compare.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/dist_dependence_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/multitest.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/nonparametric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/_knockoff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/sandwich_covariance.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/weightstats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/_lilliefors_critical_values.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/contrast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/correlation_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/outliers_influence.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/tabledist.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/regularized_covariance.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/robust_compare.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/knockoff_regeffects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/_lilliefors_critical_values.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/mediation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/gof.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
 copying statsmodels/stats/rates.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-copying statsmodels/stats/diagnostic_gen.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
-creating build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/feasible_gls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/_prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/dimred.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/process_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/recursive_ls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/rolling.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/mixed_linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-copying statsmodels/regression/quantile_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
-creating build/lib.linux-x86_64-cpython-313/statsmodels/duration
-copying statsmodels/duration/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
-copying statsmodels/duration/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
-copying statsmodels/duration/hazard_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
-copying statsmodels/duration/_kernel_estimates.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
-copying statsmodels/duration/survfunc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
+copying statsmodels/stats/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/weightstats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/multivariate_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/proportion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+copying statsmodels/stats/_delta_method.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats
+creating build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/_penalties.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/elastic_net.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/l1_slsqp.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/covtype.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/_prediction_inference.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/transform.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/distributed_estimation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/l1_solvers_common.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/l1_cvxopt.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/_parameter_inference.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/_penalized.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/_screening.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/optimizer.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/_constraints.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+copying statsmodels/base/wrapper.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base
+creating build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/count_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/_diagnostics_count.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/discrete_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/discrete_margins.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/truncated_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+copying statsmodels/discrete/conditional_models.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete
+creating build/lib.linux-x86_64-cpython-313/statsmodels/formula
+copying statsmodels/formula/formulatools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula
+copying statsmodels/formula/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula
+copying statsmodels/formula/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula
+creating build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/smpickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/foreign.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/openfile.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/stata_summary_examples.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/table.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/summary.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/summary2.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+copying statsmodels/iolib/tableformatting.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
+creating build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/discrete.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/mixture_rvs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/bernstein.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/edgeworth.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/distributions/empirical_distribution.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
 creating build/lib.linux-x86_64-cpython-313/statsmodels/compat
-copying statsmodels/compat/python.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
+copying statsmodels/compat/pytest.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
+copying statsmodels/compat/numpy.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
+copying statsmodels/compat/_scipy_multivariate_t.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
 copying statsmodels/compat/platform.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
 copying statsmodels/compat/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
+copying statsmodels/compat/scipy.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
 copying statsmodels/compat/pandas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
-copying statsmodels/compat/_scipy_multivariate_t.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
+copying statsmodels/compat/python.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
 copying statsmodels/compat/patsy.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
-copying statsmodels/compat/scipy.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
-copying statsmodels/compat/pytest.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
-copying statsmodels/compat/numpy.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat
+creating build/lib.linux-x86_64-cpython-313/statsmodels/imputation
+copying statsmodels/imputation/ros.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
+copying statsmodels/imputation/bayes_mi.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
+copying statsmodels/imputation/mice.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
+copying statsmodels/imputation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tests
+copying statsmodels/tests/test_x13.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tests
+copying statsmodels/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tests
+copying statsmodels/tests/test_package.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
-copying statsmodels/multivariate/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
-copying statsmodels/multivariate/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
-copying statsmodels/multivariate/factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
+copying statsmodels/multivariate/cancorr.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
+copying statsmodels/multivariate/manova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
 copying statsmodels/multivariate/plots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
-copying statsmodels/multivariate/pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
 copying statsmodels/multivariate/multivariate_ols.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
-copying statsmodels/multivariate/manova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
-copying statsmodels/multivariate/cancorr.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
+copying statsmodels/multivariate/pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
+copying statsmodels/multivariate/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
+copying statsmodels/multivariate/factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
+copying statsmodels/multivariate/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate
 creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+copying statsmodels/sandbox/multilinear.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+copying statsmodels/sandbox/pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
 copying statsmodels/sandbox/rls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+copying statsmodels/sandbox/descstats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+copying statsmodels/sandbox/bspline.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+copying statsmodels/sandbox/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+copying statsmodels/sandbox/predict_functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
 copying statsmodels/sandbox/infotheo.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
 copying statsmodels/sandbox/sysreg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/mle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/predict_functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/multilinear.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/descstats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
 copying statsmodels/sandbox/gam.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-copying statsmodels/sandbox/bspline.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
-creating build/lib.linux-x86_64-cpython-313/statsmodels/treatment
-copying statsmodels/treatment/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment
-copying statsmodels/treatment/treatment_effects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/print_version.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/_testing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/rootfinding.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/eval_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/sm_exceptions.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/parallel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/web.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/grouputils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/numdiff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/typing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/_test_runner.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/sequences.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/decorators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/rng_qrng.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/linalg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/transform_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/testing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/docstring.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-copying statsmodels/tools/catadd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools
-creating build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/summary2.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/foreign.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/tableformatting.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/smpickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/stata_summary_examples.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/summary.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/table.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-copying statsmodels/iolib/openfile.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib
-creating build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/edgeworth.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/bernstein.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/discrete.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/empirical_distribution.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
-copying statsmodels/distributions/mixture_rvs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions
+copying statsmodels/sandbox/mle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox
+creating build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/ordinal_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/count.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/nonlinls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/tmodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+copying statsmodels/miscmodels/try_mlecov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels
+creating build/lib.linux-x86_64-cpython-313/statsmodels/duration
+copying statsmodels/duration/survfunc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
+copying statsmodels/duration/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
+copying statsmodels/duration/_kernel_estimates.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
+copying statsmodels/duration/hazard_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
+copying statsmodels/duration/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration
 creating build/lib.linux-x86_64-cpython-313/statsmodels/interface
 copying statsmodels/interface/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/interface
-creating build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
-copying statsmodels/gam/gam_cross_validation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
-copying statsmodels/gam/gam_cross_validation/gam_cross_validation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
-copying statsmodels/gam/gam_cross_validation/cross_validators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
+creating build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/correlation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/regressionplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/tsaplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/gofplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/plot_grids.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/plottools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/agreement.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/mosaicplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/tukeyplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/boxplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/factorplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/_regressionplots_doc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+copying statsmodels/graphics/dotplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics
+creating build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/descriptive.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/originregress.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/aft_el.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/elregress.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/elanova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+copying statsmodels/emplike/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike
+creating build/lib.linux-x86_64-cpython-313/statsmodels/gam
+copying statsmodels/gam/generalized_additive_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
+copying statsmodels/gam/gam_penalties.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
+copying statsmodels/gam/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
+copying statsmodels/gam/smooth_basis.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
+copying statsmodels/gam/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam
+creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/generalized_linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/_tweedie_compound_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/bayes_mixed_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/qif.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/generalized_estimating_equations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/cov_struct.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+copying statsmodels/genmod/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod
+creating build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/bandwidths.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/smoothers_lowess.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/kernels_asymmetric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/smoothers_lowess_old.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/kernel_density.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/kdetools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/_kernel_base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/kernel_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/kernels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+copying statsmodels/nonparametric/kde.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric
+creating build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/mixed_linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/recursive_ls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/linear_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/feasible_gls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/dimred.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/_prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/quantile_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/rolling.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+copying statsmodels/regression/process_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression
+creating build/lib.linux-x86_64-cpython-313/statsmodels/src
+copying statsmodels/src/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/src
+creating build/lib.linux-x86_64-cpython-313/statsmodels/treatment
+copying statsmodels/treatment/treatment_effects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment
+copying statsmodels/treatment/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/ar_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/arma_mle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/varma_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/x13.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/stattools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/coint_tables.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/arima_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/descriptivestats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/tsatools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/adfvalues.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/deterministic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/mlemodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/arima_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/seasonal.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+copying statsmodels/tsa/_bds.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa
+creating build/lib.linux-x86_64-cpython-313/statsmodels/othermod
+copying statsmodels/othermod/betareg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod
+copying statsmodels/othermod/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod
+copying statsmodels/othermod/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets
+copying statsmodels/datasets/template_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets
+copying statsmodels/datasets/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets
+copying statsmodels/datasets/utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_linalg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_grouputils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_decorators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_eval_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_docstring.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_numdiff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_sequences.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_web.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_testing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_transform_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_catadd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_parallel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_rootfinding.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+copying statsmodels/tools/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
+copying statsmodels/tools/validation/validation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
+copying statsmodels/tools/validation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
+copying statsmodels/tools/validation/decorators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests
+copying statsmodels/tools/validation/tests/test_validation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests
+copying statsmodels/tools/validation/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
+copying statsmodels/robust/tests/test_norms.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
+copying statsmodels/robust/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
+copying statsmodels/robust/tests/test_scale.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
+copying statsmodels/robust/tests/test_mquantiles.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
+copying statsmodels/robust/tests/test_rlm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
+copying statsmodels/robust/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
+copying statsmodels/robust/tests/results/results_rlm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
+copying statsmodels/robust/tests/results/results_norms.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
+copying statsmodels/stats/libqsturng/qsturng_.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
+copying statsmodels/stats/libqsturng/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
+copying statsmodels/stats/libqsturng/make_tbls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
+creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_anova_rm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_power.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_statstools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_contingency_tables.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_knockoff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_effectsize.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_rates_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_oneway.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_outliers_influence.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_regularized_covariance.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_moment_helpers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_proportion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_meta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_tabledist.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_inter_rater.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_corrpsd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_oaxaca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_lilliefors.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_nonparametric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_diagnostic_other.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_dist_dependant_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_weightstats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_multi.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_descriptivestats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_contrast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_deltacov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_correlation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_sandwich.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_mediation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_tost.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_gof.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_qsturng.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_pairwise.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_influence.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_robust_compare.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_groups_sw.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/tests/test_panel_robustcov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
+copying statsmodels/stats/libqsturng/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
+copying statsmodels/stats/libqsturng/tests/test_qsturng.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_meta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_multinomial_proportions.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_power.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_proportion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_panelrobust.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_rates.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_screening.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_penalized.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_predict.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_shrink_pickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_distributed_estimation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_generic_methods.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_optimize.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_transform.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
+copying statsmodels/base/tests/test_penalties.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
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+copying statsmodels/distributions/copula/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
+copying statsmodels/distributions/copula/depfunc_ev.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
+copying statsmodels/distributions/copula/copulas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
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+copying statsmodels/distributions/copula/other_copulas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
+copying statsmodels/distributions/copula/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
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+copying statsmodels/compat/tests/test_itercompat.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
+copying statsmodels/compat/tests/test_pandas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
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+copying statsmodels/imputation/tests/test_ros.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
+copying statsmodels/imputation/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
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+copying statsmodels/multivariate/factor_rotation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation/tests
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/mcevaluate
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+copying statsmodels/sandbox/nonparametric/kernels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
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+copying statsmodels/sandbox/tsa/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats/tests
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+copying statsmodels/sandbox/stats/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats/tests
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+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
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+copying statsmodels/sandbox/distributions/examples/ex_fitfr.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
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+copying statsmodels/sandbox/distributions/examples/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
+copying statsmodels/sandbox/distributions/examples/ex_mvelliptical.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/test_transf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/check_moments.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/test_multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/test_gof_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/distparams.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/test_extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/_est_fit.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+copying statsmodels/sandbox/distributions/tests/test_norm_expan.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel/tests
+copying statsmodels/sandbox/panel/tests/test_random_panel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel/tests
+copying statsmodels/sandbox/panel/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+copying statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+copying statsmodels/sandbox/nonparametric/tests/ex_gam_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+copying statsmodels/sandbox/nonparametric/tests/ex_smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+copying statsmodels/sandbox/nonparametric/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+copying statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+copying statsmodels/sandbox/nonparametric/tests/test_smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/results_gmm_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/results_gmm_griliches.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/test_gmm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/test_gmm_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+copying statsmodels/miscmodels/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+copying statsmodels/miscmodels/tests/test_ordinal_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+copying statsmodels/miscmodels/tests/test_generic_mle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+copying statsmodels/miscmodels/tests/results_tmodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+copying statsmodels/miscmodels/tests/test_tmodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+copying statsmodels/miscmodels/tests/test_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
+copying statsmodels/miscmodels/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
+copying statsmodels/miscmodels/tests/results/results_ordinal_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
+copying statsmodels/duration/tests/test_phreg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
+copying statsmodels/duration/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
+copying statsmodels/duration/tests/test_survfunc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/survival_enet_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/survival_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/phreg_gentests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_gofplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_agreement.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_mosaicplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_boxplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_tsaplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_correlation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_factorplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_regressionplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+copying statsmodels/graphics/tests/test_dotplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+copying statsmodels/emplike/tests/test_aft.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+copying statsmodels/emplike/tests/test_origin.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+copying statsmodels/emplike/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+copying statsmodels/emplike/tests/test_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+copying statsmodels/emplike/tests/test_descriptive.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+copying statsmodels/emplike/tests/test_anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results
+copying statsmodels/emplike/tests/results/el_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results
+copying statsmodels/emplike/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests
-copying statsmodels/gam/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests
 copying statsmodels/gam/tests/test_gam.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests
 copying statsmodels/gam/tests/test_penalized.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests
 copying statsmodels/gam/tests/test_smooth_basis.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests
+copying statsmodels/gam/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
+copying statsmodels/gam/gam_cross_validation/gam_cross_validation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
+copying statsmodels/gam/gam_cross_validation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
+copying statsmodels/gam/gam_cross_validation/cross_validators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation
 creating build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/results_mpg_bs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/results_mpg_bs_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
 copying statsmodels/gam/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
 copying statsmodels/gam/tests/results/results_pls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/results_mpg_bs_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/results_mpg_bs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
-copying statsmodels/tsa/base/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
-copying statsmodels/tsa/base/prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
-copying statsmodels/tsa/base/datetools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
-copying statsmodels/tsa/base/tsa_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
-copying statsmodels/tsa/innovations/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
-copying statsmodels/tsa/innovations/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
-copying statsmodels/tsa/innovations/arma_innovations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
+creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/gee_gaussian_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/gee_categorical_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_bayes_mixed_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_score_test.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_gee.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_glm_weights.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_constrained.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_qif.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/gee_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/gee_poisson_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_gee_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+copying statsmodels/genmod/tests/test_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
+copying statsmodels/genmod/families/links.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
+copying statsmodels/genmod/families/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
+copying statsmodels/genmod/families/family.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
+copying statsmodels/genmod/families/varfuncs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
+creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/glm_test_resids.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/glmnet_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/res_R_var_weight.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/results_glm_poisson_weights.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/elastic_net_generate_tests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/gee_generate_tests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/results_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
+copying statsmodels/genmod/families/tests/test_link.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
+copying statsmodels/genmod/families/tests/test_family.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
+copying statsmodels/genmod/families/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_asymmetric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_kernels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_kernel_density.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_lowess.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_kernel_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_bandwidths.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+copying statsmodels/nonparametric/tests/test_kde.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_theil.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_predict.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_processreg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_rolling.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_lme.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_dimred.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_quantile_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_glsar_gretl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_cov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_robustcov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_recursive_ls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+copying statsmodels/regression/tests/test_glsar_stata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/glmnet_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_macro_ols_robust.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_theil_textile.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/generate_lme.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/generate_lasso.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_quantile_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/macro_gr_corc_stata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests
+copying statsmodels/treatment/tests/test_teffects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests
+copying statsmodels/treatment/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
+copying statsmodels/treatment/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
+copying statsmodels/treatment/tests/results/results_teffects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/news.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/initialization.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/cfa_simulation_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/_pykalman_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/simulation_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/structural.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 copying statsmodels/tsa/statespace/varmax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/kalman_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 copying statsmodels/tsa/statespace/representation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/structural.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/dynamic_factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 copying statsmodels/tsa/statespace/mlemodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/news.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 copying statsmodels/tsa/statespace/kalman_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/sarimax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/simulation_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 copying statsmodels/tsa/statespace/exponential_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/dynamic_factor_mq.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/dynamic_factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/kalman_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/cfa_simulation_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/initialization.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 copying statsmodels/tsa/statespace/_quarterly_ar1.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-copying statsmodels/tsa/statespace/_pykalman_smoother.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_tsa_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_adfuller_lag.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_ar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_bds.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_x13.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_arima_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_seasonal.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_deterministic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_exponential_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-copying statsmodels/tsa/tests/test_stattools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
-copying statsmodels/tsa/ardl/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
-copying statsmodels/tsa/ardl/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
-copying statsmodels/tsa/ardl/pss_critical_values.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/filtertools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/hp_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/cf_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/_utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-copying statsmodels/tsa/filters/bk_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
-copying statsmodels/tsa/arima/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
-copying statsmodels/tsa/arima/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
-copying statsmodels/tsa/arima/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
-copying statsmodels/tsa/arima/params.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
-copying statsmodels/tsa/arima/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
-copying statsmodels/tsa/arima/specification.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/statespace/sarimax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
+copying statsmodels/tsa/statespace/dynamic_factor_mq.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting
-copying statsmodels/tsa/forecasting/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting
-copying statsmodels/tsa/forecasting/stl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting
 copying statsmodels/tsa/forecasting/theta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
-copying statsmodels/tsa/exponential_smoothing/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
-copying statsmodels/tsa/exponential_smoothing/base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
-copying statsmodels/tsa/exponential_smoothing/ets.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
-copying statsmodels/tsa/exponential_smoothing/initialization.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
-copying statsmodels/tsa/holtwinters/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
-copying statsmodels/tsa/holtwinters/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
-copying statsmodels/tsa/holtwinters/results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
-copying statsmodels/tsa/holtwinters/_smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
+copying statsmodels/tsa/forecasting/stl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting
+copying statsmodels/tsa/forecasting/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
+copying statsmodels/tsa/base/prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
+copying statsmodels/tsa/base/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
+copying statsmodels/tsa/base/datetools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
+copying statsmodels/tsa/base/tsa_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp
 copying statsmodels/tsa/interp/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp
 copying statsmodels/tsa/interp/denton.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/svar_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/irf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/util.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
 copying statsmodels/tsa/vector_ar/vecm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
 copying statsmodels/tsa/vector_ar/hypothesis_test_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/output.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/util.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
-copying statsmodels/tsa/vector_ar/var_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
 copying statsmodels/tsa/vector_ar/plotting.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/var_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/svar_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/output.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+copying statsmodels/tsa/vector_ar/irf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_seasonal.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_stattools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_ar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_x13.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_tsa_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_adfuller_lag.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_exponential_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_deterministic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_bds.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+copying statsmodels/tsa/tests/test_arima_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
+copying statsmodels/tsa/innovations/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
+copying statsmodels/tsa/innovations/arma_innovations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
+copying statsmodels/tsa/innovations/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/arima/params.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/arima/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/arima/specification.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/arima/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/arima/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+copying statsmodels/tsa/arima/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/hp_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/filtertools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/cf_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/bk_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/_utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
+copying statsmodels/tsa/filters/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching
-copying statsmodels/tsa/regime_switching/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching
 copying statsmodels/tsa/regime_switching/markov_switching.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching
 copying statsmodels/tsa/regime_switching/markov_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching
+copying statsmodels/tsa/regime_switching/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching
 copying statsmodels/tsa/regime_switching/markov_autoregression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
+copying statsmodels/tsa/exponential_smoothing/initialization.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
+copying statsmodels/tsa/exponential_smoothing/base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
+copying statsmodels/tsa/exponential_smoothing/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
+copying statsmodels/tsa/exponential_smoothing/ets.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl
-copying statsmodels/tsa/stl/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl
 copying statsmodels/tsa/stl/mstl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
-copying statsmodels/tsa/base/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
-copying statsmodels/tsa/base/tests/test_prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
-copying statsmodels/tsa/base/tests/test_base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
-copying statsmodels/tsa/base/tests/test_datetools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
-copying statsmodels/tsa/base/tests/test_tsa_indexes.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
-copying statsmodels/tsa/innovations/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
-copying statsmodels/tsa/innovations/tests/test_arma_innovations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
-copying statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
+copying statsmodels/tsa/stl/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
+copying statsmodels/tsa/ardl/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
+copying statsmodels/tsa/ardl/pss_critical_values.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
+copying statsmodels/tsa/ardl/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
+copying statsmodels/tsa/holtwinters/model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
+copying statsmodels/tsa/holtwinters/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
+copying statsmodels/tsa/holtwinters/_smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
+copying statsmodels/tsa/holtwinters/results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters
 copying statsmodels/tsa/statespace/_filters/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_simulate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_representation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_varmax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_collapsed.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_conserve_memory.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_univariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_dynamic_factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_varmax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_weights.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_models.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/kfas_helpers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_simulate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_representation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_initialization.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_fixed_params.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_mlemodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_kalman.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_simulation_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_pickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_news.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_chandrasekhar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_save.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_mlemodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_fixed_params.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_structural.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_impulse_responses.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_forecasting.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_options.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_sarimax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_var.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_exponential_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_conserve_memory.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_univariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/kfas_helpers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_save.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_models.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_concentrated.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_simulation_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_pickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_chandrasekhar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_var.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_initialization.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_sarimax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 copying statsmodels/tsa/statespace/tests/test_decompose.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
-copying statsmodels/tsa/statespace/tests/test_exponential_smoothing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_impulse_responses.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_options.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
+copying statsmodels/tsa/statespace/tests/test_kalman.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers
 copying statsmodels/tsa/statespace/_smoothers/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_dynamic_factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_kalman_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_varmax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_sarimax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_structural.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_var_misc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_kalman_filter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_var_R.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_structural.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_sarimax.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
+copying statsmodels/tsa/forecasting/tests/test_theta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
+copying statsmodels/tsa/forecasting/tests/test_stl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
+copying statsmodels/tsa/forecasting/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
+copying statsmodels/tsa/base/tests/test_prediction.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
+copying statsmodels/tsa/base/tests/test_base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
+copying statsmodels/tsa/base/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
+copying statsmodels/tsa/base/tests/test_tsa_indexes.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
+copying statsmodels/tsa/base/tests/test_datetools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests
+copying statsmodels/tsa/interp/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests
+copying statsmodels/tsa/interp/tests/test_denton.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/test_coint.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/test_var_jmulti.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/test_svar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/test_vecm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/example_svar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+copying statsmodels/tsa/vector_ar/tests/test_var.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results
+copying statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/results_var.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/results_svar_st.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/results_var_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/results_svar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima111nc_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima112_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima112_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/tests/results/arima211nc_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/savedrvs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arma_acf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima211nc_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/tests/results/arima111nc_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/datamlw_tls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima111_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima112_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima112_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima111_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_ar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/make_arma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/tests/results/arima211_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima112nc_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/tests/results/arima112nc_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/tests/results/results_process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima211_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/tests/results/results_arma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests
-copying statsmodels/tsa/ardl/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests
-copying statsmodels/tsa/ardl/tests/test_ardl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
-copying statsmodels/tsa/ardl/_pss_critical_values/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
-copying statsmodels/tsa/ardl/_pss_critical_values/pss.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
-copying statsmodels/tsa/ardl/_pss_critical_values/pss-process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests
-copying statsmodels/tsa/filters/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests
-copying statsmodels/tsa/filters/tests/test_filters.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results
-copying statsmodels/tsa/filters/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results
-copying statsmodels/tsa/filters/tests/results/filter_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results
+copying statsmodels/tsa/tests/results/arima112nc_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima111nc_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima211nc_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/savedrvs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima211_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/datamlw_tls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima111_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/make_arma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima111_css_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arma_acf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_ar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
+copying statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
+copying statsmodels/tsa/innovations/tests/test_arma_innovations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
+copying statsmodels/tsa/innovations/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
 copying statsmodels/tsa/arima/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
-copying statsmodels/tsa/arima/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
-copying statsmodels/tsa/arima/tests/test_params.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
 copying statsmodels/tsa/arima/tests/test_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
 copying statsmodels/tsa/arima/tests/test_specification.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
+copying statsmodels/tsa/arima/tests/test_params.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
+copying statsmodels/tsa/arima/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
 copying statsmodels/tsa/arima/estimators/statespace.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
-copying statsmodels/tsa/arima/estimators/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
-copying statsmodels/tsa/arima/estimators/durbin_levinson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
 copying statsmodels/tsa/arima/estimators/hannan_rissanen.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
-copying statsmodels/tsa/arima/estimators/yule_walker.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
-copying statsmodels/tsa/arima/estimators/gls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
+copying statsmodels/tsa/arima/estimators/durbin_levinson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
 copying statsmodels/tsa/arima/estimators/burg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
+copying statsmodels/tsa/arima/estimators/yule_walker.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
+copying statsmodels/tsa/arima/estimators/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
 copying statsmodels/tsa/arima/estimators/innovations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
+copying statsmodels/tsa/arima/estimators/gls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets
 copying statsmodels/tsa/arima/datasets/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
-copying statsmodels/tsa/arima/estimators/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
-copying statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
+copying statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
 copying statsmodels/tsa/arima/estimators/tests/test_gls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
 copying statsmodels/tsa/arima/estimators/tests/test_innovations.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
-copying statsmodels/tsa/arima/estimators/tests/test_yule_walker.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
 copying statsmodels/tsa/arima/estimators/tests/test_burg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
+copying statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
+copying statsmodels/tsa/arima/estimators/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
+copying statsmodels/tsa/arima/estimators/tests/test_yule_walker.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
 copying statsmodels/tsa/arima/estimators/tests/test_statespace.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
-copying statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002
 copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
 copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/oshorts.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
 copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/dowj.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
 copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/lake.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
-copying statsmodels/tsa/forecasting/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
-copying statsmodels/tsa/forecasting/tests/test_stl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
-copying statsmodels/tsa/forecasting/tests/test_theta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests
-copying statsmodels/tsa/holtwinters/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests
-copying statsmodels/tsa/holtwinters/tests/test_holtwinters.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results
-copying statsmodels/tsa/holtwinters/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests
-copying statsmodels/tsa/interp/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests
-copying statsmodels/tsa/interp/tests/test_denton.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/test_vecm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/test_coint.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/test_svar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/test_var.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/test_var_jmulti.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-copying statsmodels/tsa/vector_ar/tests/example_svar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results
-copying statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/results_svar.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/results_var.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/results_svar_st.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/results_var_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests
+copying statsmodels/tsa/filters/tests/test_filters.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests
+copying statsmodels/tsa/filters/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results
+copying statsmodels/tsa/filters/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results
+copying statsmodels/tsa/filters/tests/results/filter_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
-copying statsmodels/tsa/regime_switching/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
-copying statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
 copying statsmodels/tsa/regime_switching/tests/test_markov_switching.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
+copying statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
+copying statsmodels/tsa/regime_switching/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
 copying statsmodels/tsa/regime_switching/tests/test_markov_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results
 copying statsmodels/tsa/regime_switching/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests
-copying statsmodels/tsa/stl/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests
 copying statsmodels/tsa/stl/tests/test_stl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests
 copying statsmodels/tsa/stl/tests/test_mstl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests
+copying statsmodels/tsa/stl/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
 copying statsmodels/tsa/stl/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_penalties.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_distributed_estimation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_optimize.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_screening.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_penalized.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_generic_methods.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_predict.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_transform.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-copying statsmodels/base/tests/test_shrink_pickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/base/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_kernels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_asymmetric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_kde.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_kernel_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_bandwidths.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_kernel_density.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-copying statsmodels/nonparametric/tests/test_lowess.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-copying statsmodels/emplike/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-copying statsmodels/emplike/tests/test_origin.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-copying statsmodels/emplike/tests/test_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-copying statsmodels/emplike/tests/test_aft.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-copying statsmodels/emplike/tests/test_descriptive.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-copying statsmodels/emplike/tests/test_anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results
-copying statsmodels/emplike/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results
-copying statsmodels/emplike/tests/results/el_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
-copying statsmodels/imputation/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
-copying statsmodels/imputation/tests/test_mice.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
-copying statsmodels/imputation/tests/test_bayes_mi.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
-copying statsmodels/imputation/tests/test_ros.py -> build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_correlation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_regressionplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_boxplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_agreement.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_mosaicplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_tsaplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_dotplot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_factorplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-copying statsmodels/graphics/tests/test_gofplots.py -> build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-copying statsmodels/miscmodels/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-copying statsmodels/miscmodels/tests/test_tmodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-copying statsmodels/miscmodels/tests/results_tmodel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-copying statsmodels/miscmodels/tests/test_generic_mle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-copying statsmodels/miscmodels/tests/test_ordinal_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-copying statsmodels/miscmodels/tests/test_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
-copying statsmodels/miscmodels/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
-copying statsmodels/miscmodels/tests/results/results_ordinal_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
+copying statsmodels/tsa/ardl/_pss_critical_values/pss.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
+copying statsmodels/tsa/ardl/_pss_critical_values/pss-process.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
+copying statsmodels/tsa/ardl/_pss_critical_values/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests
+copying statsmodels/tsa/ardl/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests
+copying statsmodels/tsa/ardl/tests/test_ardl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests
+copying statsmodels/tsa/holtwinters/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests
+copying statsmodels/tsa/holtwinters/tests/test_holtwinters.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results
+copying statsmodels/tsa/holtwinters/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results
 creating build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests
-copying statsmodels/othermod/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests
 copying statsmodels/othermod/tests/test_beta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests
+copying statsmodels/othermod/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests
 creating build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
-copying statsmodels/othermod/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
 copying statsmodels/othermod/tests/results/results_betareg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_constrained.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_count_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_discrete.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_conditional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_margins.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_sandwich_cov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_predict.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-copying statsmodels/discrete/tests/test_truncated_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_predict.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_truncated.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_glm_logit_constrained.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_discrete.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_count_robust_cluster.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_count_margins.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_poisson_constrained.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/results_truncated_st.py -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
-copying statsmodels/datasets/stackloss/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
-copying statsmodels/datasets/stackloss/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
-copying statsmodels/datasets/modechoice/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
-copying statsmodels/datasets/modechoice/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
-copying statsmodels/datasets/macrodata/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
-copying statsmodels/datasets/macrodata/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
-copying statsmodels/datasets/co2/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
-copying statsmodels/datasets/co2/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
-copying statsmodels/datasets/cpunish/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
-copying statsmodels/datasets/cpunish/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
-copying statsmodels/datasets/copper/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
-copying statsmodels/datasets/copper/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
-copying statsmodels/datasets/anes96/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
-copying statsmodels/datasets/anes96/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
-copying statsmodels/datasets/heart/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
-copying statsmodels/datasets/heart/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
+copying statsmodels/othermod/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
+copying statsmodels/datasets/nile/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
+copying statsmodels/datasets/nile/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
+copying statsmodels/datasets/china_smoking/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
+copying statsmodels/datasets/china_smoking/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
+copying statsmodels/datasets/committee/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
+copying statsmodels/datasets/committee/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
+copying statsmodels/datasets/scotland/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
+copying statsmodels/datasets/scotland/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
+copying statsmodels/datasets/statecrime/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
+copying statsmodels/datasets/statecrime/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data
-copying statsmodels/datasets/danish_data/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data
 copying statsmodels/datasets/danish_data/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
-copying statsmodels/datasets/star98/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
-copying statsmodels/datasets/star98/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
+copying statsmodels/datasets/danish_data/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
+copying statsmodels/datasets/anes96/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
+copying statsmodels/datasets/anes96/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
+copying statsmodels/datasets/longley/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
+copying statsmodels/datasets/longley/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
+copying statsmodels/datasets/copper/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
+copying statsmodels/datasets/copper/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests
-copying statsmodels/datasets/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests
 copying statsmodels/datasets/tests/test_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests
+copying statsmodels/datasets/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests
 copying statsmodels/datasets/tests/test_utils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
+copying statsmodels/datasets/spector/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
+copying statsmodels/datasets/spector/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld
-copying statsmodels/datasets/grunfeld/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld
 copying statsmodels/datasets/grunfeld/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
-copying statsmodels/datasets/longley/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
-copying statsmodels/datasets/longley/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
-copying statsmodels/datasets/randhie/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
-copying statsmodels/datasets/randhie/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
+copying statsmodels/datasets/grunfeld/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair
-copying statsmodels/datasets/fair/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair
 copying statsmodels/datasets/fair/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
-copying statsmodels/datasets/spector/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
-copying statsmodels/datasets/spector/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
-copying statsmodels/datasets/scotland/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
-copying statsmodels/datasets/scotland/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
+copying statsmodels/datasets/fair/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard
-copying statsmodels/datasets/ccard/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard
 copying statsmodels/datasets/ccard/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
-copying statsmodels/datasets/strikes/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
-copying statsmodels/datasets/strikes/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
-copying statsmodels/datasets/statecrime/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
-copying statsmodels/datasets/statecrime/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
+copying statsmodels/datasets/ccard/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
+copying statsmodels/datasets/cpunish/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
+copying statsmodels/datasets/cpunish/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino
-copying statsmodels/datasets/elnino/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino
 copying statsmodels/datasets/elnino/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
-copying statsmodels/datasets/nile/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
-copying statsmodels/datasets/nile/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
-copying statsmodels/datasets/elec_equip/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
-copying statsmodels/datasets/elec_equip/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
-copying statsmodels/datasets/committee/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
-copying statsmodels/datasets/committee/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
+copying statsmodels/datasets/elnino/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
+copying statsmodels/datasets/strikes/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
+copying statsmodels/datasets/strikes/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
+copying statsmodels/datasets/engel/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
+copying statsmodels/datasets/engel/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
+copying statsmodels/datasets/co2/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
+copying statsmodels/datasets/co2/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots
-copying statsmodels/datasets/sunspots/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots
 copying statsmodels/datasets/sunspots/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots
+copying statsmodels/datasets/sunspots/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
+copying statsmodels/datasets/modechoice/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
+copying statsmodels/datasets/modechoice/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
+copying statsmodels/datasets/heart/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
+copying statsmodels/datasets/heart/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
+copying statsmodels/datasets/elec_equip/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
+copying statsmodels/datasets/elec_equip/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
+copying statsmodels/datasets/macrodata/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
+copying statsmodels/datasets/macrodata/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
+copying statsmodels/datasets/star98/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
+copying statsmodels/datasets/star98/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer
-copying statsmodels/datasets/cancer/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer
 copying statsmodels/datasets/cancer/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
-copying statsmodels/datasets/china_smoking/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
-copying statsmodels/datasets/china_smoking/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
+copying statsmodels/datasets/cancer/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
+copying statsmodels/datasets/randhie/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
+copying statsmodels/datasets/randhie/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
+creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
+copying statsmodels/datasets/stackloss/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
+copying statsmodels/datasets/stackloss/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation
-copying statsmodels/datasets/interest_inflation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation
 copying statsmodels/datasets/interest_inflation/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation
+copying statsmodels/datasets/interest_inflation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation
 creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility
-copying statsmodels/datasets/fertility/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility
 copying statsmodels/datasets/fertility/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility
-creating build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
-copying statsmodels/datasets/engel/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
-copying statsmodels/datasets/engel/data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
-creating build/lib.linux-x86_64-cpython-313/statsmodels/formula/tests
-copying statsmodels/formula/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula/tests
-copying statsmodels/formula/tests/test_formula.py -> build/lib.linux-x86_64-cpython-313/statsmodels/formula/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
-copying statsmodels/robust/tests/test_mquantiles.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
-copying statsmodels/robust/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
-copying statsmodels/robust/tests/test_rlm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
-copying statsmodels/robust/tests/test_norms.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
-copying statsmodels/robust/tests/test_scale.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
-copying statsmodels/robust/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
-copying statsmodels/robust/tests/results/results_norms.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
-copying statsmodels/robust/tests/results/results_rlm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
-copying statsmodels/genmod/families/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
-copying statsmodels/genmod/families/varfuncs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
-copying statsmodels/genmod/families/family.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
-copying statsmodels/genmod/families/links.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families
-creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_score_test.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_gee_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_constrained.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/gee_poisson_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_glm_weights.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_gee.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/gee_categorical_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/gee_gaussian_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/gee_simulation_check.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_qif.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_bayes_mixed_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-copying statsmodels/genmod/tests/test_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
-copying statsmodels/genmod/families/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
-copying statsmodels/genmod/families/tests/test_link.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
-copying statsmodels/genmod/families/tests/test_family.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/families/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/glmnet_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/glm_test_resids.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/results_glm_poisson_weights.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/results_glm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_generate_tests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/res_R_var_weight.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/elastic_net_generate_tests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_moment_helpers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_descriptivestats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_correlation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_power.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_panel_robustcov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_oneway.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_effectsize.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_contrast.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_multi.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_tost.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_knockoff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_lilliefors.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_robust_compare.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_nonparametric.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_base.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_anova_rm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_rates_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_proportion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_statstools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_tabledist.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_corrpsd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_diagnostic_other.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_regularized_covariance.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_weightstats.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_gof.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_sandwich.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_qsturng.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_meta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_mediation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_deltacov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_influence.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_oaxaca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_contingency_tables.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_pairwise.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_groups_sw.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_dist_dependant_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_outliers_influence.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/tests/test_inter_rater.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
-copying statsmodels/stats/libqsturng/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
-copying statsmodels/stats/libqsturng/make_tbls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
-copying statsmodels/stats/libqsturng/qsturng_.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
-creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_rates.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_proportion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_multinomial_proportions.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/lilliefors_critical_value_simulation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_power.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_meta.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_panelrobust.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
-copying statsmodels/stats/libqsturng/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
-copying statsmodels/stats/libqsturng/tests/test_qsturng.py -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_lme.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_dimred.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_theil.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_processreg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_rolling.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_robustcov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_predict.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_glsar_gretl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_recursive_ls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_quantile_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_glsar_stata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-copying statsmodels/regression/tests/test_cov.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/glmnet_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/generate_lme.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_macro_ols_robust.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/macro_gr_corc_stata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_quantile_regression.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_theil_textile.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/generate_lasso.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_grunfeld_ols_robust_cluster.py -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
-copying statsmodels/duration/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
-copying statsmodels/duration/tests/test_phreg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
-copying statsmodels/duration/tests/test_survfunc.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/survival_enet_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/survival_r_results.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/phreg_gentests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
-copying statsmodels/compat/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
-copying statsmodels/compat/tests/test_scipy_compat.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
-copying statsmodels/compat/tests/test_pandas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
-copying statsmodels/compat/tests/test_itercompat.py -> build/lib.linux-x86_64-cpython-313/statsmodels/compat/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/test_cancorr.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/test_pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/test_multivariate_ols.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/test_manova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/test_factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-copying statsmodels/multivariate/tests/test_ml_factor.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
-copying statsmodels/multivariate/factor_rotation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
-copying statsmodels/multivariate/factor_rotation/_gpa_rotation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
-copying statsmodels/multivariate/factor_rotation/_analytic_rotation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
-copying statsmodels/multivariate/factor_rotation/_wrappers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation
-creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
-copying statsmodels/multivariate/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
-copying statsmodels/multivariate/tests/results/datamlw.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation/tests
-copying statsmodels/multivariate/factor_rotation/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation/tests
-copying statsmodels/multivariate/factor_rotation/tests/test_rotation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/factor_rotation/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/try_arma_more.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/diffusion2.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/movstat.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/try_fi.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/diffusion.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/example_arma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/try_var_convolve.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/fftarma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-copying statsmodels/sandbox/tsa/varma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
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-copying statsmodels/sandbox/nonparametric/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/testdata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/kernel_extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/densityorthopoly.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/kde2.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/kernels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-copying statsmodels/sandbox/nonparametric/kdecovclass.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-copying statsmodels/sandbox/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-copying statsmodels/sandbox/tests/test_gam.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-copying statsmodels/sandbox/tests/maketests_mlabwrap.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-copying statsmodels/sandbox/tests/test_predict_functional.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-copying statsmodels/sandbox/tests/test_pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-copying statsmodels/sandbox/tests/savervs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/mcevaluate
-copying statsmodels/sandbox/mcevaluate/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/mcevaluate
-copying statsmodels/sandbox/mcevaluate/arma.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/mcevaluate
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/datarich
-copying statsmodels/sandbox/datarich/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/datarich
-copying statsmodels/sandbox/datarich/factormodels.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/datarich
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/stats_dhuard.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/ex_newtests.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/multicomp.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/contrast_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/diagnostic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/runs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-copying statsmodels/sandbox/stats/stats_mstats_short.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/try_catdata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/kernridgeregress_class.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/sympy_diff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/example_kernridge.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/penalized.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/predstd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/onewaygls.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/try_ols_anova.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/ols_anova_original.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/ar_panel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/treewalkerclass.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/gmm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/anova_nistcertified.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/try_treewalker.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-copying statsmodels/sandbox/regression/runmnl.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/archive
-copying statsmodels/sandbox/archive/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/archive
-copying statsmodels/sandbox/archive/tsa.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/archive
-copying statsmodels/sandbox/archive/linalg_covmat.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/archive
-copying statsmodels/sandbox/archive/linalg_decomp_1.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/archive
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/mixed.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/panelmod.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/correlation_structures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/sandwich_covariance_generic.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/panel_short.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-copying statsmodels/sandbox/panel/random_panel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tools
-copying statsmodels/sandbox/tools/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tools
-copying statsmodels/sandbox/tools/mctools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tools
-copying statsmodels/sandbox/tools/tools_pca.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tools
-copying statsmodels/sandbox/tools/try_mctools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tools
-copying statsmodels/sandbox/tools/cross_val.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tools
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/mv_normal.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/transform_functions.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/try_max.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/sppatch.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/try_pot.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/transformed.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/genpareto.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/quantize.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/gof_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/otherdist.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/mv_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-copying statsmodels/sandbox/distributions/estimators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-copying statsmodels/sandbox/nonparametric/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-copying statsmodels/sandbox/nonparametric/tests/ex_gam_am_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-copying statsmodels/sandbox/nonparametric/tests/test_kernel_extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-copying statsmodels/sandbox/nonparametric/tests/ex_gam_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-copying statsmodels/sandbox/nonparametric/tests/ex_smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-copying statsmodels/sandbox/nonparametric/tests/test_smoothers.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/nonparametric/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats/tests
-copying statsmodels/sandbox/stats/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats/tests
-copying statsmodels/sandbox/stats/tests/test_multicomp.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats/tests
-copying statsmodels/sandbox/stats/tests/test_runs.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/stats/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/results_gmm_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/test_gmm_poisson.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/results_ivreg2_griliches.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/test_gmm.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/results_gmm_griliches.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/results_gmm_griliches_iter.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel/tests
-copying statsmodels/sandbox/panel/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel/tests
-copying statsmodels/sandbox/panel/tests/test_random_panel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/panel/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/test_transf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/test_norm_expan.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/test_extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/_est_fit.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/check_moments.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/test_gof_new.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/test_multivariate.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-copying statsmodels/sandbox/distributions/tests/distparams.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/ex_gof.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/matchdist.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/ex_transf2.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/ex_mvelliptical.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/ex_extras.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-copying statsmodels/sandbox/distributions/examples/ex_fitfr.py -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/distributions/examples
-creating build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests
-copying statsmodels/treatment/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests
-copying statsmodels/treatment/tests/test_teffects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
-copying statsmodels/treatment/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
-copying statsmodels/treatment/tests/results/results_teffects.py -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_transform_model.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_data.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_rootfinding.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_linalg.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_catadd.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_eval_measures.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_grouputils.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_decorators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_web.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_sequences.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_testing.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_numdiff.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_parallel.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-copying statsmodels/tools/tests/test_docstring.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
-copying statsmodels/tools/validation/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
-copying statsmodels/tools/validation/validation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
-copying statsmodels/tools/validation/decorators.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation
-creating build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests
-copying statsmodels/tools/validation/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests
-copying statsmodels/tools/validation/tests/test_validation.py -> build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/test_summary2.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/test_summary.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/test_summary_old.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/test_table_econpy.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/test_pickle.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-copying statsmodels/iolib/tests/test_table.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
-copying statsmodels/iolib/tests/results/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
-copying statsmodels/iolib/tests/results/macrodata.py -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
-creating build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/test_tools.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/test_discrete.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/test_ecdf.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/test_mixture.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/test_edgeworth.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-copying statsmodels/distributions/tests/test_bernstein.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tests
-creating build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/api.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/other_copulas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/depfunc_ev.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/copulas.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/elliptical.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/extreme_value.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/_special.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/archimedean.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
-copying statsmodels/distributions/copula/transforms.py -> build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula
+copying statsmodels/datasets/fertility/__init__.py -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility
 copying statsmodels/LICENSE.txt -> build/lib.linux-x86_64-cpython-313/statsmodels
 copying statsmodels/setup.cfg -> build/lib.linux-x86_64-cpython-313/statsmodels
-copying statsmodels/gam/tests/results/autos_predict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/autos_exog.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/gam_PIRLS_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/motorcycle.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/cubic_cyclic_splines_from_mgcv.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/stats/libqsturng/CH.r -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
+copying statsmodels/stats/libqsturng/LICENSE.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
+copying statsmodels/stats/tests/test_data.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
+copying statsmodels/stats/libqsturng/tests/bootleg.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
+copying statsmodels/stats/tests/results/contingency_table_r_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/wspec2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/framing.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/wspec4.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/influence_measures_bool_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/results_influence_logit.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/bootleg.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/wspec1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/binary_constrict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/wspec3.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/influence_measures_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/influence_lsdiag_R.json -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/stats/tests/results/data.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
+copying statsmodels/discrete/tests/results/phat_mnlogit.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/yhat_mnlogit.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/poisson_resid.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/yhat_poisson.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/nbinom_resids.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/ships.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/sm3533.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/predict_prob_poisson.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/mnlogit_resid.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/discrete/tests/results/mn_logit_summary.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
+copying statsmodels/iolib/tests/results/data_missing.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
+copying statsmodels/iolib/tests/results/time_series_examples.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
+copying statsmodels/multivariate/tests/results/factor_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
+copying statsmodels/multivariate/tests/results/factors_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
+copying statsmodels/sandbox/regression/tests/griliches76.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
+copying statsmodels/miscmodels/tests/results/ologit_ucla.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
+copying statsmodels/duration/tests/results/survival_data_50_2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/survival_data_1000_10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/bmt_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/survival_data_20_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/bmt.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/survival_data_100_5.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
+copying statsmodels/duration/tests/results/survival_data_50_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
 copying statsmodels/gam/tests/results/logit_gam_mgcv.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/gam/tests/results/autos.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
 copying statsmodels/gam/tests/results/prediction_from_mgcv.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_smoothing_generalobscov_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/gam/tests/results/gam_PIRLS_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/autos_exog.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/autos.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/autos_predict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/cubic_cyclic_splines_from_mgcv.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/gam/tests/results/motorcycle.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results
+copying statsmodels/genmod/tests/results/gee_linear_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/iris.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/stata_cancer_glm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/gee_ordinal_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/stata_medpar1_glm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/results_tweedie_aweights_nonrobust.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/enet_poisson.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/gee_nested_linear_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/gee_logistic_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/gee_poisson_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/inv_gaussian.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/epil.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/medparlogresids.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/gee_nominal_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/enet_binomial.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/stata_lbw_glm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/genmod/tests/results/igaussident_resids.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
+copying statsmodels/nonparametric/tests/results/test_lowess_simple.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/results_kde_weights.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/results_kde.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/test_lowess_frac.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/results_kcde.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/results_kernel_regression.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/results_kde_fft.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/test_lowess_delta.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/nonparametric/tests/results/test_lowess_iter.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
+copying statsmodels/regression/tests/results/pastes.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme02.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lasso_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme06.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/dietox.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/theil_textile_predict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme01.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_rls_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme03.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme07.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/results_rls_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme09.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme05.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme11.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme08.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme00.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme04.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/lme10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
+copying statsmodels/treatment/tests/results/cataneo2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_missing_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_level_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_mixed_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_dfm_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_matlab_ssm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing6.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/clark1989.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing0.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3_variates.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_var_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_var_R_output.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_missing_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_intercepts_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing4.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_restricted_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_level_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_missing_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_dfm_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_smoothing2_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_var_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_smoothing3_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_varmax_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_matlab_ssm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_measurement_error_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_smoothing3_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/clark1989.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_smoothing_generalobscov_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_S10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_var_R_output.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing0.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing5.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_missing_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/results_sarimax_coverage.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing4.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_simulation_smoothing1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
+copying statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_measurement_error_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/manufac.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
 copying statsmodels/tsa/statespace/tests/results/sm-0.9-sarimax.pkl -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_112.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_22F.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_111.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_22F.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_222.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_222.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_221.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_112.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-06-29.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
 copying statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-07-29.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
-copying statsmodels/tsa/tests/results/results_corrgram.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_ccf.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/yhat_exact_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_forecasts.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/rand10000.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/resids_exact_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/ARMLEConstantPredict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima212_forecast.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/resids_exact_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/rgnpq.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/bds_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/rgnp.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/y_arma_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/gnpdef.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/stkprc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/AROLSNoConstantPredict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/yhat_css_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/AROLSConstantPredict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/arima111_forecasts.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/yhat_exact_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_forecasts_all_mle_diff.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/resids_css_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_forecasts_all_mle.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/yhat_css_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arma_forecasts.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/results_arima_forecasts_all_css_diff.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/bds_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/resids_css_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/lutkepohl2.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/fit_ets_results_seasonal.json -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/fit_ets_results.json -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
-copying statsmodels/tsa/holtwinters/tests/results/housing-data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results
 copying statsmodels/tsa/vector_ar/tests/results/vars_results.npz -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/e6.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/e5.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/vector_ar/tests/results/e3.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
 copying statsmodels/tsa/vector_ar/tests/results/e1.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
 copying statsmodels/tsa/vector_ar/tests/results/e4.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
 copying statsmodels/tsa/vector_ar/tests/results/e2.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
-copying statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/e3.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/e5.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/vector_ar/tests/results/e6.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results
+copying statsmodels/tsa/tests/results/yhat_exact_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/yhat_exact_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_forecasts_all_mle_diff.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/rgnpq.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/ARMLEConstantPredict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/rand10000.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/AROLSNoConstantPredict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_corrgram.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_forecasts_all_css_diff.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/yhat_css_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/yhat_css_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima111_forecasts.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_ccf.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/arima212_forecast.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_forecasts_all_mle.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/bds_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/y_arma_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/rgnp.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/resids_css_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/resids_exact_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/stkprc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arma_forecasts.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/gnpdef.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/resids_exact_nc.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/AROLSConstantPredict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/resids_css_c.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/bds_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/results_arima_forecasts.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/lutkepohl2.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/fit_ets_results_seasonal.json -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/fit_ets_results.json -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
+copying statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results
 copying statsmodels/tsa/regime_switching/tests/results/results_predict_fedfunds.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results
 copying statsmodels/tsa/regime_switching/tests/results/mar_filardo.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results
+copying statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results
+copying statsmodels/tsa/stl/tests/results/mstl_test_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
+copying statsmodels/tsa/stl/tests/results/mstl_elec_vic.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
 copying statsmodels/tsa/stl/tests/results/stl_test_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
 copying statsmodels/tsa/stl/tests/results/stl_co2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
-copying statsmodels/tsa/stl/tests/results/mstl_elec_vic.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
-copying statsmodels/tsa/stl/tests/results/mstl_test_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results
-copying statsmodels/nonparametric/tests/results/test_lowess_delta.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/test_lowess_iter.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/results_kernel_regression.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/test_lowess_simple.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/results_kde_weights.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/results_kde_fft.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/results_kcde.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/test_lowess_frac.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/nonparametric/tests/results/results_kde.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results
-copying statsmodels/miscmodels/tests/results/ologit_ucla.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results
+copying statsmodels/tsa/holtwinters/tests/results/housing-data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results
 copying statsmodels/othermod/tests/results/methylation-test.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
 copying statsmodels/othermod/tests/results/resid_methylation.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
 copying statsmodels/othermod/tests/results/foodexpenditure.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results
-copying statsmodels/discrete/tests/results/yhat_mnlogit.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/poisson_resid.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/nbinom_resids.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/mnlogit_resid.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/sm3533.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/ships.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/yhat_poisson.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/predict_prob_poisson.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/phat_mnlogit.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/discrete/tests/results/mn_logit_summary.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/discrete/tests/results
-copying statsmodels/datasets/stackloss/stackloss.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
-copying statsmodels/datasets/modechoice/modechoice.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
-copying statsmodels/datasets/macrodata/macrodata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
-copying statsmodels/datasets/macrodata/macrodata.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
-copying statsmodels/datasets/co2/co2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
-copying statsmodels/datasets/cpunish/cpunish.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
-copying statsmodels/datasets/copper/copper.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
-copying statsmodels/datasets/anes96/anes96.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
-copying statsmodels/datasets/heart/heart.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
+copying statsmodels/datasets/nile/nile.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
+copying statsmodels/datasets/china_smoking/china_smoking.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
+copying statsmodels/datasets/committee/committee.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
+copying statsmodels/datasets/scotland/scotvote.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
+copying statsmodels/datasets/statecrime/statecrime.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
 copying statsmodels/datasets/danish_data/data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data
-copying statsmodels/datasets/star98/star98.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
-copying statsmodels/datasets/grunfeld/grunfeld.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld
+copying statsmodels/datasets/anes96/anes96.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96
 copying statsmodels/datasets/longley/longley.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley
-copying statsmodels/datasets/randhie/randhie.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
+copying statsmodels/datasets/copper/copper.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper
+copying statsmodels/datasets/spector/spector.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
+copying statsmodels/datasets/grunfeld/grunfeld.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld
 copying statsmodels/datasets/fair/fair.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair
 copying statsmodels/datasets/fair/fair_pt.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair
-copying statsmodels/datasets/spector/spector.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector
-copying statsmodels/datasets/scotland/scotvote.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland
 copying statsmodels/datasets/ccard/ccard.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard
-copying statsmodels/datasets/strikes/strikes.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
-copying statsmodels/datasets/statecrime/statecrime.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime
+copying statsmodels/datasets/cpunish/cpunish.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish
 copying statsmodels/datasets/elnino/elnino.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino
-copying statsmodels/datasets/nile/nile.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile
-copying statsmodels/datasets/elec_equip/elec_equip.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
-copying statsmodels/datasets/committee/committee.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee
+copying statsmodels/datasets/strikes/strikes.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes
+copying statsmodels/datasets/engel/engel.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
+copying statsmodels/datasets/co2/co2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2
 copying statsmodels/datasets/sunspots/sunspots.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots
+copying statsmodels/datasets/modechoice/modechoice.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice
+copying statsmodels/datasets/heart/heart.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart
+copying statsmodels/datasets/elec_equip/elec_equip.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip
+copying statsmodels/datasets/macrodata/macrodata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
+copying statsmodels/datasets/macrodata/macrodata.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata
+copying statsmodels/datasets/star98/star98.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98
 copying statsmodels/datasets/cancer/cancer.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer
-copying statsmodels/datasets/china_smoking/china_smoking.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking
+copying statsmodels/datasets/randhie/randhie.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie
+copying statsmodels/datasets/stackloss/stackloss.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss
 copying statsmodels/datasets/interest_inflation/E6_jmulti.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation
 copying statsmodels/datasets/interest_inflation/E6.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation
 copying statsmodels/datasets/fertility/fertility.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility
-copying statsmodels/datasets/engel/engel.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel
-copying statsmodels/genmod/tests/results/inv_gaussian.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_ordinal_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/epil.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_nested_linear_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/iris.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/results_tweedie_aweights_nonrobust.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/stata_lbw_glm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_linear_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/stata_medpar1_glm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_poisson_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/enet_binomial.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_logistic_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/gee_nominal_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/medparlogresids.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/igaussident_resids.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/stata_cancer_glm.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/genmod/tests/results/enet_poisson.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/results
-copying statsmodels/stats/tests/test_data.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests
-copying statsmodels/stats/libqsturng/CH.r -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
-copying statsmodels/stats/libqsturng/LICENSE.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng
-copying statsmodels/stats/tests/results/wspec3.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/wspec4.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/framing.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/bootleg.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/influence_measures_bool_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/binary_constrict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/wspec2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/results_influence_logit.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/influence_measures_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/contingency_table_r_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/wspec1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/influence_lsdiag_R.json -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/tests/results/data.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/tests/results
-copying statsmodels/stats/libqsturng/tests/bootleg.dat -> build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests
-copying statsmodels/regression/tests/results/pastes.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme04.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme06.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme08.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme00.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme01.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/theil_textile_predict.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_rls_R.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme07.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme02.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/dietox.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme11.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme05.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/results_rls_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme03.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lme09.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/lasso_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/regression/tests/results/leverage_influence_ols_nostars.txt -> build/lib.linux-x86_64-cpython-313/statsmodels/regression/tests/results
-copying statsmodels/duration/tests/results/survival_data_20_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/bmt.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/survival_data_50_1.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/survival_data_50_2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/survival_data_100_5.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/survival_data_1000_10.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/duration/tests/results/bmt_results.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results
-copying statsmodels/multivariate/tests/results/factors_stata.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
-copying statsmodels/multivariate/tests/results/factor_data.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/multivariate/tests/results
-copying statsmodels/sandbox/regression/tests/griliches76.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/sandbox/regression/tests/racd10data_with_transformed.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/regression/tests
-copying statsmodels/treatment/tests/results/cataneo2.csv -> build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results
-copying statsmodels/iolib/tests/results/data_missing.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
-copying statsmodels/iolib/tests/results/time_series_examples.dta -> build/lib.linux-x86_64-cpython-313/statsmodels/iolib/tests/results
 running build_ext
 building 'statsmodels.tsa.stl._stl' extension
 creating build/temp.linux-x86_64-cpython-313/statsmodels/tsa/stl
@@ -4131,1569 +6715,1569 @@
 running install_lib
 creating build/bdist.linux-x86_64/wheel
 creating build/bdist.linux-x86_64/wheel/statsmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/sequences.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+creating build/bdist.linux-x86_64/wheel/statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_linalg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_grouputils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_decorators.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_eval_measures.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_docstring.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_numdiff.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_sequences.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_web.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_testing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_transform_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_catadd.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_parallel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_rootfinding.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tests/test_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/typing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/sm_exceptions.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/_testing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/_test_runner.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/transform_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/linalg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/numdiff.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/catadd.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/grouputils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/docstring.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/testing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/print_version.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/parallel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/web.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/decorators.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/rng_qrng.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+creating build/bdist.linux-x86_64/wheel/statsmodels/tools/validation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/validation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/validation
+creating build/bdist.linux-x86_64/wheel/statsmodels/tools/validation/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests/test_validation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/validation/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/validation/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/validation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/validation/decorators.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools/validation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/rootfinding.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tools/eval_measures.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tools
+copying build/lib.linux-x86_64-cpython-313/statsmodels/_version.py -> build/bdist.linux-x86_64/wheel/./statsmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/robust
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/_qn.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/robust
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/robust_linear_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust
+creating build/bdist.linux-x86_64/wheel/statsmodels/robust/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/test_norms.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/robust/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results/results_rlm.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/results/results_norms.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/test_scale.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/test_mquantiles.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/tests/test_rlm.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/norms.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust
+copying build/lib.linux-x86_64-cpython-313/statsmodels/robust/scale.py -> build/bdist.linux-x86_64/wheel/./statsmodels/robust
+copying build/lib.linux-x86_64-cpython-313/statsmodels/setup.cfg -> build/bdist.linux-x86_64/wheel/./statsmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/LICENSE.txt -> build/bdist.linux-x86_64/wheel/./statsmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/stats
+creating build/bdist.linux-x86_64/wheel/statsmodels/stats/libqsturng
+copying build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/LICENSE.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/stats/libqsturng
+creating build/bdist.linux-x86_64/wheel/statsmodels/stats/libqsturng/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/stats/libqsturng/tests/bootleg.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/stats/libqsturng/tests
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+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa/movstat.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa/try_fi.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa/diffusion.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa/example_arma.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/tsa/try_arma_more.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/sandbox/mle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/sandbox
+creating build/bdist.linux-x86_64/wheel/statsmodels/miscmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/ordinal_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/count.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/miscmodels/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_ordinal_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/miscmodels/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results/ologit_ucla.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results/results_ordinal_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_generic_mle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results_tmodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_tmodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_poisson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/nonlinls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tmodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/try_mlecov.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/duration
+creating build/bdist.linux-x86_64/wheel/statsmodels/duration/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/test_phreg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/test_survfunc.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_data_50_2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_data_1000_10.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_enet_r_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/bmt_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_data_20_1.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_r_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/phreg_gentests.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/bmt.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_data_100_5.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/tests/results/survival_data_50_1.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/duration/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/survfunc.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/_kernel_estimates.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/hazard_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration
+copying build/lib.linux-x86_64-cpython-313/statsmodels/duration/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/duration
+creating build/bdist.linux-x86_64/wheel/statsmodels/interface
+copying build/lib.linux-x86_64-cpython-313/statsmodels/interface/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/interface
+creating build/bdist.linux-x86_64/wheel/statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/correlation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/functional.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/regressionplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+creating build/bdist.linux-x86_64/wheel/statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_gofplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_agreement.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_mosaicplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_functional.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_boxplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_tsaplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_correlation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_factorplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_regressionplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_dotplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tsaplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/utils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/gofplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/plot_grids.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/plottools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/agreement.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/mosaicplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tukeyplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/boxplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/factorplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/_regressionplots_doc.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
+copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/dotplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
 copying build/lib.linux-x86_64-cpython-313/statsmodels/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/emplike
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/descriptive.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/originregress.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/aft_el.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
+creating build/bdist.linux-x86_64/wheel/statsmodels/emplike/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_aft.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_origin.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/emplike/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results/el_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_descriptive.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_anova.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/elregress.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/elanova.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
+copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
 creating build/bdist.linux-x86_64/wheel/statsmodels/gam
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_penalties.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
-creating build/bdist.linux-x86_64/wheel/statsmodels/gam/gam_cross_validation
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/gam_cross_validation
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation/gam_cross_validation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/gam_cross_validation
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation/cross_validators.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/gam_cross_validation
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/generalized_additive_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_penalties.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
 creating build/bdist.linux-x86_64/wheel/statsmodels/gam/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/test_gam.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/test_penalized.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/test_smooth_basis.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/results_mpg_bs.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/logit_gam_mgcv.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/results_mpg_bs_poisson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/prediction_from_mgcv.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/gam_PIRLS_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/autos_exog.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/autos.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/autos_predict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/autos_exog.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/gam_PIRLS_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/motorcycle.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/cubic_cyclic_splines_from_mgcv.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/motorcycle.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/results_pls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/logit_gam_mgcv.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/results_mpg_bs_poisson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/results_mpg_bs.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/autos.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/tests/results/prediction_from_mgcv.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
+creating build/bdist.linux-x86_64/wheel/statsmodels/gam/gam_cross_validation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation/gam_cross_validation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/gam_cross_validation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/gam_cross_validation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/gam_cross_validation/cross_validators.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam/gam_cross_validation
 copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/smooth_basis.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
+copying build/lib.linux-x86_64-cpython-313/statsmodels/gam/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/gam
+creating build/bdist.linux-x86_64/wheel/statsmodels/genmod
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/generalized_linear_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod
+creating build/bdist.linux-x86_64/wheel/statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/gee_gaussian_simulation_check.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/gee_categorical_simulation_check.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/test_bayes_mixed_glm.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/test_score_test.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/test_gee.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/genmod/tests/test_glm_weights.py -> build/bdist.linux-x86_64/wheel/./statsmodels/genmod/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/genmod/tests/results
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+copying build/lib.linux-x86_64-cpython-313/statsmodels/regression/_prediction.py -> build/bdist.linux-x86_64/wheel/./statsmodels/regression
+copying build/lib.linux-x86_64-cpython-313/statsmodels/regression/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/regression
+copying build/lib.linux-x86_64-cpython-313/statsmodels/regression/quantile_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/regression
+copying build/lib.linux-x86_64-cpython-313/statsmodels/regression/rolling.py -> build/bdist.linux-x86_64/wheel/./statsmodels/regression
+copying build/lib.linux-x86_64-cpython-313/statsmodels/regression/process_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/regression
+creating build/bdist.linux-x86_64/wheel/statsmodels/src
+copying build/lib.linux-x86_64-cpython-313/statsmodels/src/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/src
+creating build/bdist.linux-x86_64/wheel/statsmodels/treatment
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/treatment_effects.py -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment
+creating build/bdist.linux-x86_64/wheel/statsmodels/treatment/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/test_teffects.py -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/treatment/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results/cataneo2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/tests/results/results_teffects.py -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/treatment/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/treatment
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/x13.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/prediction.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/datetools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_prediction.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_base.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_datetools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_tsa_indexes.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tsa_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ar_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/_innovations.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/innovations
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/_arma_innovations.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/innovations/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests/test_arma_innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/arma_innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/descriptivestats.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/varma_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stattools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/mlemodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/varmax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_representation.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/representation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_simulation_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/structural.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/mlemodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_tools.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/news.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/initialization.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/_filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/_univariate.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/_inversions.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/_univariate_diffuse.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/_univariate.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/_conventional.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_filters/_univariate_diffuse.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/cfa_simulation_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_simulation_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_kalman_filter.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_pykalman_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_initialization.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/simulation_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_simulate.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_representation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_varmax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_collapsed.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_conserve_memory.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_univariate.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_dynamic_factor.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_varmax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_weights.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_models.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/kfas_helpers.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_simulate.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_representation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_prediction.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_initialization.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_fixed_params.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_mlemodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_kalman.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_simulation_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_pickle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_news.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_chandrasekhar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_save.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_mlemodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_fixed_params.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_structural.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_impulse_responses.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_forecasting.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_options.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_sarimax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_var.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_conserve_memory.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_univariate.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/kfas_helpers.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_concentrated.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_simulation_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_pickle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_decompose.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_save.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/manufac.dta -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing_generalobscov_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_mixed_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_dfm_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing6.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_dynamic_factor.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/clark1989.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_kalman_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing0.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_varmax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3_variates.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_R_output.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_sarimax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_missing_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_intercepts_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing4.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_restricted_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_misc.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_level_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/sm-0.9-sarimax.pkl -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_missing_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing2_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_varmax_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_level_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_mixed_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_matlab_ssm.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_measurement_error_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing3_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing1.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_R.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_S10.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_112.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_111.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-06-29.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-07-29.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_22F.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_111.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_222.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_222.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_221.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_112.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing5.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results/frbny_nowcast
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing6.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_realgdpar_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_dynamic_factor_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3_variates.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_dynamic_factor.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_varmax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_intercepts_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_restricted_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_structural.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_dfm_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing2_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_common_level_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing3_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/manufac.dta -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_varmax_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/sm-0.9-sarimax.pkl -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_misc.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_kalman_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/clark1989.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_smoothing_generalobscov_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing3.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_S10.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_R_output.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing0.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing5.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_local_linear_trend_missing_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_sarimax_coverage.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_kalman_filter.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_var_R.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing4.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_simulation_smoothing1.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_exact_initial_var1_measurement_error_R.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/results/results_sarimax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_models.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_concentrated.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_chandrasekhar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_var.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_initialization.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_sarimax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_decompose.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_impulse_responses.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_options.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tests/test_kalman.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/structural.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_representation.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/varmax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_cfa_simulation_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/kalman_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/representation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_kalman_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/kalman_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/sarimax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/simulation_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/exponential_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/dynamic_factor_mq.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_initialization.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/dynamic_factor.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/statespace/_smoothers
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_classical.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_alternative.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_univariate.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_conventional.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_cfa_simulation_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_tools.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/kalman_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/cfa_simulation_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/initialization.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_alternative.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace/_smoothers
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/mlemodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/kalman_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/exponential_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_quarterly_ar1.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/_pykalman_smoother.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/deterministic.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/sarimax.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/statespace/dynamic_factor_mq.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/statespace
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ar_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arma_mle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_tsa_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_adfuller_lag.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_ar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_bds.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_x13.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_arima_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_seasonal.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_deterministic.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_exponential_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111nc_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211nc_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/fit_ets_results_seasonal.json -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/savedrvs.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_corrgram.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_ccf.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_exact_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/rand10000.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arma_acf.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_exact_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/ARMLEConstantPredict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima212_forecast.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211nc_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_exact_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/rgnpq.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/bds_data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/rgnp.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/y_arma_data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/gnpdef.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/stkprc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111nc_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/datamlw_tls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/AROLSNoConstantPredict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_css_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/AROLSConstantPredict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111_forecasts.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_exact_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_mle_diff.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_css_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_ar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/make_arma.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_mle.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112nc_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112nc_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_css_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arma_forecasts.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/fit_ets_results.json -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_css_diff.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/bds_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arma.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_css_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/lutkepohl2.dta -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_stattools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/seasonal.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tsatools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/ardl
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/ardl/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests/test_ardl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/ardl/_pss_critical_values
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/_pss_critical_values
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values/pss.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/_pss_critical_values
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values/pss-process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/_pss_critical_values
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/pss_critical_values.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/adfvalues.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/filtertools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/hp_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/cf_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/filters/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/test_filters.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/filters/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results/filter_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/_utils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/bk_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/_bds.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_params.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_specification.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/statespace.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_gls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_yule_walker.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_burg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_statespace.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/durbin_levinson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/hannan_rissanen.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/yule_walker.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/gls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/burg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/params.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/datasets
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/datasets/brockwell_davis_2002
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/oshorts.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/dowj.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/lake.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/specification.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/coint_tables.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/varma_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/x13.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stattools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/forecasting
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/theta.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/forecasting/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests/test_stl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests/test_theta.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests/test_stl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/stl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/theta.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/exponential_smoothing
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/base.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/ets.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/_ets_smooth.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/initialization.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/holtwinters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/holtwinters/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/test_holtwinters.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/holtwinters/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results/housing-data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/_smoothers.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/_exponential_smoothers.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/forecasting/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/forecasting
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/base
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/prediction.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/base/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_prediction.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_base.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_tsa_indexes.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tests/test_datetools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/datetools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/base/tsa_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/base
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/interp
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/interp
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/interp/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/interp/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/tests/test_denton.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/interp/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/interp
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/interp/denton.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/interp
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/coint_tables.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/descriptivestats.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/svar_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/irf.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/util.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/vecm.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/hypothesis_test_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/vector_ar/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_coint.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/vector_ar/tests/Matlab_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/Matlab_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/Matlab_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_var_jmulti.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_svar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_var_output.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/parse_jmulti_vecm_output.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cis_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_diag.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realgdp_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_nc.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_colo_granger_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cos_ir.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cs_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ct_granger_causality_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_co_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_inst_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ci_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_granger_causality_r_dp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_ncs_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_cst_granger_causality_realcons_realgdp.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cili_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_cisli_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_coslo_lagorder.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_c_granger_causality_realcons.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_inst_causality_dp_r.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_Sigmau.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/macrodata_jmulti_nc_granger_causality_realcons_realinv.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/JMulTi_results/vecm_e6_jmulti_ncs_fc5.txt -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/JMulTi_results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_vecm.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_coint.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_svar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/vector_ar/tests/Matlab_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/Matlab_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/Matlab_results/test_coint.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/Matlab_results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_var.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_var_jmulti.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/example_svar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e6.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/vars_results.npz -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e5.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/results_svar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e3.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/results_var.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e1.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e4.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e2.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/vars_results.npz -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/results_svar_st.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/results_var_data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/vecm.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/hypothesis_test_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/output.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/util.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/var_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/results_svar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e2.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e3.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e5.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/results/e6.dat -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/tests/test_var.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/plotting.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/var_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/svar_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/output.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/vector_ar/irf.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/vector_ar
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_seasonal.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_stattools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_ar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_x13.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_tsa_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_adfuller_lag.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_exponential_smoothing.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_deterministic.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_bds.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/fit_ets_results_seasonal.json -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_exact_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_exact_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_exog_forecasts_css.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_mle_diff.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/rgnpq.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/ARMLEConstantPredict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/rand10000.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/AROLSNoConstantPredict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211nc_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_ar_forecast_mle_dynamic.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111nc_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_corrgram.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112nc_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/fit_ets_results.json -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_css_diff.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_css_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/yhat_css_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111_forecasts.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_ccf.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima212_forecast.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_mle.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/bds_data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/y_arma_data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/rgnp.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arma.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima112nc_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111nc_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_css_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211nc_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_exact_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/stkprc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/savedrvs.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts_all_css.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arma_forecasts.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/gnpdef.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_exog_forecasts_mle.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima211_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_exact_nc.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/AROLSConstantPredict.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/datamlw_tls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/lutkepohl2.dta -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/resids_css_c.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/make_arma.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/arima111_css_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/bds_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arma_acf.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_arima_forecasts.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/results/results_ar.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tests/test_arima_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/innovations
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/innovations/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests/test_cython_arma_innovations_fast.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests/test_arma_innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/arma_innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/innovations/_arma_innovations.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/innovations
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/tsatools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/_innovations.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/adfvalues.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/params.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_specification.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_params.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tests/test_tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/specification.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/statespace.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/hannan_rissanen.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/durbin_levinson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_durbin_levinson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_gls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_burg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_yule_walker.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/tests/test_statespace.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/burg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/yule_walker.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/innovations.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/estimators/gls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/estimators
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/datasets
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/datasets/brockwell_davis_2002
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/oshorts.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/dowj.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/lake.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/brockwell_davis_2002/data/sbl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets/brockwell_davis_2002/data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima/datasets/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/arima/datasets
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/hp_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/filtertools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/filters/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/test_filters.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/filters/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/tests/results/filter_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/cf_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/bk_filter.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/_utils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/filters/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/filters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/deterministic.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/mlemodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/regime_switching
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/_kim_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/_hamilton_filter.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/regime_switching/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/test_markov_switching.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/test_markov_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/test_markov_autoregression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/regime_switching/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results/results_predict_fedfunds.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results/mar_filardo.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results/results_predict_rgnp.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/tests/test_markov_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/markov_switching.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/_hamilton_filter.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/markov_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/markov_autoregression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/regime_switching/_kim_smoother.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/regime_switching
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/arima_process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/exponential_smoothing
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/initialization.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/base.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/_ets_smooth.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/exponential_smoothing/ets.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/exponential_smoothing
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/stl
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/mstl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/stl/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/test_stl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/test_mstl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/stl/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/mstl_test_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/mstl_elec_vic.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/stl_test_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/stl_co2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/mstl_elec_vic.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/mstl_test_results.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl
 copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/_stl.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/stl/mstl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/stl
-creating build/bdist.linux-x86_64/wheel/statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/l1_solvers_common.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/l1_slsqp.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/_screening.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/_parameter_inference.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-creating build/bdist.linux-x86_64/wheel/statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_penalties.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_distributed_estimation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_optimize.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_screening.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_penalized.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_generic_methods.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_predict.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_transform.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/tests/test_shrink_pickle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/wrapper.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/_constraints.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/l1_cvxopt.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/covtype.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/_prediction_inference.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/transform.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/optimizer.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/distributed_estimation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/_penalties.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/elastic_net.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-copying build/lib.linux-x86_64-cpython-313/statsmodels/base/_penalized.py -> build/bdist.linux-x86_64/wheel/./statsmodels/base
-creating build/bdist.linux-x86_64/wheel/statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/kernel_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/_smoothers_lowess.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/linbin.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-creating build/bdist.linux-x86_64/wheel/statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_kernels.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_asymmetric.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_kde.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_kernel_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_bandwidths.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_kernel_density.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/test_lowess.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/test_lowess_delta.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/test_lowess_iter.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/results_kernel_regression.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/test_lowess_simple.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/results_kde_weights.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/results_kde_fft.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/results_kde_univ_weights.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/results_kcde.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/test_lowess_frac.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/tests/results/results_kde.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/smoothers_lowess_old.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/kernel_density.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/_kernel_base.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/kernels.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/kernels_asymmetric.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/kde.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/smoothers_lowess.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/bandwidths.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/nonparametric/kdetools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/nonparametric
-copying build/lib.linux-x86_64-cpython-313/statsmodels/conftest.py -> build/bdist.linux-x86_64/wheel/./statsmodels
-creating build/bdist.linux-x86_64/wheel/statsmodels/emplike
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/aft_el.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/elanova.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-creating build/bdist.linux-x86_64/wheel/statsmodels/emplike/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_origin.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_regression.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_aft.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_descriptive.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/test_anova.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/emplike/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/tests/results/el_results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/originregress.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/descriptive.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-copying build/lib.linux-x86_64-cpython-313/statsmodels/emplike/elregress.py -> build/bdist.linux-x86_64/wheel/./statsmodels/emplike
-creating build/bdist.linux-x86_64/wheel/statsmodels/imputation
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/ros.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation
-creating build/bdist.linux-x86_64/wheel/statsmodels/imputation/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests/test_mice.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests/test_bayes_mi.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/tests/test_ros.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/mice.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation
-copying build/lib.linux-x86_64-cpython-313/statsmodels/imputation/bayes_mi.py -> build/bdist.linux-x86_64/wheel/./statsmodels/imputation
-creating build/bdist.linux-x86_64/wheel/statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/boxplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tsaplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/agreement.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/regressionplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/correlation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/mosaicplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-creating build/bdist.linux-x86_64/wheel/statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_correlation.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_regressionplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_boxplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_agreement.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_mosaicplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_functional.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_tsaplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_dotplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_factorplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tests/test_gofplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/_regressionplots_doc.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/functional.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/plot_grids.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/gofplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/dotplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/plottools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/tukeyplot.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/utils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-copying build/lib.linux-x86_64-cpython-313/statsmodels/graphics/factorplots.py -> build/bdist.linux-x86_64/wheel/./statsmodels/graphics
-creating build/bdist.linux-x86_64/wheel/statsmodels/miscmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-creating build/bdist.linux-x86_64/wheel/statsmodels/miscmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_tmodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results_tmodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_generic_mle.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_ordinal_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/test_poisson.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests
-creating build/bdist.linux-x86_64/wheel/statsmodels/miscmodels/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results/ologit_ucla.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tests/results/results_ordinal_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/try_mlecov.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/count.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/nonlinls.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/tmodel.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-copying build/lib.linux-x86_64-cpython-313/statsmodels/miscmodels/ordinal_model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/miscmodels
-creating build/bdist.linux-x86_64/wheel/statsmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tests/test_package.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/tests/test_x13.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/_version.py -> build/bdist.linux-x86_64/wheel/./statsmodels
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/ardl
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/ardl/_pss_critical_values
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values/pss.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/_pss_critical_values
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values/pss-process.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/_pss_critical_values
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/_pss_critical_values/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/_pss_critical_values
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/ardl/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/tests/test_ardl.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/pss_critical_values.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/ardl/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/ardl
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/seasonal.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/holtwinters
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/holtwinters/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/tsa/holtwinters/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/results/housing-data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/tests/test_holtwinters.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/model.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/_exponential_smoothers.cpython-313-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/_smoothers.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/holtwinters/results.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa/holtwinters
+copying build/lib.linux-x86_64-cpython-313/statsmodels/tsa/_bds.py -> build/bdist.linux-x86_64/wheel/./statsmodels/tsa
+copying build/lib.linux-x86_64-cpython-313/statsmodels/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels
 creating build/bdist.linux-x86_64/wheel/statsmodels/othermod
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod
 creating build/bdist.linux-x86_64/wheel/statsmodels/othermod/tests
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests
 copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/test_beta.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests
 creating build/bdist.linux-x86_64/wheel/statsmodels/othermod/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/methylation-test.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/results_betareg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
-copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/resid_methylation.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice/modechoice.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/modechoice
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/modechoice
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/macrodata.dta -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/macrodata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2/co2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/co2
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/co2
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cpunish
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cpunish
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper/copper.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/copper
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/copper
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96/anes96.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/anes96
-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/anes96
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart/heart.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/heart
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data/data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/danish_data
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-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/other_copulas.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/depfunc_ev.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/copulas.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/elliptical.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/extreme_value.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/_special.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/archimedean.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/copula/transforms.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions/copula
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/tools.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/discrete.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/empirical_distribution.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions
-copying build/lib.linux-x86_64-cpython-313/statsmodels/distributions/mixture_rvs.py -> build/bdist.linux-x86_64/wheel/./statsmodels/distributions
-creating build/bdist.linux-x86_64/wheel/statsmodels/interface
-copying build/lib.linux-x86_64-cpython-313/statsmodels/interface/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/interface
-copying build/lib.linux-x86_64-cpython-313/statsmodels/setup.cfg -> build/bdist.linux-x86_64/wheel/./statsmodels
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/methylation-test.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/results_betareg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/resid_methylation.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/tests/results/foodexpenditure.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod/tests/results
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/betareg.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod
+copying build/lib.linux-x86_64-cpython-313/statsmodels/othermod/api.py -> build/bdist.linux-x86_64/wheel/./statsmodels/othermod
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/template_data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/nile
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/nile
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/nile
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/nile/nile.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/nile
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/china_smoking
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking/china_smoking.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/china_smoking
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/china_smoking
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/china_smoking/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/china_smoking
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/committee
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/committee
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/committee
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/committee/committee.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/committee
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/scotland
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/scotland
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/scotland
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/scotland/scotvote.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/scotland
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/statecrime
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/statecrime
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/statecrime
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/statecrime/statecrime.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/statecrime
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/danish_data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data/data.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/danish_data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/danish_data
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/danish_data/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/danish_data
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/anes96
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/anes96
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/anes96
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/anes96/anes96.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/anes96
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/longley
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley/longley.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/longley
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/longley
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/longley/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/longley
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/copper
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/copper
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/copper
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/copper/copper.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/copper
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests/test_data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/tests
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/tests/test_utils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/tests
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/spector
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector/spector.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/spector
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/spector
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/spector/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/spector
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/grunfeld
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld/grunfeld.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/grunfeld
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/grunfeld
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/grunfeld/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/grunfeld
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/fair
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair/fair.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fair
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair/fair_pt.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fair
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fair
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fair/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fair
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/ccard
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/ccard
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/ccard
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/ccard/ccard.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/ccard
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/cpunish
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish/cpunish.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cpunish
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cpunish
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cpunish/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cpunish
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/elnino
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino/elnino.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/elnino
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/elnino
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elnino/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/elnino
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/strikes
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes/strikes.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/strikes
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/strikes
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/strikes/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/strikes
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/engel
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/engel
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/engel
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/engel/engel.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/engel
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/co2
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2/co2.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/co2
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/co2
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/co2/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/co2
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/utils.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/sunspots
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/sunspots
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/sunspots
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/sunspots/sunspots.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/sunspots
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/modechoice
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice/modechoice.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/modechoice
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/modechoice
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/modechoice/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/modechoice
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/heart
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/heart
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/heart
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/heart/heart.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/heart
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/elec_equip
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/elec_equip
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/elec_equip
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/elec_equip/elec_equip.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/elec_equip
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/macrodata
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/macrodata.dta -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/macrodata/macrodata.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/macrodata
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/star98
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/star98
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/star98
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/star98/star98.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/star98
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/cancer
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer/cancer.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cancer
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cancer
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/cancer/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/cancer
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/randhie
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/randhie
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/randhie
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/randhie/randhie.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/randhie
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/stackloss
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss/stackloss.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/stackloss
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/stackloss
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/stackloss/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/stackloss
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/interest_inflation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation/E6_jmulti.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/interest_inflation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation/E6.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/interest_inflation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/interest_inflation
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/interest_inflation/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/interest_inflation
+creating build/bdist.linux-x86_64/wheel/statsmodels/datasets/fertility
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility/fertility.csv -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fertility
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility/data.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fertility
+copying build/lib.linux-x86_64-cpython-313/statsmodels/datasets/fertility/__init__.py -> build/bdist.linux-x86_64/wheel/./statsmodels/datasets/fertility
+copying build/lib.linux-x86_64-cpython-313/statsmodels/conftest.py -> build/bdist.linux-x86_64/wheel/./statsmodels
 running install_egg_info
 running egg_info
 creating statsmodels.egg-info
@@ -5854,7 +8438,7 @@
 Copying statsmodels.egg-info to build/bdist.linux-x86_64/wheel/./statsmodels-0.14.5+dfsg.egg-info
 running install_scripts
 creating build/bdist.linux-x86_64/wheel/statsmodels-0.14.5+dfsg.dist-info/WHEEL
-creating '/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/.tmp-o5xuvxn6/statsmodels-0.14.5+dfsg-cp313-cp313-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it
+creating '/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/.tmp-hq42lkzi/statsmodels-0.14.5+dfsg-cp313-cp313-linux_x86_64.whl' and adding 'build/bdist.linux-x86_64/wheel' to it
 adding 'statsmodels/LICENSE.txt'
 adding 'statsmodels/__init__.py'
 adding 'statsmodels/_version.py'
@@ -7268,476 +9852,476 @@
 : # 0.14.5 sphinx-build only: in reading sources, last line output is statsmodels.functional.graphics.hdrboxplot
 mkdir -p build/html
 python3 debian/datasets/prepopulate_cache.py
-./boot/grav.html
-./boot/cane.html
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+./carData/Ericksen.html
+./carData/Greene.html
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-./carData/Burt.html
-./carData/USPop.html
-./carData/Freedman.html
-./carData/Chile.html
-./carData/Baumann.html
-./carData/Wong.html
-./carData/Quartet.html
-./carData/Leinhardt.html
-./carData/Pottery.html
-./carData/Hartnagel.html
-./carData/Womenlf.html
-./carData/Mandel.html
-./carData/Wells.html
-./carData/Cowles.html
-./carData/MplsStops.html
-./carData/Soils.html
-./carData/Prestige.html
-./lme4/Dyestuff2.html
-./lme4/Dyestuff.html
-./lme4/cbpp.html
-./lme4/Penicillin.html
-./lme4/Pastes.html
-./lme4/sleepstudy.html
-./lme4/Arabidopsis.html
-./lme4/InstEval.html
-./lme4/grouseticks.html
-./lme4/cake.html
-./lme4/VerbAgg.html
+./datasets/ChickWeight.html
+./datasets/Orange.html
+./datasets/attenu.html
+./datasets/rock.html
+./datasets/UKDriverDeaths.html
 LC_ALL=C.UTF-8 LANGUAGE=C.UTF-8 python3 tools/export_notebooks_to_python.py
-Converting statespace_arma_0
-Converting regression_plots
-Converting predict
-Converting distributed_estimation
-Converting stats_rankcompare
-Converting copula
-Converting glm_weights
-Converting robust_models_0
 Converting mixed_lm_example
-Converting autoregressions
-Converting chi2_fitting
+Converting statespace_local_linear_trend
+Converting gee_score_test_simulation
 Converting robust_models_1
-Converting statespace_tvpvar_mcmc_cfa
-Converting linear_regression_diagnostics_plots
-Converting regression_diagnostics
-Converting markov_autoregression
-Converting mediation_survival
-Converting generic_mle
-Converting pca_fertility_factors
-Converting formulas
-Converting plots_boxplots
-Converting stl_decomposition
-Converting rolling_ls
-Converting wls
-Converting statespace_sarimax_stata
-Converting markov_regression
-Converting count_hurdle
-Converting glm_formula
+Converting robust_models_0
 Converting statespace_concentrated_scale
-Converting statespace_structural_harvey_jaeger
-Converting quantile_regression
-Converting tsa_arma_1
-Converting statespace_chandrasekhar
-Converting theta-model
-Converting tsa_arma_0
-Converting discrete_choice_example
-Converting stationarity_detrending_adf_kpss
-Converting categorical_interaction_plot
-Converting statespace_custom_models
-Converting stats_poisson
-Converting statespace_sarimax_faq
-Converting statespace_cycles
-Converting quasibinomial
-Converting gee_score_test_simulation
-Converting statespace_fixed_params
-Converting statespace_local_linear_trend
-Converting glm
-Converting influence_glm_logit
+Converting predict
 Converting gls
-Converting interactions_anova
+Converting statespace_fixed_params
+Converting kernel_density
+Converting distributed_estimation
+Converting statespace_sarimax_stata
+Converting discrete_choice_example
 Converting statespace_sarimax_internet
+Converting statespace_chandrasekhar
+Converting pca_fertility_factors
+Converting autoregressive_distributed_lag
 Converting statespace_forecasting
-Converting deterministics
-Converting statespace_news
+Converting ordinal_regression
 Converting discrete_choice_overview
-Converting autoregressive_distributed_lag
-Converting contrasts
-Converting ets
-Converting postestimation_poisson
+Converting stats_poisson
+Converting regression_diagnostics
 Converting statespace_varmax
-Converting statespace_dfm_coincident
-Converting variance_components
-Converting tsa_filters
-Converting tsa_dates
-Converting ordinal_regression
+Converting quantile_regression
+Converting statespace_sarimax_pymc3
+Converting statespace_custom_models
+Converting copula
+Converting wls
+Converting categorical_interaction_plot
 Converting mstl_decomposition
-Converting gee_nested_simulation
-Converting metaanalysis1
-Converting ols
+Converting statespace_tvpvar_mcmc_cfa
+Converting statespace_structural_harvey_jaeger
+Converting autoregressions
+Converting ets
+Converting stats_rankcompare
+Converting count_hurdle
+Converting tsa_arma_1
 Converting statespace_seasonal
-Converting treatment_effect
+Converting postestimation_poisson
 Converting lowess
-Converting recursive_ls
-Converting statespace_sarimax_pymc3
+Converting formulas
+Converting gee_nested_simulation
+Converting tsa_filters
+Converting theta-model
+Converting markov_autoregression
+Converting variance_components
+Converting rolling_ls
+Converting chi2_fitting
+Converting linear_regression_diagnostics_plots
+Converting statespace_sarimax_faq
+Converting glm_weights
+Converting regression_plots
+Converting influence_glm_logit
 Converting exponential_smoothing
-Converting kernel_density
+Converting statespace_arma_0
+Converting treatment_effect
+Converting markov_regression
+Converting stationarity_detrending_adf_kpss
+Converting statespace_news
+Converting recursive_ls
+Converting mediation_survival
+Converting ols
+Converting plots_boxplots
+Converting stl_decomposition
+Converting generic_mle
+Converting deterministics
+Converting quasibinomial
+Converting metaanalysis1
+Converting statespace_dfm_coincident
+Converting tsa_dates
+Converting statespace_cycles
+Converting contrasts
+Converting glm
+Converting glm_formula
+Converting interactions_anova
+Converting tsa_arma_0
 PYTHONPATH=/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build \
 BUILDDIR=/build/reproducible-path/statsmodels-0.14.5+dfsg/build \
 STATSMODELS_DATA=/build/reproducible-path/statsmodels-0.14.5+dfsg/build/datacache/ \
@@ -7780,46 +10364,68 @@
 mkdir -p build/source/examples/notebooks/generated
 # Black list notebooks from doc build here
 ../tools/nbgenerate.py --parallel --report-errors --skip-existing --execute-only --execution-blacklist statespace_custom_models
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_arma_0.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_arma_0.ipynbExecuting /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/predict.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/predict.ipynb
-
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/distributed_estimation.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/distributed_estimation.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/regression_plots.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_plots.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/glm_weights.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_weights.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/copula.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/copula.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stats_rankcompare.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_rankcompare.ipynb
 Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mixed_lm_example.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mixed_lm_example.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/robust_models_0.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_0.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/autoregressions.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressions.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/chi2_fitting.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/chi2_fitting.ipynb
 Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/robust_models_1.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_1.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_tvpvar_mcmc_cfa.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/regression_diagnostics.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_diagnostics.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/linear_regression_diagnostics_plots.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/linear_regression_diagnostics_plots.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/markov_autoregression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_autoregression.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mediation_survival.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mediation_survival.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/generic_mle.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/generic_mle.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/pca_fertility_factors.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/pca_fertility_factors.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/formulas.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/formulas.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/plots_boxplots.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/plots_boxplots.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stl_decomposition.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stl_decomposition.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/rolling_ls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/rolling_ls.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/gee_score_test_simulation.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gee_score_test_simulation.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_local_linear_trend.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_local_linear_trend.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/robust_models_0.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_0.ipynbExecuting /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_concentrated_scale.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_concentrated_scale.ipynb
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/predict.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/predict.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/gls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gls.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/kernel_density.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/kernel_density.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_fixed_params.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_fixed_params.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/distributed_estimation.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/distributed_estimation.ipynb
 Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_stata.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_stata.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/discrete_choice_example.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/discrete_choice_example.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_internet.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_internet.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_chandrasekhar.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_chandrasekhar.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/pca_fertility_factors.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/pca_fertility_factors.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/autoregressive_distributed_lag.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressive_distributed_lag.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_forecasting.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_forecasting.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ordinal_regression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ordinal_regression.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/discrete_choice_overview.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/discrete_choice_overview.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/regression_diagnostics.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_diagnostics.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_varmax.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_varmax.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stats_poisson.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_poisson.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/quantile_regression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quantile_regression.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/copula.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/copula.ipynb
 Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/wls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/wls.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/count_hurdle.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/count_hurdle.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/markov_regression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_regression.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_concentrated_scale.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_concentrated_scale.ipynbExecuting /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/glm_formula.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_formula.ipynb
-
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_pymc3.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_pymc3.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/categorical_interaction_plot.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/categorical_interaction_plot.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mstl_decomposition.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mstl_decomposition.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_tvpvar_mcmc_cfa.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb
 Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_structural_harvey_jaeger.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_structural_harvey_jaeger.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/quantile_regression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quantile_regression.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/autoregressions.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressions.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ets.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ets.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stats_rankcompare.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_rankcompare.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/count_hurdle.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/count_hurdle.ipynb
 Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_arma_1.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_arma_1.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_chandrasekhar.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_chandrasekhar.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/theta-model.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/theta-model.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_arma_0.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_arma_0.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/discrete_choice_example.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/discrete_choice_example.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stationarity_detrending_adf_kpss.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stationarity_detrending_adf_kpss.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/categorical_interaction_plot.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/categorical_interaction_plot.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stats_poisson.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_poisson.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_faq.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_faq.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_seasonal.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_seasonal.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/postestimation_poisson.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/postestimation_poisson.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/lowess.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/lowess.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/formulas.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/formulas.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/gee_nested_simulation.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gee_nested_simulation.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_filters.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_filters.ipynb
+Exception in thread Heartbeat:
+Traceback (most recent call last):
+  File "/usr/lib/python3.13/threading.py", line 1043, in _bootstrap_inner
+    self.run()
+    ~~~~~~~~^^
+  File "/usr/lib/python3/dist-packages/ipykernel/heartbeat.py", line 99, in run
+    self._bind_socket()
+    ~~~~~~~~~~~~~~~~~^^
+  File "/usr/lib/python3/dist-packages/ipykernel/heartbeat.py", line 78, in _bind_socket
+    self._try_bind_socket()
+    ~~~~~~~~~~~~~~~~~~~~~^^
+  File "/usr/lib/python3/dist-packages/ipykernel/heartbeat.py", line 65, in _try_bind_socket
+    return self.socket.bind(f"{self.transport}://{self.ip}" + c + str(self.port))
+           ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/lib/python3/dist-packages/zmq/sugar/socket.py", line 320, in bind
+    super().bind(addr)
+    ~~~~~~~~~~~~^^^^^^
+  File "_zmq.py", line 1009, in zmq.backend.cython._zmq.Socket.bind
+  File "_zmq.py", line 190, in zmq.backend.cython._zmq._check_rc
+zmq.error.ZMQError: Address already in use (addr='tcp://127.0.0.1:36375')
 Traceback (most recent call last):
   File "/usr/lib/python3.13/runpy.py", line 198, in _run_module_as_main
     return _run_code(code, main_globals, None,
@@ -7838,8 +10444,11 @@
   File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 692, in initialize
     self.init_sockets()
     ~~~~~~~~~~~~~~~~~^^
-  File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 331, in init_sockets
-    self.shell_port = self._bind_socket(self.shell_socket, self.shell_port)
+  File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 346, in init_sockets
+    self.init_iopub(context)
+    ~~~~~~~~~~~~~~~^^^^^^^^^
+  File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 375, in init_iopub
+    self.iopub_port = self._bind_socket(self.iopub_socket, self.iopub_port)
                       ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
   File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 253, in _bind_socket
     return self._try_bind_socket(s, port)
@@ -7852,280 +10461,78 @@
     ~~~~~~~~~~~~^^^^^^
   File "_zmq.py", line 1009, in zmq.backend.cython._zmq.Socket.bind
   File "_zmq.py", line 190, in zmq.backend.cython._zmq._check_rc
-zmq.error.ZMQError: Address already in use (addr='tcp://127.0.0.1:40123')
-
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/theta-model.ipynb
-An error occurred while executing the following cell:
-------------------
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import pandas_datareader as pdr
-import seaborn as sns
-
-plt.rc("figure", figsize=(16, 8))
-plt.rc("font", size=15)
-plt.rc("lines", linewidth=3)
-sns.set_style("darkgrid")
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 4
-      2 import numpy as np
-      3 import pandas as pd
-----> 4 import pandas_datareader as pdr
-      5 import seaborn as sns
-      7 plt.rc("figure", figsize=(16, 8))
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-An error occurred while executing the following cell:
-------------------
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import pandas_datareader as pdr
-import seaborn as sns
-
-plt.rc("figure", figsize=(16, 8))
-plt.rc("font", size=15)
-plt.rc("lines", linewidth=3)
-sns.set_style("darkgrid")
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 4
-      2 import numpy as np
-      3 import pandas as pd
-----> 4 import pandas_datareader as pdr
-      5 import seaborn as sns
-      7 plt.rc("figure", figsize=(16, 8))
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-******************************************************************************
-
-
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_cycles.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_cycles.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/quasibinomial.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quasibinomial.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/gee_score_test_simulation.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gee_score_test_simulation.ipynb
-
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/rolling_ls.ipynb
-An error occurred while executing the following cell:
-------------------
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import pandas_datareader as pdr
-import seaborn
-
-import statsmodels.api as sm
-from statsmodels.regression.rolling import RollingOLS
-
-seaborn.set_style("darkgrid")
-pd.plotting.register_matplotlib_converters()
-%matplotlib inline
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 4
-      2 import numpy as np
-      3 import pandas as pd
-----> 4 import pandas_datareader as pdr
-      5 import seaborn
-      7 import statsmodels.api as sm
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-An error occurred while executing the following cell:
-------------------
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import pandas_datareader as pdr
-import seaborn
-
-import statsmodels.api as sm
-from statsmodels.regression.rolling import RollingOLS
-
-seaborn.set_style("darkgrid")
-pd.plotting.register_matplotlib_converters()
-%matplotlib inline
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 4
-      2 import numpy as np
-      3 import pandas as pd
-----> 4 import pandas_datareader as pdr
-      5 import seaborn
-      7 import statsmodels.api as sm
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-******************************************************************************
-
-
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_fixed_params.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_fixed_params.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_local_linear_trend.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_local_linear_trend.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/glm.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/influence_glm_logit.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/influence_glm_logit.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/gls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gls.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/interactions_anova.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/interactions_anova.ipynb
+zmq.error.ZMQError: Address already in use (addr='tcp://127.0.0.1:33499')
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/theta-model.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/theta-model.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/markov_autoregression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_autoregression.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/variance_components.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/variance_components.ipynb
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/markov_autoregression.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_pymc3.ipynb
 An error occurred while executing the following cell:
 ------------------
 %matplotlib inline
-
-from datetime import datetime
-from io import BytesIO
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import requests
+import pymc3 as pm
 import statsmodels.api as sm
-
-# NBER recessions
+import theano
+import theano.tensor as tt
+from pandas.plotting import register_matplotlib_converters
 from pandas_datareader.data import DataReader
 
-usrec = DataReader(
-    "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
-)
+plt.style.use("seaborn")
+register_matplotlib_converters()
 ------------------
 
 
 ---------------------------------------------------------------------------
 ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 13
-     10 import statsmodels.api as sm
-     12 # NBER recessions
----> 13 from pandas_datareader.data import DataReader
-     15 usrec = DataReader(
-     16     "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
-     17 )
+Cell In[1], line 5
+      3 import numpy as np
+      4 import pandas as pd
+----> 5 import pymc3 as pm
+      6 import statsmodels.api as sm
+      7 import theano
 
-ModuleNotFoundError: No module named 'pandas_datareader'
+ModuleNotFoundError: No module named 'pymc3'
 
 An error occurred while executing the following cell:
 ------------------
 %matplotlib inline
-
-from datetime import datetime
-from io import BytesIO
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import requests
+import pymc3 as pm
 import statsmodels.api as sm
-
-# NBER recessions
-from pandas_datareader.data import DataReader
-
-usrec = DataReader(
-    "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
-)
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 13
-     10 import statsmodels.api as sm
-     12 # NBER recessions
----> 13 from pandas_datareader.data import DataReader
-     15 usrec = DataReader(
-     16     "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
-     17 )
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-******************************************************************************
-
-
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_internet.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_internet.ipynb
-
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_structural_harvey_jaeger.ipynb
-An error occurred while executing the following cell:
-------------------
-# Datasets
-from pandas_datareader.data import DataReader
-
-# Get the raw data
-start = '1948-01'
-end = '2008-01'
-us_gnp = DataReader('GNPC96', 'fred', start=start, end=end)
-us_gnp_deflator = DataReader('GNPDEF', 'fred', start=start, end=end)
-us_monetary_base = DataReader('AMBSL', 'fred', start=start, end=end).resample('QS').mean()
-recessions = DataReader('USRECQ', 'fred', start=start, end=end).resample('QS').last().values[:,0]
-
-# Construct the dataframe
-dta = pd.concat(map(np.log, (us_gnp, us_gnp_deflator, us_monetary_base)), axis=1)
-dta.columns = ['US GNP','US Prices','US monetary base']
-dta.index.freq = dta.index.inferred_freq
-dates = dta.index._mpl_repr()
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 2
-      1 # Datasets
-----> 2 from pandas_datareader.data import DataReader
-      4 # Get the raw data
-      5 start = '1948-01'
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-An error occurred while executing the following cell:
-------------------
-# Datasets
+import theano
+import theano.tensor as tt
+from pandas.plotting import register_matplotlib_converters
 from pandas_datareader.data import DataReader
 
-# Get the raw data
-start = '1948-01'
-end = '2008-01'
-us_gnp = DataReader('GNPC96', 'fred', start=start, end=end)
-us_gnp_deflator = DataReader('GNPDEF', 'fred', start=start, end=end)
-us_monetary_base = DataReader('AMBSL', 'fred', start=start, end=end).resample('QS').mean()
-recessions = DataReader('USRECQ', 'fred', start=start, end=end).resample('QS').last().values[:,0]
-
-# Construct the dataframe
-dta = pd.concat(map(np.log, (us_gnp, us_gnp_deflator, us_monetary_base)), axis=1)
-dta.columns = ['US GNP','US Prices','US monetary base']
-dta.index.freq = dta.index.inferred_freq
-dates = dta.index._mpl_repr()
+plt.style.use("seaborn")
+register_matplotlib_converters()
 ------------------
 
 
 ---------------------------------------------------------------------------
 ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 2
-      1 # Datasets
-----> 2 from pandas_datareader.data import DataReader
-      4 # Get the raw data
-      5 start = '1948-01'
+Cell In[1], line 5
+      3 import numpy as np
+      4 import pandas as pd
+----> 5 import pymc3 as pm
+      6 import statsmodels.api as sm
+      7 import theano
 
-ModuleNotFoundError: No module named 'pandas_datareader'
+ModuleNotFoundError: No module named 'pymc3'
 
 ******************************************************************************
 
 
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_forecasting.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_forecasting.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/rolling_ls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/rolling_ls.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/chi2_fitting.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/chi2_fitting.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/linear_regression_diagnostics_plots.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/linear_regression_diagnostics_plots.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_faq.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_faq.ipynb
 
 ******************************************************************************
 ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/autoregressions.ipynb
@@ -8178,18 +10585,15 @@
 ******************************************************************************
 
 
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/deterministics.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/deterministics.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_news.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_news.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/discrete_choice_overview.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/discrete_choice_overview.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/glm_weights.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_weights.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/regression_plots.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_plots.ipynb
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/linear_regression_diagnostics_plots.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ordinal_regression.ipynb
 An error occurred while executing the following cell:
 ------------------
-# Load data
-data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
-df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
-df.head()
+url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
+data_student = pd.read_stata(url)
 ------------------
 
 
@@ -8251,54 +10655,55 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[2], line 3
-      1 # Load data
-      2 data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
-----> 3 df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
-      4 df.head()
+Cell In[2], line 2
+      1 url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
+----> 2 data_student = pd.read_stata(url)
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
-   1013 kwds_defaults = _refine_defaults_read(
-   1014     dialect,
-   1015     delimiter,
-   (...)
-   1022     dtype_backend=dtype_backend,
-   1023 )
-   1024 kwds.update(kwds_defaults)
--> 1026 return _read(filepath_or_buffer, kwds)
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:2117, in read_stata(filepath_or_buffer, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals, chunksize, iterator, compression, storage_options)
+   2114     return reader
+   2116 with reader:
+-> 2117     return reader.read()
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
-    617 _validate_names(kwds.get("names", None))
-    619 # Create the parser.
---> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
-    622 if chunksize or iterator:
-    623     return parser
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:1691, in StataReader.read(self, nrows, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals)
+   1679 @Appender(_read_method_doc)
+   1680 def read(
+   1681     self,
+   (...)
+   1689     order_categoricals: bool | None = None,
+   1690 ) -> DataFrame:
+-> 1691     self._ensure_open()
+   1693     # Handle options
+   1694     if convert_dates is None:
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
-   1617     self.options["has_index_names"] = kwds["has_index_names"]
-   1619 self.handles: IOHandles | None = None
--> 1620 self._engine = self._make_engine(f, self.engine)
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:1183, in StataReader._ensure_open(self)
+   1179 """
+   1180 Ensure the file has been opened and its header data read.
+   1181 """
+   1182 if not hasattr(self, "_path_or_buf"):
+-> 1183     self._open_file()
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
-   1878     if "b" not in mode:
-   1879         mode += "b"
--> 1880 self.handles = get_handle(
-   1881     f,
-   1882     mode,
-   1883     encoding=self.options.get("encoding", None),
-   1884     compression=self.options.get("compression", None),
-   1885     memory_map=self.options.get("memory_map", False),
-   1886     is_text=is_text,
-   1887     errors=self.options.get("encoding_errors", "strict"),
-   1888     storage_options=self.options.get("storage_options", None),
-   1889 )
-   1890 assert self.handles is not None
-   1891 f = self.handles.handle
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:1196, in StataReader._open_file(self)
+   1189 if not self._entered:
+   1190     warnings.warn(
+   1191         "StataReader is being used without using a context manager. "
+   1192         "Using StataReader as a context manager is the only supported method.",
+   1193         ResourceWarning,
+   1194         stacklevel=find_stack_level(),
+   1195     )
+-> 1196 handles = get_handle(
+   1197     self._original_path_or_buf,
+   1198     "rb",
+   1199     storage_options=self._storage_options,
+   1200     is_text=False,
+   1201     compression=self._compression,
+   1202 )
+   1203 if hasattr(handles.handle, "seekable") and handles.handle.seekable():
+   1204     # If the handle is directly seekable, use it without an extra copy.
+   1205     self._path_or_buf = handles.handle
 
 File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
     725     codecs.lookup_error(errors)
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/autoregressive_distributed_lag.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressive_distributed_lag.ipynb    727 # open URLs
-
+    727 # open URLs
 --> 728 ioargs = _get_filepath_or_buffer(
     729     path_or_buf,
     730     encoding=encoding,
@@ -8332,7 +10737,8 @@
 
 File /usr/lib/python3.13/urllib/request.py:489, in OpenerDirector.open(self, fullurl, data, timeout)
     486     req = meth(req)
-    488 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
+    488 sys.audit(Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/influence_glm_logit.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/influence_glm_logit.ipynb
+'urllib.Request', req.full_url, req.data, req.headers, req.get_method())
 --> 489 response = self._open(req, data)
     491 # post-process response
     492 meth_name = protocol+"_response"
@@ -8369,10 +10775,8 @@
 
 An error occurred while executing the following cell:
 ------------------
-# Load data
-data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
-df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
-df.head()
+url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
+data_student = pd.read_stata(url)
 ------------------
 
 
@@ -8434,49 +10838,51 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[2], line 3
-      1 # Load data
-      2 data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
-----> 3 df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
-      4 df.head()
+Cell In[2], line 2
+      1 url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
+----> 2 data_student = pd.read_stata(url)
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
-   1013 kwds_defaults = _refine_defaults_read(
-   1014     dialect,
-   1015     delimiter,
-   (...)
-   1022     dtype_backend=dtype_backend,
-   1023 )
-   1024 kwds.update(kwds_defaults)
--> 1026 return _read(filepath_or_buffer, kwds)
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:2117, in read_stata(filepath_or_buffer, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals, chunksize, iterator, compression, storage_options)
+   2114     return reader
+   2116 with reader:
+-> 2117     return reader.read()
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
-    617 _validate_names(kwds.get("names", None))
-    619 # Create the parser.
---> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
-    622 if chunksize or iterator:
-    623     return parser
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:1691, in StataReader.read(self, nrows, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals)
+   1679 @Appender(_read_method_doc)
+   1680 def read(
+   1681     self,
+   (...)
+   1689     order_categoricals: bool | None = None,
+   1690 ) -> DataFrame:
+-> 1691     self._ensure_open()
+   1693     # Handle options
+   1694     if convert_dates is None:
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
-   1617     self.options["has_index_names"] = kwds["has_index_names"]
-   1619 self.handles: IOHandles | None = None
--> 1620 self._engine = self._make_engine(f, self.engine)
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:1183, in StataReader._ensure_open(self)
+   1179 """
+   1180 Ensure the file has been opened and its header data read.
+   1181 """
+   1182 if not hasattr(self, "_path_or_buf"):
+-> 1183     self._open_file()
 
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
-   1878     if "b" not in mode:
-   1879         mode += "b"
--> 1880 self.handles = get_handle(
-   1881     f,
-   1882     mode,
-   1883     encoding=self.options.get("encoding", None),
-   1884     compression=self.options.get("compression", None),
-   1885     memory_map=self.options.get("memory_map", False),
-   1886     is_text=is_text,
-   1887     errors=self.options.get("encoding_errors", "strict"),
-   1888     storage_options=self.options.get("storage_options", None),
-   1889 )
-   1890 assert self.handles is not None
-   1891 f = self.handles.handle
+File /usr/lib/python3/dist-packages/pandas/io/stata.py:1196, in StataReader._open_file(self)
+   1189 if not self._entered:
+   1190     warnings.warn(
+   1191         "StataReader is being used without using a context manager. "
+   1192         "Using StataReader as a context manager is the only supported method.",
+   1193         ResourceWarning,
+   1194         stacklevel=find_stack_level(),
+   1195     )
+-> 1196 handles = get_handle(
+   1197     self._original_path_or_buf,
+   1198     "rb",
+   1199     storage_options=self._storage_options,
+   1200     is_text=False,
+   1201     compression=self._compression,
+   1202 )
+   1203 if hasattr(handles.handle, "seekable") and handles.handle.seekable():
+   1204     # If the handle is directly seekable, use it without an extra copy.
+   1205     self._path_or_buf = handles.handle
 
 File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
     725     codecs.lookup_error(errors)
@@ -8552,17 +10958,185 @@
 ******************************************************************************
 
 
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/contrasts.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/contrasts.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ets.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ets.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/postestimation_poisson.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/postestimation_poisson.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_varmax.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_varmax.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_dfm_coincident.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_dfm_coincident.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/variance_components.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/variance_components.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_dates.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_dates.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_filters.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_filters.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ordinal_regression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ordinal_regression.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_arma_0.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mstl_decomposition.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mstl_decomposition.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/exponential_smoothing.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/exponential_smoothing.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_arma_0.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_arma_0.ipynb
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_fixed_params.ipynb
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+
+from importlib import reload
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+
+from pandas_datareader.data import DataReader
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 9
+      6 import statsmodels.api as sm
+      7 import matplotlib.pyplot as plt
+----> 9 from pandas_datareader.data import DataReader
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+
+from importlib import reload
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+
+from pandas_datareader.data import DataReader
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 9
+      6 import statsmodels.api as sm
+      7 import matplotlib.pyplot as plt
+----> 9 from pandas_datareader.data import DataReader
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/treatment_effect.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/treatment_effect.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/markov_regression.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_regression.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stationarity_detrending_adf_kpss.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stationarity_detrending_adf_kpss.ipynb
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_chandrasekhar.ipynb
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+
+from pandas_datareader.data import DataReader
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 8
+      5 import statsmodels.api as sm
+      6 import matplotlib.pyplot as plt
+----> 8 from pandas_datareader.data import DataReader
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+import matplotlib.pyplot as plt
+
+from pandas_datareader.data import DataReader
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 8
+      5 import statsmodels.api as sm
+      6 import matplotlib.pyplot as plt
+----> 8 from pandas_datareader.data import DataReader
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_news.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_news.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/recursive_ls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/recursive_ls.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mediation_survival.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mediation_survival.ipynb
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_structural_harvey_jaeger.ipynb
+An error occurred while executing the following cell:
+------------------
+# Datasets
+from pandas_datareader.data import DataReader
+
+# Get the raw data
+start = '1948-01'
+end = '2008-01'
+us_gnp = DataReader('GNPC96', 'fred', start=start, end=end)
+us_gnp_deflator = DataReader('GNPDEF', 'fred', start=start, end=end)
+us_monetary_base = DataReader('AMBSL', 'fred', start=start, end=end).resample('QS').mean()
+recessions = DataReader('USRECQ', 'fred', start=start, end=end).resample('QS').last().values[:,0]
+
+# Construct the dataframe
+dta = pd.concat(map(np.log, (us_gnp, us_gnp_deflator, us_monetary_base)), axis=1)
+dta.columns = ['US GNP','US Prices','US monetary base']
+dta.index.freq = dta.index.inferred_freq
+dates = dta.index._mpl_repr()
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[2], line 2
+      1 # Datasets
+----> 2 from pandas_datareader.data import DataReader
+      4 # Get the raw data
+      5 start = '1948-01'
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+An error occurred while executing the following cell:
+------------------
+# Datasets
+from pandas_datareader.data import DataReader
+
+# Get the raw data
+start = '1948-01'
+end = '2008-01'
+us_gnp = DataReader('GNPC96', 'fred', start=start, end=end)
+us_gnp_deflator = DataReader('GNPDEF', 'fred', start=start, end=end)
+us_monetary_base = DataReader('AMBSL', 'fred', start=start, end=end).resample('QS').mean()
+recessions = DataReader('USRECQ', 'fred', start=start, end=end).resample('QS').last().values[:,0]
+
+# Construct the dataframe
+dta = pd.concat(map(np.log, (us_gnp, us_gnp_deflator, us_monetary_base)), axis=1)
+dta.columns = ['US GNP','US Prices','US monetary base']
+dta.index.freq = dta.index.inferred_freq
+dates = dta.index._mpl_repr()
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[2], line 2
+      1 # Datasets
+----> 2 from pandas_datareader.data import DataReader
+      4 # Get the raw data
+      5 start = '1948-01'
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ols.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ols.ipynb
 
 ******************************************************************************
 ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_stata.ipynb
@@ -8636,12 +11210,12 @@
     215     ) from e
     217 sys.audit("http.client.connect", self, self.host, self.port)
 
-NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused
+NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused
 
 The above exception was the direct cause of the following exception:
 
 ProxyError                                Traceback (most recent call last)
-ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused'))
+ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused'))
 
 The above exception was the direct cause of the following exception:
 
@@ -8675,7 +11249,7 @@
 --> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
     521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
 
-MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
+MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 During handling of the above exception, another exception occurred:
 
@@ -8727,7 +11301,7 @@
     674     # This branch is for urllib3 v1.22 and later.
     675     raise SSLError(e, request=request)
 
-ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
+ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 An error occurred while executing the following cell:
 ------------------
@@ -8799,12 +11373,12 @@
     215     ) from e
     217 sys.audit("http.client.connect", self, self.host, self.port)
 
-NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused
+NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused
 
 The above exception was the direct cause of the following exception:
 
 ProxyError                                Traceback (most recent call last)
-ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused'))
+ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused'))
 
 The above exception was the direct cause of the following exception:
 
@@ -8838,7 +11412,7 @@
 --> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
     521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
 
-MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
+MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 During handling of the above exception, another exception occurred:
 
@@ -8890,114 +11464,70 @@
     674     # This branch is for urllib3 v1.22 and later.
     675     raise SSLError(e, request=request)
 
-ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f1d00c46cf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
-
-******************************************************************************
-
-
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/gee_nested_simulation.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gee_nested_simulation.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/metaanalysis1.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/metaanalysis1.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ols.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ols.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_seasonal.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_seasonal.ipynb
-
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_fixed_params.ipynb
-An error occurred while executing the following cell:
-------------------
-%matplotlib inline
-
-from importlib import reload
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
-import matplotlib.pyplot as plt
-
-from pandas_datareader.data import DataReader
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 9
-      6 import statsmodels.api as sm
-      7 import matplotlib.pyplot as plt
-----> 9 from pandas_datareader.data import DataReader
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-An error occurred while executing the following cell:
-------------------
-%matplotlib inline
-
-from importlib import reload
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
-import matplotlib.pyplot as plt
-
-from pandas_datareader.data import DataReader
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 9
-      6 import statsmodels.api as sm
-      7 import matplotlib.pyplot as plt
-----> 9 from pandas_datareader.data import DataReader
-
-ModuleNotFoundError: No module named 'pandas_datareader'
+ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/wpi1.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff735f9ecf0>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 ******************************************************************************
 
 
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/treatment_effect.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/treatment_effect.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/lowess.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/lowess.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/recursive_ls.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/recursive_ls.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/plots_boxplots.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/plots_boxplots.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/stl_decomposition.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stl_decomposition.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/generic_mle.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/generic_mle.ipynb
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_cycles.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_varmax.ipynb
 An error occurred while executing the following cell:
 ------------------
-from pandas_datareader.data import DataReader
-endog = DataReader('UNRATE', 'fred', start='1954-01-01')
-endog.index.freq = endog.index.inferred_freq
-------------------
+import requests
+import shutil 
 
+def download_file(url):
+    local_filename = url.split('/')[-1]
+    with requests.get(url, stream=True) as r:
+        with open(local_filename, 'wb') as f:
+            shutil.copyfileobj(r.raw, f)
 
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[3], line 1
-----> 1 from pandas_datareader.data import DataReader
-      2 endog = DataReader('UNRATE', 'fred', start='1954-01-01')
-      3 endog.index.freq = endog.index.inferred_freq
+    return local_filename
 
-ModuleNotFoundError: No module named 'pandas_datareader'
+filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
 
-An error occurred while executing the following cell:
-------------------
-from pandas_datareader.data import DataReader
-endog = DataReader('UNRATE', 'fred', start='1954-01-01')
-endog.index.freq = endog.index.inferred_freq
+dta = pd.read_stata(filename)
+dta.index = dta.qtr
+dta.index.freq = dta.index.inferred_freq
+endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]
 ------------------
 
 
 ---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[3], line 1
-----> 1 from pandas_datareader.data import DataReader
-      2 endog = DataReader('UNRATE', 'fred', start='1954-01-01')
-      3 endog.index.freq = endog.index.inferred_freq
+ConnectionRefusedError                    Traceback (most recent call last)
+File /usr/lib/python3/dist-packages/urllib3/connection.py:198, in HTTPConnection._new_conn(self)
+    197 try:
+--> 198     sock = connection.create_connection(
+    199         (self._dns_host, self.port),
+    200         self.timeout,
+    201         source_address=self.source_address,
+    202         socket_options=self.socket_options,
+    203     )
+    204 except socket.gaierror as e:
 
-ModuleNotFoundError: No module named 'pandas_datareader'
+File /usr/lib/python3/dist-packages/urllib3/util/connection.py:85, in create_connection(address, timeout, source_address, socket_options)
+     84 try:
+---> 85     raise err
+     86 finally:
+     87     # Break explicitly a reference cycle
 
-******************************************************************************
+File /usr/lib/python3/dist-packages/urllib3/util/connection.py:73, in create_connection(address, timeout, source_address, socket_options)
+     72     sock.bind(source_address)
+---> 73 sock.connect(sa)
+     74 # Break explicitly a reference cycle
 
+ConnectionRefusedError: [Errno 111] Connection refused
 
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_pymc3.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_pymc3.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/exponential_smoothing.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/exponential_smoothing.ipynb
-Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/kernel_density.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/kernel_density.ipynb
+The above exception was the direct cause of the following exception:
 
+NewConnectionError                        Traceback (most recent call last)
+File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:773, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
+    772 try:
+--> 773     self._prepare_proxy(conn
 ******************************************************************************
 ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_local_linear_trend.ipynb
 An error occurred while executing the following cell:
@@ -9019,120 +11549,97 @@
 ------------------
 
 
----------------------------------------------------------------------------
-ConnectionRefusedError                    Traceback (most recent call last)
-File /usr/lib/python3.13/urllib/request.py:1319, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
-   1318 try:
--> 1319     h.request(req.get_method(), req.selector, req.data, headers,
-   1320               encode_chunked=req.has_header('Transfer-encoding'))
-   1321 except OSError as err: # timeout error
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/contrasts.ipynb
-An error occurred while executing the following cell:
-------------------
-import pandas as pd
-
-url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
-hsb2 = pd.read_table(url, delimiter=",")
-------------------
-
-
-
-
-File /usr/lib/python3.13/http/client.py:1338---------------------------------------------------------------------------, in 
-HTTPConnection.requestConnectionRefusedError(self, method, url, body, headers, encode_chunked)
-                    Traceback (most recent call last)
-File    1337/usr/lib/python3.13/urllib/request.py:1319 , in AbstractHTTPHandler.do_open"""Send a complete request to the server."""(self, http_class, req, **http_conn_args)
-
--> 1338   1318  tryself:
-.-> 1319     _send_requesth(.methodrequest,( urlreq,. get_methodbody(,) ,headers ,req .encode_chunkedselector),
-
-File  /usr/lib/python3.13/http/client.py:1384req, in .HTTPConnection._send_requestdata(self, method, url, body, headers, encode_chunked),
-   1383      body headers=, _encode(body, 
-'   1320body               ')
-encode_chunked-> 1384= selfreq..endheadershas_header((body',Transfer-encoding 'encode_chunked)=)encode_chunked)
-
-File /usr/lib/python3.13/http/client.py:1333, in HTTPConnection.endheaders(self, message_body, encode_chunked)
-   1332     raise CannotSendHeader()
--> 1333 self.
-_send_output   1321 (exceptmessage_body OSError,  asencode_chunked err: # timeout error=
-
-File encode_chunked/usr/lib/python3.13/http/client.py:1338), in 
-
-File HTTPConnection.request/usr/lib/python3.13/http/client.py:1093, in (self, method, url, body, headers, encode_chunked)
-HTTPConnection._send_output   1337(self, message_body, encode_chunked) 
-   1092"""Send a complete request to the server.""" 
-del-> 1338  selfself.._buffer[:]
--> 1093_send_request (selfmethod.,send (urlmsg,) 
-   1095body ,if  message_body isheaders ,not  Noneencode_chunked:
-)   1096
-
-File  
-/usr/lib/python3.13/http/client.py:1384   1097, in      HTTPConnection._send_request# create a consistent interface to message_body(self, method, url, body, headers, encode_chunked)
+)
+    774--------------------------------------------------------------------------- 
+except (BaseSSLError, ConnectionRefusedErrorOSError, SocketTimeout)                     Traceback (most recent call last)
+File as/usr/lib/python3.13/urllib/request.py:1319 e:
+
+File , in /usr/lib/python3/dist-packages/urllib3/connectionpool.py:1042AbstractHTTPHandler.do_open, in (self, http_class, req, **http_conn_args)HTTPSConnectionPool._prepare_proxy
+(self, conn)   1318
+    1036try conn:
+.-> 1319set_tunnel(
+        1037h     scheme=.tunnel_scheme,
+request   1038(     host=reqself..get_method_tunnel_host,
+(   1039)     port,= selfreq..port,
+   1040selector     headers,= selfreq..proxy_headers,
+data   1041 )
+,-> 1042  connheaders.,
+connect   1320( )              
+
+File encode_chunked/usr/lib/python3/dist-packages/urllib3/connection.py:753=, in reqHTTPSConnection.connect.(self)
+has_header    752( sock: socket.'socket Transfer-encoding| ssl'.)SSLSocket
+--> 753) 
+self   1321. sock except=  sock OSError=  asself err: .# timeout error
+
+File _new_conn(/usr/lib/python3.13/http/client.py:1338, in )HTTPConnection.request
+    754(self, method, url, body, headers, encode_chunked) server_hostname: 
+str   1337  = """Send a complete request to the server."""self
+.-> 1338host
+
+File  /usr/lib/python3/dist-packages/urllib3/connection.py:213self, in .HTTPConnection._new_conn_send_request(self)
+(    212method except,  OSErrorurl as, e:
+ --> 213     bodyraise, NewConnectionError(
+     214headers         self,,  fencode_chunked")Failed to establish a new connection: 
+
+File {/usr/lib/python3.13/http/client.py:1384e, in }HTTPConnection._send_request"
+(self, method, url, body, headers, encode_chunked)    215
+     )    1383from     body  =e _encode(body, 
+'    217 sysbody.'audit()
+"-> 1384http.client.connect self", .selfendheaders, self(.bodyhost, ,self .encode_chunkedport)
 
-File 
-/usr/lib/python3.13/http/client.py:1037   1383, in      body HTTPConnection.send=(self, data) _encode(body, 
-'   1036 bodyif' )
-self-> 1384 .selfauto_open:
--> 1037.     endheadersself(.bodyconnect,() 
-encode_chunked   1038 =elseencode_chunked:
-
-File )/usr/lib/python3.13/http/client.py:1472
-
-File , in /usr/lib/python3.13/http/client.py:1333HTTPSConnection.connect, in (self)HTTPConnection.endheaders
-   1470(self, message_body, encode_chunked) 
-"   1332     Connect to a host on a given (SSL) port.raise" CannotSendHeader()
+NewConnectionError=encode_chunked: <urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused
 
--> 1333-> 1472  selfsuper.(_send_output)(.message_bodyconnect(,) 
-   1474 if self._tunnel_host:
+The above exception was the direct cause of the following exception:
 
-File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
-   1002 sys.audit("http.client.connect", encode_chunkedself=, selfencode_chunked.)host, 
+)ProxyError
 
-File self/usr/lib/python3.13/http/client.py:1093., in port)
-HTTPConnection._send_output-> 1003(self, message_body, encode_chunked) 
-self   1092. sock del=  selfself.._buffer[:]
-_create_connection-> 1093( 
-self   1004.     send((selfmsg.)
-host   1095, selfif message_body .isport )not ,None :
-self   1096 
-.   1097timeout     ,# create a consistent interface to message_body 
-
-File self/usr/lib/python3.13/http/client.py:1037., in HTTPConnection.sendsource_address(self, data))
-
-   1036   1005  if# Might fail in OSs that don't implement TCP_NODELAY 
-
-File self/usr/lib/python3.13/socket.py:864., in auto_open:
-create_connection-> 1037(address, timeout, source_address, all_errors)     
-self    863. ifconnect (not) all_errors:
-
---> 864   1038      raiseelse exceptions[:
-
-File 0/usr/lib/python3.13/http/client.py:1472]
-, in     865 HTTPSConnection.connectraise(self) ExceptionGroup(
-"   1470 create_connection failed""Connect to a host on a given (SSL) port., exceptions)
-
-File "/usr/lib/python3.13/socket.py:849
-, in -> 1472create_connection (address, timeout, source_address, all_errors)super
-(    848     sock)..bind(source_address)
-connect--> 849 (sock).
-   1474connect (if saself)
-.    850_tunnel_host:
+File /usr/lib/python3.13/http/client.py:1333                                Traceback (most recent call last)
+, in ProxyErrorHTTPConnection.endheaders(self, message_body, encode_chunked): ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused'))
 
-File  /usr/lib/python3.13/http/client.py:1003# Break explicitly a reference cycle, in 
+The above exception was the direct cause of the following exception:
 
-HTTPConnection.connectConnectionRefusedError(self)
-: [Errno 111] Connection refused
 
-During handling of the above exception, another exception occurred:
+MaxRetryError   1332                             Traceback (most recent call last)
+File      raise/usr/lib/python3/dist-packages/requests/adapters.py:644 CannotSendHeader()
+, in -> 1333HTTPAdapter.send (self, request, stream, timeout, verify, cert, proxies)self
+.    643_send_output try(:
+message_body--> 644,     resp =  encode_chunkedconn=.encode_chunkedurlopen)
+
+File (
+/usr/lib/python3.13/http/client.py:1093    645, in  HTTPConnection._send_output        (self, message_body, encode_chunked)method
+=   1092 requestdel. methodself,.
+_buffer[:]
+    646-> 1093          selfurl.=sendurl(,msg
+    647) 
+           1095body if= message_body requestis. bodynot, 
+None    648:
+    1096         
+headers   1097=     request# create a consistent interface to message_body.
+
+File headers/usr/lib/python3.13/http/client.py:1037, in ,
+HTTPConnection.send    649(self, data) 
+           1036 redirectif= Falseself,.
+auto_open:
+    650-> 1037              selfassert_same_host.=connectFalse(,)
+
+    651   1038          elsepreload_content:
+
+File =/usr/lib/python3.13/http/client.py:1472, in FalseHTTPSConnection.connect,(self)
+
+    652   1470          "decode_contentConnect to a host on a given (SSL) port.="False
+,-> 1472 
+super    653 (        )retries.=connectself(.)max_retries
+,   1474
+     654if          selftimeout.=_tunnel_host:
+
+File timeout/usr/lib/python3.13/http/client.py:1003,, in 
+HTTPConnection.connect    655(self) 
+           1002chunked sys.=audit(chunked",
+http.client.connect    656" ,     self), 
+self    658. excepthost, self (ProtocolError, .OSErrorport)
+) -> 1003as  err:
 
-URLError                                  Traceback (most recent call last)
-Cell In[3], line 7
-      4 import requests
-      6 # Download the dataset
-----> 7   1002 df = sys .pdaudit(."read_tablehttp.client.connect(
-"      8,  self    , self".host, self.port)
--> 1003 self.sock = self._create_connection(
+File self/usr/lib/python3/dist-packages/urllib3/connectionpool.py:841., in sock HTTPConnectionPool.urlopen= self(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)._create_connection(
    1004     (self.host,self.port), self.timeout, self.source_address)
    1005 # Might fail in OSs that don't implement TCP_NODELAY
 
@@ -9151,10 +11658,17 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[2], line 4
-      1 import pandas as pd
-      3 url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
-----> 4 hsb2 = pd.read_table(url, delimiter=",")
+Cell In[3], line 7
+      4 import requests
+      6 # Download the dataset
+----> 7 df = pd.read_table(
+      8     "https://raw.githubusercontent.com/statsmodels/smdatasets/refs/heads/main/data/statespace-local-linear-trend/NorwayFinland.txt",
+      9     skiprows=1,
+     10     header=None,
+     11     sep=r"\s+",
+     12     engine="python",
+     13     names=["date", "nf", "ff"],
+     14 )
 
 File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405, in read_table(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    1392 kwds_defaults = _refine_defaults_read(
@@ -9187,55 +11701,9 @@
    1883     encoding=self.options.get("encoding", None),
    1884     compression=self.options.get("compression", None),
    1885     memory_map=self.options.get("memory_map", False),
-   1886     is_text=is_texthttps://raw.githubusercontent.com/statsmodels/smdatasets/refs/heads/main/data/statespace-local-linear-trend/NorwayFinland.txt",
-,
-   1887     errors=self.options.get("encoding_errors", "strict      9 "    )skiprows,=
-1   1888, 
-         10 storage_options    =header=selfNone.,options
-     11.     getsep(="rstorage_options""\s+," ,None
-     12) ,    
-engine   1889= "python)
-"   1890, 
-     13assert      selfnames.=handles [is" datenot" ,None 
-"   1891nf f ", "ff"],
-     14 )
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405, in read_table=(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
-   1392 kwds_defaults = _refine_defaults_read(
-   1393     dialect,
-   1394     delimiter,
-   (...)
-   1401     dtype_backend=dtype_backend,
-   1402 )
-   1403 kwds.update(kwds_defaults)
--> 1405 return _read(filepath_or_buffer, kwds)
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
-    617 _validate_names(kwds.get("names", None))
-    619 # Create the parser.
---> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
-    622 if chunksize or iterator:
-    623     return parser
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
-   1617     self.options["has_index_names"] = kwds["has_index_names"]
-   1619 self.handles: IOHandles | None = None
--> 1620 self._engine = self._make_engine(f, self.engine)
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
-   1878     if "b" not in mode:
-   1879         mode += "b"
--> 1880 self.handles = get_handle(
-   1881     f,
-   1882     mode,
-   1883     encoding=self.options.get("encoding", None),
-   1884     compression=self.options.get("compression", None),
-   1885 self     memory_map=self.options.get("memory_map", False),
    1886     is_text=is_text,
    1887     errors=self.options.get("encoding_errors", "strict"),
-   1888     storage_options=self.options..get("storage_options", None)handles.handle
-
-File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options),
+   1888     storage_options=self.options.get("storage_options", None),
    1889 )
    1890 assert self.handles is not None
    1891 f = self.handles.handle
@@ -9245,13 +11713,6 @@
     727 # open URLs
 --> 728 ioargs = _get_filepath_or_buffer(
     729     path_or_buf,
-
-    725    730      codecs    .encodinglookup_error(errors)
-    727= encoding# open URLs
-,--> 728
- ioargs     731=      compression_get_filepath_or_buffer=(
-compression,
-    732     mode=mode,    729     path_or_buf,
     730     encoding=encoding,
     731     compression=compression,
     732     mode=mode,
@@ -9265,21 +11726,6 @@
     383 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
 --> 384 with urlopen(req_info) as req:
     385     content_encoding = req.headers.get("Content-Encoding", None)
-
-    733     storage_options=storage_options,
-    734 )
-    736 handle = ioargs.filepath_or_buffer
-    737 handles: list[BaseBuffer]
-
-File /usr/lib/python3/dist-packages/pandas/io/common.py:384, in _get_filepath_or_buffer    386(filepath_or_buffer, encoding, compression, mode, storage_options)     
-if    382 content_encoding  ==# assuming storage_options is to be interpreted as headers 
-"    383 req_info gzip=" urllib:
-.request    387.         Request(filepath_or_buffer, headers# Override compression based on Content-Encoding header=storage_options)
-
-
-File --> 384/usr/lib/python3/dist-packages/pandas/io/common.py:289 , in with urlopenurlopen(*args, **kwargs)(
-req_info    283) as req:
-    385     content_encoding = req.headers.get("Content-Encoding", None)
     386     if content_encoding == "gzip":
     387         # Override compression based on Content-Encoding header
 
@@ -9300,7 +11746,360 @@
     486     req = meth(req)
     488 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
 --> 489 response = self._open(req, data)
-    491  """
+    491 # post-process response
+    492 meth_name = protocol+"_response"
+
+File /usr/lib/python3.13/urllib/request.py:506, in OpenerDirector._open(self, req, data)
+    503     return result
+    505 protocol = req.type
+--> 506 result = self._call_chain(self.handle_open,
+ protocol,    839      new_e protocol=  ProtocolError("+
+Connection aborted.    507" , new_e)
+--> 841                           retries '=_open retries'.,increment (req
+    842) 
+        508method ,if  result:
+    509url     ,return  result
+
+File error/usr/lib/python3.13/urllib/request.py:466=, in new_eOpenerDirector._call_chain,(self, chain, kind, meth_name, *args) 
+_pool    464= forself handler ,in  handlers:
+_stacktrace    465=     func sys= .getattrexc_info(handler, meth_name)
+(--> 466)     result [=2 func]
+(    843* args))
+
+    844    467 retries.     sleep()
+
+File if result /usr/lib/python3/dist-packages/urllib3/util/retry.py:519is, in  Retry.incrementnot(self, method, url, response, error, _pool, _stacktrace) 
+None    518:
+     reason     468=          error returnor result
+
+File  ResponseError(cause)
+/usr/lib/python3.13/urllib/request.py:1367--> 519, in      raiseHTTPSHandler.https_open(self, req) MaxRetryError(_pool, url, reason) 
+from   1366  reasondef   # type: ignore[arg-type]https_open
+(    521self log, req):
+.-> 1367debug(     "returnIncremented Retry for (url= self'.%sdo_open'(): http%r"., url, new_retry)
+
+clientMaxRetryError.HTTPSConnection,: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+
+During handling of the above exception, another exception occurred:
+
+ ProxyErrorreq                                Traceback (most recent call last)
+Cell ,In[3], line 12
+
+   1368      8              shutil.                        copyfileobj(rcontext.raw, f)
+=     10     selfreturn. local_filename
+---> 12_context filename )=
+
+File  /usr/lib/python3.13/urllib/request.py:1322download_file, in (AbstractHTTPHandler.do_open"(self, http_class, req, **http_conn_args)https://www.stata-press.com/data/r12/lutkepohl2.dta
+   1319"         h).
+     14request(req dta .=get_method(), req pd..selector, reqread_stata(filename)
+.     15data, headers,
+ dta.   1320index                    encode_chunked== dtareq..qtr
+
+Cell has_header(In[3], line 6, in 'download_fileTransfer-encoding(url)'
+))
+      4    1321def      exceptdownload_file (url):
+OSError      5      local_filename as= err:  url# timeout error.
+split(-> 1322'         /raise' URLError(err)
+)[   1323-     r =1 h]
+----> 6.     getresponse()
+with   1324  requestsexcept.:
+
+getURLError(url, stream: <urlopen error [Errno 111] Connection refused>
+
+An error occurred while executing the following cell:
+------------------
+from io import BytesIO
+from zipfile import ZipFile
+
+import requests
+
+# Download the dataset
+df = pd.read_table(
+    "https://raw.githubusercontent.com/statsmodels/smdatasets/refs/heads/main/data/statespace-local-linear-trend/NorwayFinland.txt",
+    skiprows=1,
+    header=None,
+    sep=r"\s+",
+    engine="python",
+    names=["date", "nf", "ff"],
+)
+------------------
+
+
+=---------------------------------------------------------------------------True
+) ConnectionRefusedErroras                    Traceback (most recent call last)
+File  r:
+      7/usr/lib/python3.13/urllib/request.py:1319         , in withAbstractHTTPHandler.do_open open(self, http_class, req, **http_conn_args)(local_filename, 
+'   1318wb try':
+) -> 1319as f:
+           8h             shutil..copyfileobj(rrequest.(raw, f)
+
+File req/usr/lib/python3/dist-packages/requests/api.py:73., in getget_method(url, params, **kwargs)(
+     62) ,def  getreq(url, params.=selectorNone, ,* *reqkwargs):
+     63. data    ,r """Sends a GET request.
+headers     64, 
+
+     65   1320      :param url: URL for the new :class:`Request` object.              
+   (...)encode_chunked
+=     70req     :rtype: requests.Response.
+has_header     71 (    """'
+---> 73Transfer-encoding     'return )request)(
+   1321" getexcept" ,OSError  asurl err: ,# timeout error 
+
+File params/usr/lib/python3.13/http/client.py:1338=, in paramsHTTPConnection.request,(self, method, url, body, headers, encode_chunked) 
+*   1337* kwargs"""Send a complete request to the server.""")
+
+
+File -> 1338/usr/lib/python3/dist-packages/requests/api.py:59 , in selfrequest.(method, url, **kwargs)
+_send_request     55( method# By using the 'with' statement we are sure the session is closed, thus we,
+     56  url# avoid leaving sockets open which can trigger a ResourceWarning in some,
+      57 body# cases, and look like a memory leak in others.,
+      58 headerswith, sessions. Session() encode_chunkedas session:
+)---> 59
+
+File      return/usr/lib/python3.13/http/client.py:1384 , in sessionHTTPConnection._send_request.(self, method, url, body, headers, encode_chunked)request
+(   1383     body method== _encode(body, method',body 'url)
+=-> 1384url self, .*endheaders*(kwargsbody),
+
+File  /usr/lib/python3/dist-packages/requests/sessions.py:589, in encode_chunkedSession.request=encode_chunked(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json))
+
+
+File     584/usr/lib/python3.13/http/client.py:1333 send_kwargs , in =HTTPConnection.endheaders {
+    585(self, message_body, encode_chunked)     
+"   1332timeout     raise": timeout,
+ CannotSendHeader()
+    586-> 1333      "selfallow_redirects."_send_output: allow_redirects,
+(    587 }
+message_body    588, send_kwargs. update(settings)
+encode_chunked--> 589 resp ==encode_chunked self).
+
+File send/usr/lib/python3.13/http/client.py:1093(, in prepHTTPConnection._send_output,(self, message_body, encode_chunked) 
+*   1092 *delsend_kwargs )self
+.    591 _buffer[:]
+return-> 1093 resp
+
+File  self/usr/lib/python3/dist-packages/requests/sessions.py:703, in .Session.sendsend(self, request, **kwargs)(
+    700msg start )=
+ preferred_clock()
+   1095    702  if# Send the request
+ message_body --> 703is r  =not  adapterNone.:
+send   1096 
+(   1097request     ,# create a consistent interface to message_body 
+
+File */usr/lib/python3.13/http/client.py:1037*, in kwargsHTTPConnection.send)(self, data)
+   1036
+     705 if # Total elapsed time of the request (approximately)self
+    706. elapsed auto_open:
+=-> 1037 preferred_clock()      -self start
+
+File ./usr/lib/python3/dist-packages/requests/adapters.py:671, in connectHTTPAdapter.send((self, request, stream, timeout, verify, cert, proxies))
+
+    668   1038      raiseelse RetryError(e, request=:
+
+File request)
+/usr/lib/python3.13/http/client.py:1472    670, in  HTTPSConnection.connectif (self)isinstance
+(e   1470. reason, _ProxyError):
+"--> 671     Connect to a host on a given (SSL) port.raise" ProxyError(e, request
+=-> 1472request)
+     673super if( isinstance)(e..reason, _SSLError):
+connect    674(     )# This branch is for urllib3 v1.22 and later.
+
+   1474    675      raiseif SSLError(e, request =selfrequest)
+
+.ProxyError_tunnel_host:
+
+File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
+   1002: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+
+An error occurred while executing the following cell:
+------------------
+import requests
+import shutil 
+
+def download_file(url):
+    local_filename = url.split('/')[-1]
+    with requests.get(url, stream=True) as r:
+        with open(local_filename, 'wb') as f:
+            shutil.copyfileobj(r.raw, f)
+
+    return local_filename
+
+filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
+
+dta = pd.read_stata(filename)
+dta.index = dta.qtr
+dta.index.freq = dta.index.inferred_freq
+endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]
+------------------
+
+
+ sys.audit(---------------------------------------------------------------------------"
+http.client.connectConnectionRefusedError"                    Traceback (most recent call last)
+File , /usr/lib/python3/dist-packages/urllib3/connection.py:198self, in , HTTPConnection._new_connself(self)
+.    197host,  selftry.:
+--> 198port)
+     sock -> 1003=  selfconnection..sock create_connection=( 
+self    199.         _create_connection((self
+.   1004 _dns_host    ,( selfself..porthost),,self
+    200. port        )self,. timeout,self
+.    201 timeout        ,source_address =selfself..source_addresssource_address,)
+
+    202    1005         socket_options# Might fail in OSs that don't implement TCP_NODELAY=
+
+File self/usr/lib/python3.13/socket.py:864., in create_connectionsocket_options,(address, timeout, source_address, all_errors)
+
+    203    863      if) 
+not    204  all_errors:
+except--> 864 socket     .raisegaierror  exceptions[as0 e:
+
+File ]
+/usr/lib/python3/dist-packages/urllib3/util/connection.py:85    865, in  create_connectionraise(address, timeout, source_address, socket_options) ExceptionGroup(
+"     84 create_connection failedtry":
+---> 85, exceptions)
+
+File      /usr/lib/python3.13/socket.py:849raise, in  err
+create_connection     86 (address, timeout, source_address, all_errors)finally
+:
+    848     87          sock.# Break explicitly a reference cyclebind(source_address)
+
+
+File --> 849/usr/lib/python3/dist-packages/urllib3/util/connection.py:73 , in sockcreate_connection.(address, timeout, source_address, socket_options)
+connect     72(     sock.sabind(source_address)
+)---> 73
+     850sock .# Break explicitly a reference cycleconnect
+
+(ConnectionRefusedErrorsa: [Errno 111] Connection refused
+
+During handling of the above exception, another exception occurred:
+
+)
+URLError     74                                   Traceback (most recent call last)
+Cell In[3], line 7# Break explicitly a reference cycle
+
+
+ConnectionRefusedError      4 : [Errno 111] Connection refused
+
+The above exception was the direct cause of the following exception:
+
+importNewConnectionError requests                        Traceback (most recent call last)
+File 
+/usr/lib/python3/dist-packages/urllib3/connectionpool.py:773      6, in  HTTPConnectionPool.urlopen# Download the dataset
+(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)----> 7
+ df     772=  trypd:
+.--> 773     read_tableself(.
+_prepare_proxy      8 (    conn")
+    774https://raw.githubusercontent.com/statsmodels/smdatasets/refs/heads/main/data/statespace-local-linear-trend/NorwayFinland.txt "except (BaseSSLError, ,OSError
+, SocketTimeout)       9as  e:
+
+File     /usr/lib/python3/dist-packages/urllib3/connectionpool.py:1042skiprows, in =HTTPSConnectionPool._prepare_proxy(self, conn)1
+,   1036
+ conn     10. set_tunnel(
+       1037     schemeheader==tunnel_scheme,
+   1038None     host,=
+self     11. _tunnel_host,
+       1039sep     port==selfr."port,
+   1040\     headerss+="self.,proxy_headers,
+
+   1041     12 )
+ -> 1042     engineconn.=connect"(python)"
+
+File ,/usr/lib/python3/dist-packages/urllib3/connection.py:753
+, in      13HTTPSConnection.connect (self)
+        752names sock: socket=.socket [|" ssl.dateSSLSocket
+"--> 753 ,self ."sock =nf sock "= ,self ."_new_connff()"
+]    754 server_hostname: ,str
+      14=  )self
+
+File ./usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405host
+
+File , in /usr/lib/python3/dist-packages/urllib3/connection.py:213read_table, in HTTPConnection._new_conn(self)
+    212 except(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend) 
+OSError   1392 as kwds_defaults  e:
+=--> 213 _refine_defaults_read(
+        1393raise     dialect,
+ NewConnectionError(
+   1394    214              delimiter,
+self   (...), 
+f   1401"     dtype_backend=Failed to establish a new connection: dtype_backend,
+{   1402e )
+}   1403" kwds
+.    215update(kwds_defaults)
+     ) -> 1405from  ereturn
+     217_read sys.(audit(filepath_or_buffer",http.client.connect ", kwdsself), 
+
+File self./usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620host, , in self_read.(filepath_or_buffer, kwds)port)
+
+
+NewConnectionError    617 _validate_names(kwds: <urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused
+
+The above exception was the direct cause of the following exception:
+
+.ProxyErrorget("                                Traceback (most recent call last)
+namesProxyError", : ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused'))
+
+The above exception was the direct cause of the following exception:
+
+NoneMaxRetryError))
+    619                             Traceback (most recent call last)
+File  /usr/lib/python3/dist-packages/requests/adapters.py:644# Create the parser., in 
+HTTPAdapter.send--> 620(self, request, stream, timeout, verify, cert, proxies) parser 
+=    643  TextFileReadertry:
+(--> 644filepath_or_buffer     resp =,  conn*.urlopen*(kwds
+)    645
+             622 methodif= chunksize requestor. iterator:
+    623method     ,return
+    646 parser
+
+File  /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620        , in urlTextFileReader.__init__=(self, f, engine, **kwds)url
+,   1617
+         647self         .options[body"=has_index_namesrequest."] body=, kwds[
+"    648 has_index_names        "headers]
+=   1619request self.headers.,handles: IOHandles 
+|    649  None         redirect= =NoneFalse
+,-> 1620
+     650self .        _engine assert_same_host== selfFalse,.
+_make_engine    651 (        fpreload_content,= Falseself,.
+    652engine )        
+
+File decode_content/usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880=, in FalseTextFileReader._make_engine,(self, f, engine)
+
+    653   1878              if "b" not in mode:
+   1879         mode += "b"
+-> 1880 self.handles = get_handle(
+   1881     f,
+   1882     mode,
+   1883     encoding=self.options.get("encoding", None),
+   1884     compression=self.options.get("compression", None),
+   1885     memory_map=self.options.get("memory_map", False),
+   1886     is_text=is_text,
+   1887     errors=self.options.get("encoding_errors", "strict"),
+   1888     storage_options=self.options.get("storage_options", None),
+   1889 )
+   1890 assert self.handles is not None
+   1891 f = self.handles.handle
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
+    725     codecs.lookup_error(errors)
+    727 # open URLs
+--> 728 ioargs = _get_filepath_or_buffer(
+    729     path_or_buf,
+    730     encoding=encoding,
+    731     compression=compression,
+    732     mode=mode,
+    733     storage_options=storage_options,
+    734 )
+    736 handle = ioargs.filepath_or_buffer
+    737 handles: list[BaseBuffer]
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:384, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
+    382 # assuming storage_options is to be interpreted as headers
+    383 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
+--> 384 with urlopen(req_info) as req:
+    385     content_encoding = req.headers.get("Content-Encoding", None)
+    386     if content_encoding == "gzip":
+    387         # Override compression based on Content-Encoding header
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:289, in urlopen(*args, **kwargs)
+    283 """
     284 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
     285 the stdlib.
     286 """
@@ -9349,14 +12148,123 @@
 
 URLError: <urlopen error [Errno 111] Connection refused>
 
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/deterministics.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/deterministics.ipynbretries
+=self.max_retries,
+    654         timeout=timeout,
+    655         chunked=chunked,
+    656     )
+    658 except (ProtocolError, OSError) as err:
+
+File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:841, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
+    839     new_e = ProtocolError("Connection aborted.", new_e)
+--> 841 retries = retries.increment(
+    842     method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
+    843 )
+    844 retries.sleep()
+
+File /usr/lib/python3/dist-packages/urllib3/util/retry.py:519, in Retry.increment(self, method, url, response, error, _pool, _stacktrace)
+    518     reason = error or ResponseError(cause)
+--> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
+    521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
+
+MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+
+During handling of the above exception, another exception occurred:
+
+ProxyError                                Traceback (most recent call last)
+Cell In[3], line 12
+      8             shutil.copyfileobj(r.raw, f)
+     10     return local_filename
+---> 12 filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
+     14 dta = pd.read_stata(filename)
+     15 dta.index = dta.qtr
+
+Cell In[3], line 6, in download_file(url)
+      4 def download_file(url):
+      5     local_filename = url.split('/')[-1]
+----> 6     with requests.get(url, stream=True) as r:
+      7         with open(local_filename, 'wb') as f:
+      8             shutil.copyfileobj(r.raw, f)
+
+File /usr/lib/python3/dist-packages/requests/api.py:73, in get(url, params, **kwargs)
+     62 def get(url, params=None, **kwargs):
+     63     r"""Sends a GET request.
+     64 
+     65     :param url: URL for the new :class:`Request` object.
+   (...)
+     70     :rtype: requests.Response
+     71     """
+---> 73     return request("get", url, params=params, **kwargs)
+
+File /usr/lib/python3/dist-packages/requests/api.py:59, in request(method, url, **kwargs)
+     55 # By using the 'with' statement we are sure the session is closed, thus we
+     56 # avoid leaving sockets open which can trigger a ResourceWarning in some
+     57 # cases, and look like a memory leak in others.
+     58 with sessions.Session() as session:
+---> 59     return session.request(method=method, url=url, **kwargs)
+
+File /usr/lib/python3/dist-packages/requests/sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
+    584 send_kwargs = {
+    585     "timeout": timeout,
+    586     "allow_redirects": allow_redirects,
+    587 }
+    588 send_kwargs.update(settings)
+--> 589 resp = self.send(prep, **send_kwargs)
+    591 return resp
+
+File /usr/lib/python3/dist-packages/requests/sessions.py:703, in Session.send(self, request, **kwargs)
+    700 start = preferred_clock()
+    702 # Send the request
+--> 703 r = adapter.send(request, **kwargs)
+    705 # Total elapsed time of the request (approximately)
+    706 elapsed = preferred_clock() - start
+
+File /usr/lib/python3/dist-packages/requests/adapters.py:671, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
+    668     raise RetryError(e, request=request)
+    670 if isinstance(e.reason, _ProxyError):
+--> 671     raise ProxyError(e, request=request)
+    673 if isinstance(e.reason, _SSLError):
+    674     # This branch is for urllib3 v1.22 and later.
+    675     raise SSLError(e, request=request)
+
+ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fae45f69a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/quasibinomial.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quasibinomial.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/metaanalysis1.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/metaanalysis1.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_dfm_coincident.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_dfm_coincident.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_dates.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_dates.ipynb
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_internet.ipynb
 An error occurred while executing the following cell:
 ------------------
-import pandas as pd
+import requests
+from io import BytesIO
+from zipfile import ZipFile
 
-url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
-hsb2 = pd.read_table(url, delimiter=",")
+# Download the dataset
+df = pd.read_table(
+    "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
+    skiprows=1, header=None, sep='\s+', engine='python',
+    names=['internet','dinternet']
+)
 ------------------
 
+----- stderr -----
+<>:8: SyntaxWarning: invalid escape sequence '\s'
+<>:8: SyntaxWarning: invalid escape sequence '\s'
+/tmp/ipykernel_522880/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
+  skiprows=1, header=None, sep='\s+', engine='python',
+----- stderr -----
+/tmp/ipykernel_522880/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
+  skiprows=1, header=None, sep='\s+', engine='python',
+------------------
 
 ---------------------------------------------------------------------------
 ConnectionRefusedError                    Traceback (most recent call last)
@@ -9392,7 +12300,175 @@
 
 File /usr/lib/python3.13/http/client.py:1472, in HTTPSConnection.connect(self)
    1470 "Connect to a host on a given (SSL) port."
--> 1472 # post-process response
+-> 1472 super().connect()
+   1474 if self._tunnel_host:
+
+File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
+   1002 sys.audit("http.client.connect", self, self.host, self.port)
+-> 1003 self.sock = self._create_connection(
+   1004     (self.host,self.port), self.timeout, self.source_address)
+   1005 # Might fail in OSs that don't implement TCP_NODELAY
+
+File /usr/lib/python3.13/socket.py:864, in create_connection(address, timeout, source_address, all_errors)
+    863 if not all_errors:
+--> 864     raise exceptions[0]
+    865 raise ExceptionGroup("create_connection failed", exceptions)
+
+File /usr/lib/python3.13/socket.py:849, in create_connection(address, timeout, source_address, all_errors)
+    848     sock.bind(source_address)
+--> 849 sock.connect(sa)
+    850 # Break explicitly a reference cycle
+
+ConnectionRefusedError: [Errno 111] Connection refused
+
+During handling of the above exception, another exception occurred:
+
+URLError                                  Traceback (most recent call last)
+Cell In[3], line 6
+      3 from zipfile import ZipFile
+      5 # Download the dataset
+----> 6 df = pd.read_table(
+      7     "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
+      8     skiprows=1, header=None, sep='\s+', engine='python',
+      9     names=['internet','dinternet']
+     10 )
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405, in read_table(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
+   1392 kwds_defaults = _refine_defaults_read(
+   1393     dialect,
+   1394     delimiter,
+   (...)
+   1401     dtype_backend=dtype_backend,
+   1402 )
+   1403 kwds.update(kwds_defaults)
+-> 1405 return _read(filepath_or_buffer, kwds)
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
+    617 _validate_names(kwds.get("names", None))
+    619 # Create the parser.
+--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
+    622 if chunksize or iterator:
+    623     return parser
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
+   1617     self.options["has_index_names"] = kwds["has_index_names"]
+   1619 self.handles: IOHandles | None = None
+-> 1620 self._engine = self._make_engine(f, self.engine)
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
+   1878     if "b" not in mode:
+   1879         mode += "b"
+-> 1880 self.handles = get_handle(
+   1881     f,
+   1882     mode,
+   1883     encoding=self.options.get("encoding", None),
+   1884     compression=self.options.get("compression", None),
+   1885     memory_map=self.options.get("memory_map", False),
+   1886     is_text=is_text,
+   1887     errors=self.options.get("encoding_errors", "strict"),
+   1888     storage_options=self.options.get("storage_options", None),
+   1889 )
+   1890 assert self.handles is not None
+   1891 f = self.handles.handle
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
+    725     codecs.lookup_error(errors)
+    727 # open URLs
+--> 728 ioargs = _get_filepath_or_buffer(
+    729     path_or_buf,
+    730     encoding=encoding,
+    731     compression=compression,
+    732     mode=mode,
+    733     storage_options=storage_options,
+    734 )
+    736 handle = ioargs.filepath_or_buffer
+    737 handles: list[BaseBuffer]
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:384, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
+    382 # assuming storage_options is to be interpreted as headers
+    383 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
+--> 384 with urlopen(req_info) as req:
+    385     content_encoding = req.headers.get("Content-Encoding", None)
+    386     if content_encoding == "gzip":
+    387         # Override compression based on Content-Encoding header
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:289, in urlopen(*args, **kwargs)
+    283 """
+    284 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
+    285 the stdlib.
+    286 """
+    287 import urllib.request
+--> 289 return urllib.request.urlopen(*args, **kwargs)
+
+File /usr/lib/python3.13/urllib/request.py:189, in urlopen(url, data, timeout, context)
+    187 else:
+    188     opener = _opener
+--> 189 return opener.open(url, data, timeout)
+
+File /usr/lib/python3.13/urllib/request.py:489, in OpenerDirector.open(self, fullurl, data, timeout)
+    486     req = meth(req)
+    488 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
+--> 489 response = 
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/theta-model.ipynb
+An error occurred while executing the following cell:
+------------------
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pandas_datareader as pdr
+import seaborn as sns
+
+plt.rc("figure", figsize=(16, 8))
+plt.rc("font", size=15)
+plt.rc("lines", linewidth=3)
+sns.set_style("darkgrid")
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 4
+      2 import numpy as np
+      3 import pandas as pd
+----> 4 import pandas_datareader as pdr
+      5 import seaborn as sns
+      7 plt.rc("figure", figsize=(16, 8))
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+An error occurred while executing the following cell:
+------------------
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pandas_datareader as pdr
+import seaborn as sns
+
+plt.rc("figure", figsize=(16, 8))
+plt.rc("font", size=15)
+plt.rc("lines", linewidth=3)
+sns.set_style("darkgrid")
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 4
+      2 import numpy as np
+      3 import pandas as pd
+----> 4 import pandas_datareader as pdr
+      5 import seaborn as sns
+      7 plt.rc("figure", figsize=(16, 8))
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+******************************************************************************
+
+
+self._open(req, data)
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_cycles.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_cycles.ipynb
+    491 # post-process response
     492 meth_name = protocol+"_response"
 
 File /usr/lib/python3.13/urllib/request.py:506, in OpenerDirector._open(self, req, data)
@@ -9427,22 +12503,27 @@
 
 An error occurred while executing the following cell:
 ------------------
+import requests
 from io import BytesIO
 from zipfile import ZipFile
 
-import requests
-
 # Download the dataset
 df = pd.read_table(
-    "https://raw.githubusercontent.com/statsmodels/smdatasets/refs/heads/main/data/statespace-local-linear-trend/NorwayFinland.txt",
-    skiprows=1,
-    header=None,
-    sep=r"\s+",
-    engine="python",
-    names=["date", "nf", "ff"],
+    "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
+    skiprows=1, header=None, sep='\s+', engine='python',
+    names=['internet','dinternet']
 )
 ------------------
 
+----- stderr -----
+<>:8: SyntaxWarning: invalid escape sequence '\s'
+<>:8: SyntaxWarning: invalid escape sequence '\s'
+/tmp/ipykernel_522880/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
+  skiprows=1, header=None, sep='\s+', engine='python',
+----- stderr -----
+/tmp/ipykernel_522880/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
+  skiprows=1, header=None, sep='\s+', engine='python',
+------------------
 
 ---------------------------------------------------------------------------
 ConnectionRefusedError                    Traceback (most recent call last)
@@ -9484,13 +12565,7 @@
 File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
    1002 sys.audit("http.client.connect", self, self.host, self.port)
 -> 1003 self.sock = self._create_connection(
-   1004 super().connect()
-   1474 if self._tunnel_host:
-
-File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
-   1002 sys.audit("http.client.connect", self, self.host, self.port)
--> 1003 self.sock = self._create_connection(
-    (self.host,self.port), self.timeout, self.source_address)
+   1004     (self.host,self.port), self.timeout, self.source_address)
    1005 # Might fail in OSs that don't implement TCP_NODELAY
 
 File /usr/lib/python3.13/socket.py:864, in create_connection(address, timeout, source_address, all_errors)
@@ -9508,17 +12583,14 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[3], line 7
-      4 import requests
-      6 # Download the dataset
-----> 7 df = pd.read_table(
-      8     "https://raw.githubusercontent.com/statsmodels/smdatasets/refs/heads/main/data/statespace-local-linear-trend/NorwayFinland.txt",
-      9     skiprows=1,
-     10     header=None,
-     11     sep=r"\s+",
-     12     engine="python",
-     13     names=["date", "nf", "ff"],
-     14 )
+Cell In[3], line 6
+      3 from zipfile import ZipFile
+      5 # Download the dataset
+----> 6 df = pd.read_table(
+      7     "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
+      8     skiprows=1, header=None, sep='\s+', engine='python',
+      9     names=['internet','dinternet']
+     10 )
 
 File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405, in read_table(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    1392 kwds_defaults = _refine_defaults_read(
@@ -9554,7 +12626,7 @@
    1886     is_text=is_text,
    1887     errors=self.options.get("encoding_errors", "strict"),
    1888     storage_options=self.options.get("storage_options", None),
-   1889   1004     (self.host,self.port), self.timeout, self )
+   1889 )
    1890 assert self.handles is not None
    1891 f = self.handles.handle
 
@@ -9594,14 +12666,8 @@
 
 File /usr/lib/python3.13/urllib/request.py:489, in OpenerDirector.open(self, fullurl, data, timeout)
     486     req = meth(req)
-    488 sys.audit('.urllib.Requestsource_address)'
-, req   1005. full_url, req# Might fail in OSs that don't implement TCP_NODELAY
-
-File ./usr/lib/python3.13/socket.py:864data, req, in .create_connectionheaders, req(address, timeout, source_address, all_errors).
-    863get_method())
- --> 489if response  =not  all_errors:
---> 864self     raise. exceptions[0_open]
-    865( raisereq, ExceptionGroup( data)
+    488 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
+--> 489 response = self._open(req, data)
     491 # post-process response
     492 meth_name = protocol+"_response"
 
@@ -9616,7 +12682,166 @@
 File /usr/lib/python3.13/urllib/request.py:466, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
     464 for handler in handlers:
     465     func = getattr(handler, meth_name)
---> 466     result = "create_connection failed", exceptions)
+--> 466     result = func(*args)
+    467     if result is not None:
+    468         return result
+
+File /usr/lib/python3.13/urllib/request.py:1367, in HTTPSHandler.https_open(self, req)
+   1366 def https_open(self, req):
+-> 1367     return self.do_open(http.client.HTTPSConnection, req,
+   1368                         context=self._context)
+
+File /usr/lib/python3.13/urllib/request.py:1322, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
+   1319         h.request(req.get_method(), req.selector, req.data, headers,
+   1320                   encode_chunked=req.has_header('Transfer-encoding'))
+   1321     except OSError as err: # timeout error
+-> 1322         raise URLError(err)
+   1323     r = h.getresponse()
+   1324 except:
+
+URLError: <urlopen error [Errno 111] Connection refused>
+
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/contrasts.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/contrasts.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/glm.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/glm_formula.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_formula.ipynb
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/markov_autoregression.ipynb
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+
+from datetime import datetime
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import requests
+import statsmodels.api as sm
+
+# NBER recessions
+from pandas_datareader.data import DataReader
+
+usrec = DataReader(
+    "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
+)
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 13
+     10 import statsmodels.api as sm
+     12 # NBER recessions
+---> 13 from pandas_datareader.data import DataReader
+     15 usrec = DataReader(
+     16     "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
+     17 )
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+
+from datetime import datetime
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import requests
+import statsmodels.api as sm
+
+# NBER recessions
+from pandas_datareader.data import DataReader
+
+usrec = DataReader(
+    "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
+)
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 13
+     10 import statsmodels.api as sm
+     12 # NBER recessions
+---> 13 from pandas_datareader.data import DataReader
+     15 usrec = DataReader(
+     16     "USREC", "fred", start=datetime(1947, 1, 1), end=datetime(2013, 4, 1)
+     17 )
+
+ModuleNotFoundError: No module named 'pandas_datareader'
+
+******************************************************************************
+
+
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/interactions_anova.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/interactions_anova.ipynb
+Executing /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/tsa_arma_0.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_arma_0.ipynb
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/linear_regression_diagnostics_plots.ipynb
+An error occurred while executing the following cell:
+------------------
+# Load data
+data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
+df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
+df.head()
+------------------
+
+
+---------------------------------------------------------------------------
+ConnectionRefusedError                    Traceback (most recent call last)
+File /usr/lib/python3.13/urllib/request.py:1319, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
+   1318 try:
+-> 1319     h.request(req.get_method(), req.selector, req.data, headers,
+   1320               encode_chunked=req.has_header('Transfer-encoding'))
+   1321 except OSError as err: # timeout error
+
+File /usr/lib/python3.13/http/client.py:1338, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
+   1337 """Send a complete request to the server."""
+-> 1338 self._send_request(method, url, body, headers, encode_chunked)
+
+File /usr/lib/python3.13/http/client.py:1384, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
+   1383     body = _encode(body, 'body')
+-> 1384 self.endheaders(body, encode_chunked=encode_chunked)
+
+File /usr/lib/python3.13/http/client.py:1333, in HTTPConnection.endheaders(self, message_body, encode_chunked)
+   1332     raise CannotSendHeader()
+-> 1333 self._send_output(message_body, encode_chunked=encode_chunked)
+
+File /usr/lib/python3.13/http/client.py:1093, in HTTPConnection._send_output(self, message_body, encode_chunked)
+   1092 del self._buffer[:]
+-> 1093 self.send(msg)
+   1095 if message_body is not None:
+   1096 
+   1097     # create a consistent interface to message_body
+
+File /usr/lib/python3.13/http/client.py:1037, in HTTPConnection.send(self, data)
+   1036 if self.auto_open:
+-> 1037     self.connect()
+   1038 else:
+
+File /usr/lib/python3.13/http/client.py:1472, in HTTPSConnection.connect(self)
+   1470 "Connect to a host on a given (SSL) port."
+-> 1472 super().connect()
+   1474 if self._tunnel_host:
+
+File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
+   1002 sys.audit("http.client.connect", self, self.host, self.port)
+-> 1003 self.sock = self._create_connection(
+   1004     (self.host,self.port), self.timeout, self.source_address)
+   1005 # Might fail in OSs that don't implement TCP_NODELAY
+
+File /usr/lib/python3.13/socket.py:864, in create_connection(address, timeout, source_address, all_errors)
+    863 if not all_errors:
+--> 864     raise exceptions[0]
+    865 raise ExceptionGroup("create_connection failed", exceptions)
 
 File /usr/lib/python3.13/socket.py:849, in create_connection(address, timeout, source_address, all_errors)
     848     sock.bind(source_address)
@@ -9628,34 +12853,114 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[2], line 4
-      1 import pandas as pdfunc(*args)
+Cell In[2], line 3
+      1 # Load data
+      2 data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
+----> 3 df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
+      4 df.head()
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
+   1013 kwds_defaults = _refine_defaults_read(
+   1014     dialect,
+   1015     delimiter,
+   (...)
+   1022     dtype_backend=dtype_backend,
+   1023 )
+   1024 kwds.update(kwds_defaults)
+-> 1026 return _read(filepath_or_buffer, kwds)
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
+    617 _validate_names(kwds.get("names", None))
+    619 # Create the parser.
+--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
+    622 if chunksize or iterator:
+    623     return parser
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
+   1617     self.options["has_index_names"] = kwds["has_index_names"]
+   1619 self.handles: IOHandles | None = None
+-> 1620 self._engine = self._make_engine(f, self.engine)
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
+   1878     if "b" not in mode:
+   1879         mode += "b"
+-> 1880 self.handles = get_handle(
+   1881     f,
+   1882     mode,
+   1883     encoding=self.options.get("encoding", None),
+   1884     compression=self.options.get("compression", None),
+   1885     memory_map=self.options.get("memory_map", False),
+   1886     is_text=is_text,
+   1887     errors=self.options.get("encoding_errors", "strict"),
+   1888     storage_options=self.options.get("storage_options", None),
+   1889 )
+   1890 assert self.handles is not None
+   1891 f = self.handles.handle
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
+    725     codecs.lookup_error(errors)
+    727 # open URLs
+--> 728 ioargs = _get_filepath_or_buffer(
+    729     path_or_buf,
+    730     encoding=encoding,
+    731     compression=compression,
+    732     mode=mode,
+    733     storage_options=storage_options,
+    734 )
+    736 handle = ioargs.filepath_or_buffer
+    737 handles: list[BaseBuffer]
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:384, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
+    382 # assuming storage_options is to be interpreted as headers
+    383 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
+--> 384 with urlopen(req_info) as req:
+    385     content_encoding = req.headers.get("Content-Encoding", None)
+    386     if content_encoding == "gzip":
+    387         # Override compression based on Content-Encoding header
+
+File /usr/lib/python3/dist-packages/pandas/io/common.py:289, in urlopen(*args, **kwargs)
+    283 """
+    284 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
+    285 the stdlib.
+    286 """
+    287 import urllib.request
+--> 289 return urllib.request.urlopen(*args, **kwargs)
+
+File /usr/lib/python3.13/urllib/request.py:189, in urlopen(url, data, timeout, context)
+    187 else:
+    188     opener = _opener
+--> 189 return opener.open(url, data, timeout)
+
+File /usr/lib/python3.13/urllib/request.py:489, in OpenerDirector.open(self, fullurl, data, timeout)
+    486     req = meth(req)
+    488 sys.audit('urllib.Request', req.full_url, req.data, req.headers, req.get_method())
+--> 489 response = self._open(req, data)
+    491 # post-process response
+    492 meth_name = protocol+"_response"
+
+File /usr/lib/python3.13/urllib/request.py:506, in OpenerDirector._open(self, req, data)
+    503     return result
+    505 protocol = req.type
+--> 506 result = self._call_chain(self.handle_open, protocol, protocol +
+    507                           '_open', req)
+    508 if result:
+    509     return result
+
+File /usr/lib/python3.13/urllib/request.py:466, in OpenerDirector._call_chain(self, chain, kind, meth_name, *args)
+    464 for handler in handlers:
+    465     func = getattr(handler, meth_name)
+--> 466     result = func(*args)
+    467     if result is not None:
+    468         return result
+
+File /usr/lib/python3.13/urllib/request.py:1367, in HTTPSHandler.https_open(self, req)
+   1366 def https_open(self, req):
+-> 1367     return self.do_open(http.client.HTTPSConnection, req,
+   1368                         context=self._context)
 
-      3    467 url      = if result "is nothttps://stats.idre.ucla.edu/stat/data/hsb2.csv None":
-    468
-         return----> 4 result
-
-File  hsb2 /usr/lib/python3.13/urllib/request.py:1367= , in pdHTTPSHandler.https_open.(self, req)read_table
-(   1366url def,  https_open(delimiterself=, req):
--> 1367"     return, "self.)
-
-File do_open/usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405(, in httpread_table.client.HTTPSConnection(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend),
-    1392 kwds_defaults req=, _refine_defaults_read(
-
-   1393   1368     dialect,
-    1394     delimiter,
-                           (...)context
-   1401=     dtype_backend=selfdtype_backend,
-.   1402 )
-_context   1403) kwds
-
-File .update(kwds_defaults)
-/usr/lib/python3.13/urllib/request.py:1322-> 1405, in  returnAbstractHTTPHandler.do_open (self, http_class, req, **http_conn_args)_read(
-   1319filepath_or_buffer,         h .request(reqkwds).
-
-File get_method(), req/usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620., in selector, req_read.(filepath_or_buffer, kwds)
-data, headers,
-    617 _validate_names(kwds   1320.                   encode_chunkedget("=reqnames.has_header('Transfer-encoding'))
+File /usr/lib/python3.13/urllib/request.py:1322, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
+   1319         h.request(req.get_method(), req.selector, req.data, headers,
+   1320                   encode_chunked=req.has_header('Transfer-encoding'))
    1321     except OSError as err: # timeout error
 -> 1322         raise URLError(err)
    1323     r = h.getresponse()
@@ -9663,10 +12968,91 @@
 
 URLError: <urlopen error [Errno 111] Connection refused>
 
-******************************************************************************
+An error occurred while executing the following cell:
+------------------
+# Load data
+data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
+df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
+df.head()
+------------------
+
+
+---------------------------------------------------------------------------
+ConnectionRefusedError                    Traceback (most recent call last)
+File /usr/lib/python3.13/urllib/request.py:1319, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
+   1318 try:
+-> 1319     h.request(req.get_method(), req.selector, req.data, headers,
+   1320               encode_chunked=req.has_header('Transfer-encoding'))
+   1321 except OSError as err: # timeout error
+
+File /usr/lib/python3.13/http/client.py:1338, in HTTPConnection.request(self, method, url, body, headers, encode_chunked)
+   1337 """Send a complete request to the server."""
+-> 1338 self._send_request(method, url, body, headers, encode_chunked)
+
+File /usr/lib/python3.13/http/client.py:1384, in HTTPConnection._send_request(self, method, url, body, headers, encode_chunked)
+   1383     body = _encode(body, 'body')
+-> 1384 self.endheaders(body, encode_chunked=encode_chunked)
+
+File /usr/lib/python3.13/http/client.py:1333, in HTTPConnection.endheaders(self, message_body, encode_chunked)
+   1332     raise CannotSendHeader()
+-> 1333 self._send_output(message_body, encode_chunked=encode_chunked)
+
+File /usr/lib/python3.13/http/client.py:1093, in HTTPConnection._send_output(self, message_body, encode_chunked)
+   1092 del self._buffer[:]
+-> 1093 self.send(msg)
+   1095 if message_body is not None:
+   1096 
+   1097     # create a consistent interface to message_body
+
+File /usr/lib/python3.13/http/client.py:1037, in HTTPConnection.send(self, data)
+   1036 if self.auto_open:
+-> 1037     self.connect()
+   1038 else:
+
+File /usr/lib/python3.13/http/client.py:1472, in HTTPSConnection.connect(self)
+   1470 "Connect to a host on a given (SSL) port."
+-> 1472 super().connect()
+   1474 if self._tunnel_host:
+
+File /usr/lib/python3.13/http/client.py:1003, in HTTPConnection.connect(self)
+   1002 sys.audit("http.client.connect", self, self.host, self.port)
+-> 1003 self.sock = self._create_connection(
+   1004     (self.host,self.port), self.timeout, self.source_address)
+   1005 # Might fail in OSs that don't implement TCP_NODELAY
+
+File /usr/lib/python3.13/socket.py:864, in create_connection(address, timeout, source_address, all_errors)
+    863 if not all_errors:
+--> 864     raise exceptions[0]
+    865 raise ExceptionGroup("create_connection failed", exceptions)
+
+File /usr/lib/python3.13/socket.py:849, in create_connection(address, timeout, source_address, all_errors)
+    848     sock.bind(source_address)
+--> 849 sock.connect(sa)
+    850 # Break explicitly a reference cycle
+
+ConnectionRefusedError: [Errno 111] Connection refused
+
+During handling of the above exception, another exception occurred:
+
+URLError                                  Traceback (most recent call last)
+Cell In[2], line 3
+      1 # Load data
+      2 data_url = "https://raw.githubusercontent.com/nguyen-toan/ISLR/07fd968ea484b5f6febc7b392a28eb64329a4945/dataset/Advertising.csv"
+----> 3 df = pd.read_csv(data_url).drop('Unnamed: 0', axis=1)
+      4 df.head()
 
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
+   1013 kwds_defaults = _refine_defaults_read(
+   1014     dialect,
+   1015     delimiter,
+   (...)
+   1022     dtype_backend=dtype_backend,
+   1023 )
+   1024 kwds.update(kwds_defaults)
+-> 1026 return _read(filepath_or_buffer, kwds)
 
-", None))
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
+    617 _validate_names(kwds.get("names", None))
     619 # Create the parser.
 --> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
     622 if chunksize or iterator:
@@ -9769,424 +13155,283 @@
 
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/interactions_anova.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/rolling_ls.ipynb
 An error occurred while executing the following cell:
 ------------------
-import os
-import shutil
-
+import matplotlib.pyplot as plt
 import numpy as np
-import requests
-
-np.set_printoptions(precision=4, suppress=True)
-
 import pandas as pd
+import pandas_datareader as pdr
+import seaborn
 
-pd.set_option("display.width", 100)
-import matplotlib.pyplot as plt
-from statsmodels.formula.api import ols
-from statsmodels.graphics.api import abline_plot, interaction_plot
-from statsmodels.stats.anova import anova_lm
-
-
-def download_file(url, mode="t"):
-    local_filename = url.split("/")[-1]
-    if os.path.exists(local_filename):
-        return local_filename
-    with requests.get(url, stream=True) as r:
-        with open(local_filename, f"w{mode}") as f:
-            f.write(r.text)
-    return local_filename
-
-
-url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
-salary_table = pd.read_csv(download_file(url), sep="\t")
+import statsmodels.api as sm
+from statsmodels.regression.rolling import RollingOLS
 
-E = salary_table.E
-M = salary_table.M
-X = salary_table.X
-S = salary_table.S
+seaborn.set_style("darkgrid")
+pd.plotting.register_matplotlib_converters()
+%matplotlib inline
 ------------------
 
 
 ---------------------------------------------------------------------------
-ConnectionRefusedError                    Traceback (most recent call last)
-File /usr/lib/python3/dist-packages/urllib3/connection.py:198, in HTTPConnection._new_conn(self)
-    197 try:
---> 198     sock = connection.create_connection(
-    199         (self._dns_host, self.port),
-    200         self.timeout,
-    201         source_address=self.source_address,
-    202         socket_options=self.socket_options,
-    203     )
-    204 except socket.gaierror as e:
-
-File /usr/lib/python3/dist-packages/urllib3/util/connection.py:85, in create_connection(address, timeout, source_address, socket_options)
-     84 try:
----> 85     raise err
-     86 finally:
-     87     # Break explicitly a reference cycle
-
-File /usr/lib/python3/dist-packages/urllib3/util/connection.py:73, in create_connection(address, timeout, source_address, socket_options)
-     72     sock.bind(source_address)
----> 73 sock.connect(sa)
-     74 # Break explicitly a reference cycle
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 4
+      2 import numpy as np
+      3 import pandas as pd
+----> 4 import pandas_datareader as pdr
+      5 import seaborn
+      7 import statsmodels.api as sm
 
-ConnectionRefusedError: [Errno 111] Connection refused
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-The above exception was the direct cause of the following exception:
+An error occurred while executing the following cell:
+------------------
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pandas_datareader as pdr
+import seaborn
 
-NewConnectionError                        Traceback (most recent call last)
-File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:773, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
-    772 try:
---> 773     self._prepare_proxy(conn)
-    774 except (BaseSSLError, OSError, SocketTimeout) as e:
+import statsmodels.api as sm
+from statsmodels.regression.rolling import RollingOLS
 
-File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:1042, in HTTPSConnectionPool._prepare_proxy(self, conn)
-   1036 conn.set_tunnel(
-   1037     scheme=tunnel_scheme,
-   1038     host=self._tunnel_host,
-   1039     port=self.port,
-   1040     headers=self.proxy_headers,
-   1041 )
--> 1042 conn.connect()
+seaborn.set_style("darkgrid")
+pd.plotting.register_matplotlib_converters()
+%matplotlib inline
+------------------
 
-File /usr/lib/python3/dist-packages/urllib3/connection.py:753, in HTTPSConnection.connect(self)
-    752 sock: socket.socket | ssl.SSLSocket
---> 753 self.sock = sock = self._new_conn()
-    754 server_hostname: str = self.host
 
-File /usr/lib/python3/dist-packages/urllib3/connection.py:213, in HTTPConnection._new_conn(self)
-    212 except OSError as e:
---> 213     raise NewConnectionError(
-    214         self, f"Failed to establish a new connection: {e}"
-    215     ) from e
-    217 sys.audit("http.client.connect", self, self.host, self.port)
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 4
+      2 import numpy as np
+      3 import pandas as pd
+----> 4 import pandas_datareader as pdr
+      5 import seaborn
+      7 import statsmodels.api as sm
 
-NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-The above exception was the direct cause of the following exception:
+******************************************************************************
 
-ProxyError                                Traceback (most recent call last)
-ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused'))
 
-The above exception was the direct cause of the following exception:
 
-MaxRetryError                             Traceback (most recent call last)
-File /usr/lib/python3/dist-packages/requests/adapters.py:644, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
-    643 try:
---> 644     resp = conn.urlopen(
-    645         method=request.method,
-    646         url=url,
-    647         body=request.body,
-    648         headers=request.headers,
-    649         redirect=False,
-    650         assert_same_host=False,
-    651         preload_content=False,
-    652         decode_content=False,
-    653         retries=self.max_retries,
-    654         timeout=timeout,
-    655         chunked=chunked,
-    656     )
-    658 except (ProtocolError, OSError) as err:
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/recursive_ls.ipynb
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+from pandas_datareader.data import DataReader
 
-File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:841, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
-    839     new_e = ProtocolError("Connection aborted.", new_e)
---> 841 retries = retries.increment(
-    842     method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
-    843 )
-    844 retries.sleep()
+np.set_printoptions(suppress=True)
+------------------
 
-File /usr/lib/python3/dist-packages/urllib3/util/retry.py:519, in Retry.increment(self, method, url, response, error, _pool, _stacktrace)
-    518     reason = error or ResponseError(cause)
---> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
-    521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
 
-MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused')))
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 6
+      4 import pandas as pd
+      5 import statsmodels.api as sm
+----> 6 from pandas_datareader.data import DataReader
+      8 np.set_printoptions(suppress=True)
 
-During handling of the above exception, another exception occurred:
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-ProxyError                                Traceback (most recent call last)
-Cell In[2], line 29
-     25     return local_filename
-     28 url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
----> 29 salary_table = pd.read_csv(download_file(url), sep="\t")
-     31 E = salary_table.E
-     32 M = salary_table.M
+An error occurred while executing the following cell:
+------------------
+%matplotlib inline
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+from pandas_datareader.data import DataReader
 
-Cell In[2], line 22, in download_file(url, mode)
-     20 if os.path.exists(local_filename):
-     21     return local_filename
----> 22 with requests.get(url, stream=True) as r:
-     23     with open(local_filename, f"w{mode}") as f:
-     24         f.write(r.text)
+np.set_printoptions(suppress=True)
+------------------
 
-File /usr/lib/python3/dist-packages/requests/api.py:73, in get(url, params, **kwargs)
-     62 def get(url, params=None, **kwargs):
-     63     r"""Sends a GET request.
-     64 
-     65     :param url: URL for the new :class:`Request` object.
-   (...)
-     70     :rtype: requests.Response
-     71     """
----> 73     return request("get", url, params=params, **kwargs)
 
-File /usr/lib/python3/dist-packages/requests/api.py:59, in request(method, url, **kwargs)
-     55 # By using the 'with' statement we are sure the session is closed, thus we
-     56 # avoid leaving sockets open which can trigger a ResourceWarning in some
-     57 # cases, and look like a memory leak in others.
-     58 with sessions.Session() as session:
----> 59     return session.request(method=method, url=url, **kwargs)
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 6
+      4 import pandas as pd
+      5 import statsmodels.api as sm
+----> 6 from pandas_datareader.data import DataReader
+      8 np.set_printoptions(suppress=True)
 
-File /usr/lib/python3/dist-packages/requests/sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
-    584 send_kwargs = {
-    585     "timeout": timeout,
-    586     "allow_redirects": allow_redirects,
-    587 }
-    588 send_kwargs.update(settings)
---> 589 resp = self.send(prep, **send_kwargs)
-    591 return resp
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-File /usr/lib/python3/dist-packages/requests/sessions.py:703, in Session.send(self, request, **kwargs)
-    700 start = preferred_clock()
-    702 # Send the request
---> 703 r = adapter.send(request, **kwargs)
-    705 # Total elapsed time of the request (approximately)
-    706 elapsed = preferred_clock() - start
+******************************************************************************
 
-File /usr/lib/python3/dist-packages/requests/adapters.py:671, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
-    668     raise RetryError(e, request=request)
-    670 if isinstance(e.reason, _ProxyError):
---> 671     raise ProxyError(e, request=request)
-    673 if isinstance(e.reason, _SSLError):
-    674     # This branch is for urllib3 v1.22 and later.
-    675     raise SSLError(e, request=request)
 
-ProxyError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_news.ipynb
 An error occurred while executing the following cell:
 ------------------
-import os
-import shutil
-
-import numpy as np
-import requests
+import pandas_datareader as pdr
 
-np.set_printoptions(precision=4, suppress=True)
+levels = pdr.get_data_fred(
+    ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
+).to_period("M")
+infl = np.log(levels).diff().iloc[1:] * 1200
+infl.columns = ["PCE", "CPI"]
 
-import pandas as pd
+# Remove two outliers and de-mean the series
+infl.loc["2001-09":"2001-10", "PCE"] = np.nan
+------------------
 
-pd.set_option("display.width", 100)
-import matplotlib.pyplot as plt
-from statsmodels.formula.api import ols
-from statsmodels.graphics.api import abline_plot, interaction_plot
-from statsmodels.stats.anova import anova_lm
 
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[14], line 1
+----> 1 import pandas_datareader as pdr
+      3 levels = pdr.get_data_fred(
+      4     ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
+      5 ).to_period("M")
+      6 infl = np.log(levels).diff().iloc[1:] * 1200
 
-def download_file(url, mode="t"):
-    local_filename = url.split("/")[-1]
-    if os.path.exists(local_filename):
-        return local_filename
-    with requests.get(url, stream=True) as r:
-        with open(local_filename, f"w{mode}") as f:
-            f.write(r.text)
-    return local_filename
+ModuleNotFoundError: No module named 'pandas_datareader'
 
+An error occurred while executing the following cell:
+------------------
+import pandas_datareader as pdr
 
-url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
-salary_table = pd.read_csv(download_file(url), sep="\t")
+levels = pdr.get_data_fred(
+    ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
+).to_period("M")
+infl = np.log(levels).diff().iloc[1:] * 1200
+infl.columns = ["PCE", "CPI"]
 
-E = salary_table.E
-M = salary_table.M
-X = salary_table.X
-S = salary_table.S
+# Remove two outliers and de-mean the series
+infl.loc["2001-09":"2001-10", "PCE"] = np.nan
 ------------------
 
 
 ---------------------------------------------------------------------------
-ConnectionRefusedError                    Traceback (most recent call last)
-File /usr/lib/python3/dist-packages/urllib3/connection.py:198, in HTTPConnection._new_conn(self)
-    197 try:
---> 198     sock = connection.create_connection(
-    199         (self._dns_host, self.port),
-    200         self.timeout,
-    201         source_address=self.source_address,
-    202         socket_options=self.socket_options,
-    203     )
-    204 except socket.gaierror as e:
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[14], line 1
+----> 1 import pandas_datareader as pdr
+      3 levels = pdr.get_data_fred(
+      4     ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
+      5 ).to_period("M")
+      6 infl = np.log(levels).diff().iloc[1:] * 1200
 
-File /usr/lib/python3/dist-packages/urllib3/util/connection.py:85, in create_connection(address, timeout, source_address, socket_options)
-     84 try:
----> 85     raise err
-     86 finally:
-     87     # Break explicitly a reference cycle
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-File /usr/lib/python3/dist-packages/urllib3/util/connection.py:73, in create_connection(address, timeout, source_address, socket_options)
-     72     sock.bind(source_address)
----> 73 sock.connect(sa)
-     74 # Break explicitly a reference cycle
+******************************************************************************
 
-ConnectionRefusedError: [Errno 111] Connection refused
 
-The above exception was the direct cause of the following exception:
 
-NewConnectionError                        Traceback (most recent call last)
-File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:773, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
-    772 try:
---> 773     self._prepare_proxy(conn)
-    774 except (BaseSSLError, OSError, SocketTimeout) as e:
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_dfm_coincident.ipynb
+An error occurred while executing the following cell:
+------------------
+from pandas_datareader.data import DataReader
 
-File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:1042, in HTTPSConnectionPool._prepare_proxy(self, conn)
-   1036 conn.set_tunnel(
-   1037     scheme=tunnel_scheme,
-   1038     host=self._tunnel_host,
-   1039     port=self.port,
-   1040     headers=self.proxy_headers,
-   1041 )
--> 1042 conn.connect()
+# Get the datasets from FRED
+start = '1979-01-01'
+end = '2014-12-01'
+indprod = DataReader('IPMAN', 'fred', start=start, end=end)
+income = DataReader('W875RX1', 'fred', start=start, end=end)
+sales = DataReader('CMRMTSPL', 'fred', start=start, end=end)
+emp = DataReader('PAYEMS', 'fred', start=start, end=end)
+# dta = pd.concat((indprod, income, sales, emp), axis=1)
+# dta.columns = ['indprod', 'income', 'sales', 'emp']
+------------------
 
-File /usr/lib/python3/dist-packages/urllib3/connection.py:753, in HTTPSConnection.connect(self)
-    752 sock: socket.socket | ssl.SSLSocket
---> 753 self.sock = sock = self._new_conn()
-    754 server_hostname: str = self.host
 
-File /usr/lib/python3/dist-packages/urllib3/connection.py:213, in HTTPConnection._new_conn(self)
-    212 except OSError as e:
---> 213     raise NewConnectionError(
-    214         self, f"Failed to establish a new connection: {e}"
-    215     ) from e
-    217 sys.audit("http.client.connect", self, self.host, self.port)
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[2], line 1
+----> 1 from pandas_datareader.data import DataReader
+      3 # Get the datasets from FRED
+      4 start = '1979-01-01'
 
-NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-The above exception was the direct cause of the following exception:
+An error occurred while executing the following cell:
+------------------
+from pandas_datareader.data import DataReader
 
-ProxyError                                Traceback (most recent call last)
-ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused'))
+# Get the datasets from FRED
+start = '1979-01-01'
+end = '2014-12-01'
+indprod = DataReader('IPMAN', 'fred', start=start, end=end)
+income = DataReader('W875RX1', 'fred', start=start, end=end)
+sales = DataReader('CMRMTSPL', 'fred', start=start, end=end)
+emp = DataReader('PAYEMS', 'fred', start=start, end=end)
+# dta = pd.concat((indprod, income, sales, emp), axis=1)
+# dta.columns = ['indprod', 'income', 'sales', 'emp']
+------------------
 
-The above exception was the direct cause of the following exception:
 
-MaxRetryError                             Traceback (most recent call last)
-File /usr/lib/python3/dist-packages/requests/adapters.py:644, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
-    643 try:
---> 644     resp = conn.urlopen(
-    645         method=request.method,
-    646         url=url,
-    647         body=request.body,
-    648         headers=request.headers,
-    649         redirect=False,
-    650         assert_same_host=False,
-    651         preload_content=False,
-    652         decode_content=False,
-    653         retries=self.max_retries,
-    654         timeout=timeout,
-    655         chunked=chunked,
-    656     )
-    658 except (ProtocolError, OSError) as err:
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[2], line 1
+----> 1 from pandas_datareader.data import DataReader
+      3 # Get the datasets from FRED
+      4 start = '1979-01-01'
 
-File /usr/lib/python3/dist-packages/urllib3/connectionpool.py:841, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
-    839     new_e = ProtocolError("Connection aborted.", new_e)
---> 841 retries = retries.increment(
-    842     method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
-    843 )
-    844 retries.sleep()
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-File /usr/lib/python3/dist-packages/urllib3/util/retry.py:519, in Retry.increment(self, method, url, response, error, _pool, _stacktrace)
-    518     reason = error or ResponseError(cause)
---> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
-    521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
+******************************************************************************
 
-MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
-During handling of the above exception, another exception occurred:
 
-ProxyError                                Traceback (most recent call last)
-Cell In[2], line 29
-     25     return local_filename
-     28 url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
----> 29 salary_table = pd.read_csv(download_file(url), sep="\t")
-     31 E = salary_table.E
-     32 M = salary_table.M
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_cycles.ipynb
+An error occurred while executing the following cell:
+------------------
+from pandas_datareader.data import DataReader
+endog = DataReader('UNRATE', 'fred', start='1954-01-01')
+endog.index.freq = endog.index.inferred_freq
+------------------
 
-Cell In[2], line 22, in download_file(url, mode)
-     20 if os.path.exists(local_filename):
-     21     return local_filename
----> 22 with requests.get(url, stream=True) as r:
-     23     with open(local_filename, f"w{mode}") as f:
-     24         f.write(r.text)
 
-File /usr/lib/python3/dist-packages/requests/api.py:73, in get(url, params, **kwargs)
-     62 def get(url, params=None, **kwargs):
-     63     r"""Sends a GET request.
-     64 
-     65     :param url: URL for the new :class:`Request` object.
-   (...)
-     70     :rtype: requests.Response
-     71     """
----> 73     return request("get", url, params=params, **kwargs)
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[3], line 1
+----> 1 from pandas_datareader.data import DataReader
+      2 endog = DataReader('UNRATE', 'fred', start='1954-01-01')
+      3 endog.index.freq = endog.index.inferred_freq
 
-File /usr/lib/python3/dist-packages/requests/api.py:59, in request(method, url, **kwargs)
-     55 # By using the 'with' statement we are sure the session is closed, thus we
-     56 # avoid leaving sockets open which can trigger a ResourceWarning in some
-     57 # cases, and look like a memory leak in others.
-     58 with sessions.Session() as session:
----> 59     return session.request(method=method, url=url, **kwargs)
+ModuleNotFoundError: No module named 'pandas_datareader'
 
-File /usr/lib/python3/dist-packages/requests/sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
-    584 send_kwargs = {
-    585     "timeout": timeout,
-    586     "allow_redirects": allow_redirects,
-    587 }
-    588 send_kwargs.update(settings)
---> 589 resp = self.send(prep, **send_kwargs)
-    591 return resp
+An error occurred while executing the following cell:
+------------------
+from pandas_datareader.data import DataReader
+endog = DataReader('UNRATE', 'fred', start='1954-01-01')
+endog.index.freq = endog.index.inferred_freq
+------------------
 
-File /usr/lib/python3/dist-packages/requests/sessions.py:703, in Session.send(self, request, **kwargs)
-    700 start = preferred_clock()
-    702 # Send the request
---> 703 r = adapter.send(request, **kwargs)
-    705 # Total elapsed time of the request (approximately)
-    706 elapsed = preferred_clock() - start
 
-File /usr/lib/python3/dist-packages/requests/adapters.py:671, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
-    668     raise RetryError(e, request=request)
-    670 if isinstance(e.reason, _ProxyError):
---> 671     raise ProxyError(e, request=request)
-    673 if isinstance(e.reason, _SSLError):
-    674     # This branch is for urllib3 v1.22 and later.
-    675     raise SSLError(e, request=request)
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[3], line 1
+----> 1 from pandas_datareader.data import DataReader
+      2 endog = DataReader('UNRATE', 'fred', start='1954-01-01')
+      3 endog.index.freq = endog.index.inferred_freq
 
-ProxyError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f04ff9a7380>: Failed to establish a new connection: [Errno 111] Connection refused')))
+ModuleNotFoundError: No module named 'pandas_datareader'
 
 ******************************************************************************
 
 
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_internet.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/contrasts.ipynb
 An error occurred while executing the following cell:
 ------------------
-import requests
-from io import BytesIO
-from zipfile import ZipFile
+import pandas as pd
 
-# Download the dataset
-df = pd.read_table(
-    "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
-    skiprows=1, header=None, sep='\s+', engine='python',
-    names=['internet','dinternet']
-)
+url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
+hsb2 = pd.read_table(url, delimiter=",")
 ------------------
 
------ stderr -----
-<>:8: SyntaxWarning: invalid escape sequence '\s'
-<>:8: SyntaxWarning: invalid escape sequence '\s'
-/tmp/ipykernel_1065844/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
-  skiprows=1, header=None, sep='\s+', engine='python',
------ stderr -----
-/tmp/ipykernel_1065844/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
-  skiprows=1, header=None, sep='\s+', engine='python',
-------------------
 
 ---------------------------------------------------------------------------
 ConnectionRefusedError                    Traceback (most recent call last)
@@ -10246,14 +13491,10 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[3], line 6
-      3 from zipfile import ZipFile
-      5 # Download the dataset
-----> 6 df = pd.read_table(
-      7     "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
-      8     skiprows=1, header=None, sep='\s+', engine='python',
-      9     names=['internet','dinternet']
-     10 )
+Cell In[2], line 4
+      1 import pandas as pd
+      3 url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
+----> 4 hsb2 = pd.read_table(url, delimiter=",")
 
 File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405, in read_table(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    1392 kwds_defaults = _refine_defaults_read(
@@ -10366,27 +13607,12 @@
 
 An error occurred while executing the following cell:
 ------------------
-import requests
-from io import BytesIO
-from zipfile import ZipFile
+import pandas as pd
 
-# Download the dataset
-df = pd.read_table(
-    "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
-    skiprows=1, header=None, sep='\s+', engine='python',
-    names=['internet','dinternet']
-)
+url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
+hsb2 = pd.read_table(url, delimiter=",")
 ------------------
 
------ stderr -----
-<>:8: SyntaxWarning: invalid escape sequence '\s'
-<>:8: SyntaxWarning: invalid escape sequence '\s'
-/tmp/ipykernel_1065844/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
-  skiprows=1, header=None, sep='\s+', engine='python',
------ stderr -----
-/tmp/ipykernel_1065844/1758367982.py:8: SyntaxWarning: invalid escape sequence '\s'
-  skiprows=1, header=None, sep='\s+', engine='python',
-------------------
 
 ---------------------------------------------------------------------------
 ConnectionRefusedError                    Traceback (most recent call last)
@@ -10446,14 +13672,10 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[3], line 6
-      3 from zipfile import ZipFile
-      5 # Download the dataset
-----> 6 df = pd.read_table(
-      7     "https://raw.githubusercontent.com/jrnold/ssmodels-in-stan/master/StanStateSpace/data-raw/DK-data/internet.dat",
-      8     skiprows=1, header=None, sep='\s+', engine='python',
-      9     names=['internet','dinternet']
-     10 )
+Cell In[2], line 4
+      1 import pandas as pd
+      3 url = "https://stats.idre.ucla.edu/stat/data/hsb2.csv"
+----> 4 hsb2 = pd.read_table(url, delimiter=",")
 
 File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1405, in read_table(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    1392 kwds_defaults = _refine_defaults_read(
@@ -10569,69 +13791,11 @@
 
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_news.ipynb
-An error occurred while executing the following cell:
-------------------
-import pandas_datareader as pdr
-
-levels = pdr.get_data_fred(
-    ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
-).to_period("M")
-infl = np.log(levels).diff().iloc[1:] * 1200
-infl.columns = ["PCE", "CPI"]
-
-# Remove two outliers and de-mean the series
-infl.loc["2001-09":"2001-10", "PCE"] = np.nan
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[14], line 1
-----> 1 import pandas_datareader as pdr
-      3 levels = pdr.get_data_fred(
-      4     ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
-      5 ).to_period("M")
-      6 infl = np.log(levels).diff().iloc[1:] * 1200
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-An error occurred while executing the following cell:
-------------------
-import pandas_datareader as pdr
-
-levels = pdr.get_data_fred(
-    ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
-).to_period("M")
-infl = np.log(levels).diff().iloc[1:] * 1200
-infl.columns = ["PCE", "CPI"]
-
-# Remove two outliers and de-mean the series
-infl.loc["2001-09":"2001-10", "PCE"] = np.nan
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[14], line 1
-----> 1 import pandas_datareader as pdr
-      3 levels = pdr.get_data_fred(
-      4     ["PCEPILFE", "CPILFESL"], start="1999", end="2019"
-      5 ).to_period("M")
-      6 infl = np.log(levels).diff().iloc[1:] * 1200
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-******************************************************************************
-
-
-
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/ordinal_regression.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mstl_decomposition.ipynb
 An error occurred while executing the following cell:
 ------------------
-url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
-data_student = pd.read_stata(url)
+url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
+df = pd.read_csv(url)
 ------------------
 
 
@@ -10693,51 +13857,47 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[2], line 2
-      1 url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
-----> 2 data_student = pd.read_stata(url)
-
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:2117, in read_stata(filepath_or_buffer, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals, chunksize, iterator, compression, storage_options)
-   2114     return reader
-   2116 with reader:
--> 2117     return reader.read()
+Cell In[10], line 2
+      1 url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
+----> 2 df = pd.read_csv(url)
 
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:1691, in StataReader.read(self, nrows, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals)
-   1679 @Appender(_read_method_doc)
-   1680 def read(
-   1681     self,
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
+   1013 kwds_defaults = _refine_defaults_read(
+   1014     dialect,
+   1015     delimiter,
    (...)
-   1689     order_categoricals: bool | None = None,
-   1690 ) -> DataFrame:
--> 1691     self._ensure_open()
-   1693     # Handle options
-   1694     if convert_dates is None:
+   1022     dtype_backend=dtype_backend,
+   1023 )
+   1024 kwds.update(kwds_defaults)
+-> 1026 return _read(filepath_or_buffer, kwds)
 
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:1183, in StataReader._ensure_open(self)
-   1179 """
-   1180 Ensure the file has been opened and its header data read.
-   1181 """
-   1182 if not hasattr(self, "_path_or_buf"):
--> 1183     self._open_file()
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
+    617 _validate_names(kwds.get("names", None))
+    619 # Create the parser.
+--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
+    622 if chunksize or iterator:
+    623     return parser
 
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:1196, in StataReader._open_file(self)
-   1189 if not self._entered:
-   1190     warnings.warn(
-   1191         "StataReader is being used without using a context manager. "
-   1192         "Using StataReader as a context manager is the only supported method.",
-   1193         ResourceWarning,
-   1194         stacklevel=find_stack_level(),
-   1195     )
--> 1196 handles = get_handle(
-   1197     self._original_path_or_buf,
-   1198     "rb",
-   1199     storage_options=self._storage_options,
-   1200     is_text=False,
-   1201     compression=self._compression,
-   1202 )
-   1203 if hasattr(handles.handle, "seekable") and handles.handle.seekable():
-   1204     # If the handle is directly seekable, use it without an extra copy.
-   1205     self._path_or_buf = handles.handle
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
+   1617     self.options["has_index_names"] = kwds["has_index_names"]
+   1619 self.handles: IOHandles | None = None
+-> 1620 self._engine = self._make_engine(f, self.engine)
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
+   1878     if "b" not in mode:
+   1879         mode += "b"
+-> 1880 self.handles = get_handle(
+   1881     f,
+   1882     mode,
+   1883     encoding=self.options.get("encoding", None),
+   1884     compression=self.options.get("compression", None),
+   1885     memory_map=self.options.get("memory_map", False),
+   1886     is_text=is_text,
+   1887     errors=self.options.get("encoding_errors", "strict"),
+   1888     storage_options=self.options.get("storage_options", None),
+   1889 )
+   1890 assert self.handles is not None
+   1891 f = self.handles.handle
 
 File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
     725     codecs.lookup_error(errors)
@@ -10812,8 +13972,8 @@
 
 An error occurred while executing the following cell:
 ------------------
-url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
-data_student = pd.read_stata(url)
+url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
+df = pd.read_csv(url)
 ------------------
 
 
@@ -10875,51 +14035,47 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[2], line 2
-      1 url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
-----> 2 data_student = pd.read_stata(url)
-
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:2117, in read_stata(filepath_or_buffer, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals, chunksize, iterator, compression, storage_options)
-   2114     return reader
-   2116 with reader:
--> 2117     return reader.read()
+Cell In[10], line 2
+      1 url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
+----> 2 df = pd.read_csv(url)
 
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:1691, in StataReader.read(self, nrows, convert_dates, convert_categoricals, index_col, convert_missing, preserve_dtypes, columns, order_categoricals)
-   1679 @Appender(_read_method_doc)
-   1680 def read(
-   1681     self,
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
+   1013 kwds_defaults = _refine_defaults_read(
+   1014     dialect,
+   1015     delimiter,
    (...)
-   1689     order_categoricals: bool | None = None,
-   1690 ) -> DataFrame:
--> 1691     self._ensure_open()
-   1693     # Handle options
-   1694     if convert_dates is None:
+   1022     dtype_backend=dtype_backend,
+   1023 )
+   1024 kwds.update(kwds_defaults)
+-> 1026 return _read(filepath_or_buffer, kwds)
 
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:1183, in StataReader._ensure_open(self)
-   1179 """
-   1180 Ensure the file has been opened and its header data read.
-   1181 """
-   1182 if not hasattr(self, "_path_or_buf"):
--> 1183     self._open_file()
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
+    617 _validate_names(kwds.get("names", None))
+    619 # Create the parser.
+--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
+    622 if chunksize or iterator:
+    623     return parser
 
-File /usr/lib/python3/dist-packages/pandas/io/stata.py:1196, in StataReader._open_file(self)
-   1189 if not self._entered:
-   1190     warnings.warn(
-   1191         "StataReader is being used without using a context manager. "
-   1192         "Using StataReader as a context manager is the only supported method.",
-   1193         ResourceWarning,
-   1194         stacklevel=find_stack_level(),
-   1195     )
--> 1196 handles = get_handle(
-   1197     self._original_path_or_buf,
-   1198     "rb",
-   1199     storage_options=self._storage_options,
-   1200     is_text=False,
-   1201     compression=self._compression,
-   1202 )
-   1203 if hasattr(handles.handle, "seekable") and handles.handle.seekable():
-   1204     # If the handle is directly seekable, use it without an extra copy.
-   1205     self._path_or_buf = handles.handle
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
+   1617     self.options["has_index_names"] = kwds["has_index_names"]
+   1619 self.handles: IOHandles | None = None
+-> 1620 self._engine = self._make_engine(f, self.engine)
+
+File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
+   1878     if "b" not in mode:
+   1879         mode += "b"
+-> 1880 self.handles = get_handle(
+   1881     f,
+   1882     mode,
+   1883     encoding=self.options.get("encoding", None),
+   1884     compression=self.options.get("compression", None),
+   1885     memory_map=self.options.get("memory_map", False),
+   1886     is_text=is_text,
+   1887     errors=self.options.get("encoding_errors", "strict"),
+   1888     storage_options=self.options.get("storage_options", None),
+   1889 )
+   1890 assert self.handles is not None
+   1891 f = self.handles.handle
 
 File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
     725     codecs.lookup_error(errors)
@@ -10997,153 +14153,43 @@
 
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_dfm_coincident.ipynb
-An error occurred while executing the following cell:
-------------------
-from pandas_datareader.data import DataReader
-
-# Get the datasets from FRED
-start = '1979-01-01'
-end = '2014-12-01'
-indprod = DataReader('IPMAN', 'fred', start=start, end=end)
-income = DataReader('W875RX1', 'fred', start=start, end=end)
-sales = DataReader('CMRMTSPL', 'fred', start=start, end=end)
-emp = DataReader('PAYEMS', 'fred', start=start, end=end)
-# dta = pd.concat((indprod, income, sales, emp), axis=1)
-# dta.columns = ['indprod', 'income', 'sales', 'emp']
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 1
-----> 1 from pandas_datareader.data import DataReader
-      3 # Get the datasets from FRED
-      4 start = '1979-01-01'
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/interactions_anova.ipynb
 An error occurred while executing the following cell:
 ------------------
-from pandas_datareader.data import DataReader
-
-# Get the datasets from FRED
-start = '1979-01-01'
-end = '2014-12-01'
-indprod = DataReader('IPMAN', 'fred', start=start, end=end)
-income = DataReader('W875RX1', 'fred', start=start, end=end)
-sales = DataReader('CMRMTSPL', 'fred', start=start, end=end)
-emp = DataReader('PAYEMS', 'fred', start=start, end=end)
-# dta = pd.concat((indprod, income, sales, emp), axis=1)
-# dta.columns = ['indprod', 'income', 'sales', 'emp']
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 1
-----> 1 from pandas_datareader.data import DataReader
-      3 # Get the datasets from FRED
-      4 start = '1979-01-01'
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-******************************************************************************
-
-
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_plots.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/predict.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/distributed_estimation.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_rankcompare.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/copula.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_weights.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_0.ipynb
+import os
+import shutil
 
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_sarimax_pymc3.ipynb
-An error occurred while executing the following cell:
-------------------
-%matplotlib inline
-import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
-import pymc3 as pm
-import statsmodels.api as sm
-import theano
-import theano.tensor as tt
-from pandas.plotting import register_matplotlib_converters
-from pandas_datareader.data import DataReader
-
-plt.style.use("seaborn")
-register_matplotlib_converters()
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 5
-      3 import numpy as np
-      4 import pandas as pd
-----> 5 import pymc3 as pm
-      6 import statsmodels.api as sm
-      7 import theano
+import requests
 
-ModuleNotFoundError: No module named 'pymc3'
+np.set_printoptions(precision=4, suppress=True)
 
-An error occurred while executing the following cell:
-------------------
-%matplotlib inline
-import matplotlib.pyplot as plt
-import numpy as np
 import pandas as pd
-import pymc3 as pm
-import statsmodels.api as sm
-import theano
-import theano.tensor as tt
-from pandas.plotting import register_matplotlib_converters
-from pandas_datareader.data import DataReader
-
-plt.style.use("seaborn")
-register_matplotlib_converters()
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 5
-      3 import numpy as np
-      4 import pandas as pd
-----> 5 import pymc3 as pm
-      6 import statsmodels.api as sm
-      7 import theano
-
-ModuleNotFoundError: No module named 'pymc3'
-
-******************************************************************************
-
 
+pd.set_option("display.width", 100)
+import matplotlib.pyplot as plt
+from statsmodels.formula.api import ols
+from statsmodels.graphics.api import abline_plot, interaction_plot
+from statsmodels.stats.anova import anova_lm
 
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_varmax.ipynb
-An error occurred while executing the following cell:
-------------------
-import requests
-import shutil 
 
-def download_file(url):
-    local_filename = url.split('/')[-1]
+def download_file(url, mode="t"):
+    local_filename = url.split("/")[-1]
+    if os.path.exists(local_filename):
+        return local_filename
     with requests.get(url, stream=True) as r:
-        with open(local_filename, 'wb') as f:
-            shutil.copyfileobj(r.raw, f)
-
+        with open(local_filename, f"w{mode}") as f:
+            f.write(r.text)
     return local_filename
 
-filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
 
-dta = pd.read_stata(filename)
-dta.index = dta.qtr
-dta.index.freq = dta.index.inferred_freq
-endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]
+url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
+salary_table = pd.read_csv(download_file(url), sep="\t")
+
+E = salary_table.E
+M = salary_table.M
+X = salary_table.X
+S = salary_table.S
 ------------------
 
 
@@ -11201,12 +14247,12 @@
     215     ) from e
     217 sys.audit("http.client.connect", self, self.host, self.port)
 
-NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused
+NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused
 
 The above exception was the direct cause of the following exception:
 
 ProxyError                                Traceback (most recent call last)
-ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused'))
+ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused'))
 
 The above exception was the direct cause of the following exception:
 
@@ -11240,24 +14286,24 @@
 --> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
     521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
 
-MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 During handling of the above exception, another exception occurred:
 
 ProxyError                                Traceback (most recent call last)
-Cell In[3], line 12
-      8             shutil.copyfileobj(r.raw, f)
-     10     return local_filename
----> 12 filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
-     14 dta = pd.read_stata(filename)
-     15 dta.index = dta.qtr
+Cell In[2], line 29
+     25     return local_filename
+     28 url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
+---> 29 salary_table = pd.read_csv(download_file(url), sep="\t")
+     31 E = salary_table.E
+     32 M = salary_table.M
 
-Cell In[3], line 6, in download_file(url)
-      4 def download_file(url):
-      5     local_filename = url.split('/')[-1]
-----> 6     with requests.get(url, stream=True) as r:
-      7         with open(local_filename, 'wb') as f:
-      8             shutil.copyfileobj(r.raw, f)
+Cell In[2], line 22, in download_file(url, mode)
+     20 if os.path.exists(local_filename):
+     21     return local_filename
+---> 22 with requests.get(url, stream=True) as r:
+     23     with open(local_filename, f"w{mode}") as f:
+     24         f.write(r.text)
 
 File /usr/lib/python3/dist-packages/requests/api.py:73, in get(url, params, **kwargs)
      62 def get(url, params=None, **kwargs):
@@ -11300,27 +14346,44 @@
     674     # This branch is for urllib3 v1.22 and later.
     675     raise SSLError(e, request=request)
 
-ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+ProxyError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 An error occurred while executing the following cell:
 ------------------
+import os
+import shutil
+
+import numpy as np
 import requests
-import shutil 
 
-def download_file(url):
-    local_filename = url.split('/')[-1]
-    with requests.get(url, stream=True) as r:
-        with open(local_filename, 'wb') as f:
-            shutil.copyfileobj(r.raw, f)
+np.set_printoptions(precision=4, suppress=True)
+
+import pandas as pd
+
+pd.set_option("display.width", 100)
+import matplotlib.pyplot as plt
+from statsmodels.formula.api import ols
+from statsmodels.graphics.api import abline_plot, interaction_plot
+from statsmodels.stats.anova import anova_lm
 
+
+def download_file(url, mode="t"):
+    local_filename = url.split("/")[-1]
+    if os.path.exists(local_filename):
+        return local_filename
+    with requests.get(url, stream=True) as r:
+        with open(local_filename, f"w{mode}") as f:
+            f.write(r.text)
     return local_filename
 
-filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
 
-dta = pd.read_stata(filename)
-dta.index = dta.qtr
-dta.index.freq = dta.index.inferred_freq
-endog = dta.loc['1960-04-01':'1978-10-01', ['dln_inv', 'dln_inc', 'dln_consump']]
+url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
+salary_table = pd.read_csv(download_file(url), sep="\t")
+
+E = salary_table.E
+M = salary_table.M
+X = salary_table.X
+S = salary_table.S
 ------------------
 
 
@@ -11378,12 +14441,12 @@
     215     ) from e
     217 sys.audit("http.client.connect", self, self.host, self.port)
 
-NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused
+NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused
 
 The above exception was the direct cause of the following exception:
 
 ProxyError                                Traceback (most recent call last)
-ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused'))
+ProxyError: ('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused'))
 
 The above exception was the direct cause of the following exception:
 
@@ -11417,24 +14480,24 @@
 --> 519     raise MaxRetryError(_pool, url, reason) from reason  # type: ignore[arg-type]
     521 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)
 
-MaxRetryError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 During handling of the above exception, another exception occurred:
 
 ProxyError                                Traceback (most recent call last)
-Cell In[3], line 12
-      8             shutil.copyfileobj(r.raw, f)
-     10     return local_filename
----> 12 filename = download_file("https://www.stata-press.com/data/r12/lutkepohl2.dta")
-     14 dta = pd.read_stata(filename)
-     15 dta.index = dta.qtr
+Cell In[2], line 29
+     25     return local_filename
+     28 url = "https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/anova/salary/salary.table"
+---> 29 salary_table = pd.read_csv(download_file(url), sep="\t")
+     31 E = salary_table.E
+     32 M = salary_table.M
 
-Cell In[3], line 6, in download_file(url)
-      4 def download_file(url):
-      5     local_filename = url.split('/')[-1]
-----> 6     with requests.get(url, stream=True) as r:
-      7         with open(local_filename, 'wb') as f:
-      8             shutil.copyfileobj(r.raw, f)
+Cell In[2], line 22, in download_file(url, mode)
+     20 if os.path.exists(local_filename):
+     21     return local_filename
+---> 22 with requests.get(url, stream=True) as r:
+     23     with open(local_filename, f"w{mode}") as f:
+     24         f.write(r.text)
 
 File /usr/lib/python3/dist-packages/requests/api.py:73, in get(url, params, **kwargs)
      62 def get(url, params=None, **kwargs):
@@ -11477,69 +14540,13 @@
     674     # This branch is for urllib3 v1.22 and later.
     675     raise SSLError(e, request=request)
 
-ProxyError: HTTPSConnectionPool(host='www.stata-press.com', port=443): Max retries exceeded with url: /data/r12/lutkepohl2.dta (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f7d2fae9a90>: Failed to establish a new connection: [Errno 111] Connection refused')))
+ProxyError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /statsmodels/smdatasets/main/data/anova/salary/salary.table (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f832f90b380>: Failed to establish a new connection: [Errno 111] Connection refused')))
 
 ******************************************************************************
 
 
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/recursive_ls.ipynb
-An error occurred while executing the following cell:
-------------------
-%matplotlib inline
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
-from pandas_datareader.data import DataReader
-
-np.set_printoptions(suppress=True)
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 6
-      4 import pandas as pd
-      5 import statsmodels.api as sm
-----> 6 from pandas_datareader.data import DataReader
-      8 np.set_printoptions(suppress=True)
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-An error occurred while executing the following cell:
-------------------
-%matplotlib inline
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
-from pandas_datareader.data import DataReader
-
-np.set_printoptions(suppress=True)
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 6
-      4 import pandas as pd
-      5 import statsmodels.api as sm
-----> 6 from pandas_datareader.data import DataReader
-      8 np.set_printoptions(suppress=True)
-
-ModuleNotFoundError: No module named 'pandas_datareader'
-
-******************************************************************************
-
-
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mixed_lm_example.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressions.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/chi2_fitting.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_1.ipynb
-
-******************************************************************************
 ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/autoregressive_distributed_lag.ipynb
 An error occurred while executing the following cell:
 ------------------
@@ -11878,17 +14885,6698 @@
 ******************************************************************************
 
 
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mixed_lm_example.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_local_linear_trend.ipynb
+Exception ignored in atexit callback <bound method IPKernelApp.close of <ipykernel.kernelapp.IPKernelApp object at 0x7f0f385dcad0>>:
+Traceback (most recent call last):
+  File "/usr/lib/python3/dist-packages/ipykernel/kernelapp.py", line 422, in close
+    self.context.term()
+  File "/usr/lib/python3/dist-packages/zmq/sugar/context.py", line 264, in term
+    super().term()
+  File "_zmq.py", line 596, in zmq.backend.cython._zmq.Context.term
+  File "_zmq.py", line 179, in zmq.backend.cython._zmq._check_rc
+KeyboardInterrupt: 
 
 ******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/mstl_decomposition.ipynb
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/lowess.ipynb
+Kernel didn't respond in 60 seconds
+Kernel didn't respond in 60 seconds
+******************************************************************************
+
+
+
+******************************************************************************
+ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_tvpvar_mcmc_cfa.ipynb
 An error occurred while executing the following cell:
 ------------------
-url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
-df = pd.read_csv(url)
+import arviz as az
+
+# Collect the observation error covariance parameters
+az_obs_cov = az.convert_to_inference_data({
+    ('Var[%s]' % mod.endog_names[i] if i == j else
+     'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
+    store_obs_cov[nburn + 1:, i, j]
+    for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+
+# Plot the credible intervals
+az.plot_forest(az_obs_cov, figsize=(8, 7));
+------------------
+
+
+---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[16], line 1
+----> 1 import arviz as az
+      3 # Collect the observation error covariance parameters
+      4 az_obs_cov = az.convert_to_inference_data({
+      5     ('Var[%s]' % mod.endog_names[i] if i == j else
+      6      'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
+      7     store_obs_cov[nburn + 1:, i, j]
+      8     for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+
+ModuleNotFoundError: No module named 'arviz'
+
+An error occurred while executing the following cell:
+------------------
+import arviz as az
+
+# Collect the observation error covariance parameters
+az_obs_cov = az.convert_to_inference_data({
+    ('Var[%s]' % mod.endog_names[i] if i == j else
+     'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
+    store_obs_cov[nburn + 1:, i, j]
+    for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+
+# Plot the credible intervals
+az.plot_forest(az_obs_cov, figsize=(8, 7));
 ------------------
 
 
 ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[16], line 1
+----> 1 import arviz as az
+      3 # Collect the observation error covariance parameters
+      4 az_obs_cov = az.convert_to_inference_data({
+      5     ('Var[%s]' % mod.endog_names[i] if i == j else
+      6      'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
+      7     store_obs_cov[nburn + 1:, i, j]
+      8     for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+
+ModuleNotFoundError: No module named 'arviz'
+
+******************************************************************************
+
+
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gee_score_test_simulation.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_1.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/robust_models_0.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_concentrated_scale.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/predict.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gls.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_fixed_params.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/kernel_density.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/distributed_estimation.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_stata.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/discrete_choice_example.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_internet.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_chandrasekhar.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/pca_fertility_factors.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressive_distributed_lag.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_forecasting.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ordinal_regression.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/discrete_choice_overview.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_poisson.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_diagnostics.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_varmax.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quantile_regression.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_pymc3.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/copula.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/wls.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/categorical_interaction_plot.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mstl_decomposition.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_structural_harvey_jaeger.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/autoregressions.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ets.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stats_rankcompare.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/count_hurdle.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_arma_1.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_seasonal.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/postestimation_poisson.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/lowess.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/formulas.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/gee_nested_simulation.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_filters.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/theta-model.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_autoregression.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/variance_components.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/rolling_ls.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/chi2_fitting.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/linear_regression_diagnostics_plots.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_faq.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_weights.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_plots.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/influence_glm_logit.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/exponential_smoothing.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_arma_0.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/treatment_effect.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_regression.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stationarity_detrending_adf_kpss.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_news.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/recursive_ls.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mediation_survival.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/ols.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/plots_boxplots.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stl_decomposition.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/generic_mle.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/deterministics.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quasibinomial.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/metaanalysis1.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_dfm_coincident.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_dates.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_cycles.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/contrasts.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_formula.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/interactions_anova.ipynb
+Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_arma_0.ipynb
+Copying (without executing) /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_custom_models.ipynb to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_custom_models.ipynb
+Finished (without execution) /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_custom_models.ipynb
+Copying notebooks that failed execution (there are usually several in Debian because some need network and/or dependencies we don't have)
+cp -nav ../examples/notebooks/*.ipynb -t source/examples/notebooks/generated || true
+'../examples/notebooks/autoregressions.ipynb' -> 'source/examples/notebooks/generated/autoregressions.ipynb'
+'../examples/notebooks/autoregressive_distributed_lag.ipynb' -> 'source/examples/notebooks/generated/autoregressive_distributed_lag.ipynb'
+'../examples/notebooks/contrasts.ipynb' -> 'source/examples/notebooks/generated/contrasts.ipynb'
+'../examples/notebooks/interactions_anova.ipynb' -> 'source/examples/notebooks/generated/interactions_anova.ipynb'
+'../examples/notebooks/linear_regression_diagnostics_plots.ipynb' -> 'source/examples/notebooks/generated/linear_regression_diagnostics_plots.ipynb'
+'../examples/notebooks/lowess.ipynb' -> 'source/examples/notebooks/generated/lowess.ipynb'
+'../examples/notebooks/markov_autoregression.ipynb' -> 'source/examples/notebooks/generated/markov_autoregression.ipynb'
+'../examples/notebooks/mstl_decomposition.ipynb' -> 'source/examples/notebooks/generated/mstl_decomposition.ipynb'
+'../examples/notebooks/ordinal_regression.ipynb' -> 'source/examples/notebooks/generated/ordinal_regression.ipynb'
+'../examples/notebooks/recursive_ls.ipynb' -> 'source/examples/notebooks/generated/recursive_ls.ipynb'
+'../examples/notebooks/rolling_ls.ipynb' -> 'source/examples/notebooks/generated/rolling_ls.ipynb'
+'../examples/notebooks/statespace_chandrasekhar.ipynb' -> 'source/examples/notebooks/generated/statespace_chandrasekhar.ipynb'
+'../examples/notebooks/statespace_cycles.ipynb' -> 'source/examples/notebooks/generated/statespace_cycles.ipynb'
+'../examples/notebooks/statespace_dfm_coincident.ipynb' -> 'source/examples/notebooks/generated/statespace_dfm_coincident.ipynb'
+'../examples/notebooks/statespace_fixed_params.ipynb' -> 'source/examples/notebooks/generated/statespace_fixed_params.ipynb'
+'../examples/notebooks/statespace_local_linear_trend.ipynb' -> 'source/examples/notebooks/generated/statespace_local_linear_trend.ipynb'
+'../examples/notebooks/statespace_news.ipynb' -> 'source/examples/notebooks/generated/statespace_news.ipynb'
+'../examples/notebooks/statespace_sarimax_internet.ipynb' -> 'source/examples/notebooks/generated/statespace_sarimax_internet.ipynb'
+'../examples/notebooks/statespace_sarimax_pymc3.ipynb' -> 'source/examples/notebooks/generated/statespace_sarimax_pymc3.ipynb'
+'../examples/notebooks/statespace_sarimax_stata.ipynb' -> 'source/examples/notebooks/generated/statespace_sarimax_stata.ipynb'
+'../examples/notebooks/statespace_structural_harvey_jaeger.ipynb' -> 'source/examples/notebooks/generated/statespace_structural_harvey_jaeger.ipynb'
+'../examples/notebooks/statespace_tvpvar_mcmc_cfa.ipynb' -> 'source/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb'
+'../examples/notebooks/statespace_varmax.ipynb' -> 'source/examples/notebooks/generated/statespace_varmax.ipynb'
+'../examples/notebooks/theta-model.ipynb' -> 'source/examples/notebooks/generated/theta-model.ipynb'
+Replacing timestamps and build paths in examples output for reproducibility
+for html in `find source/examples/notebooks/generated -name "*.html" -o -name "*.ipynb" -o -name "*.ipynb.txt"` ; do \
+    sed -i -e 's#/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/#/usr/lib/python3/dist-packages/statsmodels/#g' \
+    -e 's# at 0x[0-9a-f]\{8,16\}\(&gt;\|>\)# at 0xadde5de1e8ed\1#g' \
+    -e 's#tmp/ipykernel_[0-9]\+#tmp/ipykernel_nnnnnnn#g' \
+    -e 's#^\s\+.\(iopub.execute_input\|iopub.status.busy\|iopub.status.idle\|shell.execute_reply\).:.*# #g' \
+    -e 's#\(Date:.*\)[A-Z][a-z]\+, \+[0-9]\+,\? \+[A-Z][a-z]\+,\? \+[0-9]\+#\1Sun, 10 Aug 2025#g' \
+    -e 's#\(Time:.*\)[0-9][0-9]:[0-9][0-9]:[0-9][0-9]#\113:13:47#g' ${html} ; \
+done
+Running sphinx-build
+@sphinx-build -M html source build
+Running Sphinx v8.2.3
+loading translations [en]... done
+Converting `source_suffix = '.rst'` to `source_suffix = {'.rst': 'restructuredtext'}`.
+loading intersphinx inventory 'numpy' from /usr/share/doc/python-numpy/html/objects.inv ...
+loading intersphinx inventory 'python' from /usr/share/doc/python3-doc/html/objects.inv ...
+loading intersphinx inventory 'pydagogue' from https://matthew-brett.github.io/pydagogue/objects.inv ...
+loading intersphinx inventory 'matplotlib' from /usr/share/doc/python-matplotlib-doc/html/objects.inv ...
+loading intersphinx inventory 'scipy' from /usr/share/doc/python-scipy-doc/html/objects.inv ...
+loading intersphinx inventory 'pandas' from /usr/share/doc/python-pandas-doc/html/objects.inv ...
+WARNING: failed to reach any of the inventories with the following issues:
+intersphinx inventory 'https://matthew-brett.github.io/pydagogue/objects.inv' not fetchable due to <class 'requests.exceptions.ProxyError'>: HTTPSConnectionPool(host='matthew-brett.github.io', port=443): Max retries exceeded with url: /pydagogue/objects.inv (Caused by ProxyError('Unable to connect to proxy', NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f46a8294590>: Failed to establish a new connection: [Errno 111] Connection refused')))
+Writing evaluated template result to /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/build/html/_static/nbsphinx-code-cells.css
+[autosummary] generating autosummary for: about.rst, anova.rst, api-structure.rst, api.rst, contingency_tables.rst, contrasts.rst, datasets/generated/anes96.rst, datasets/generated/cancer.rst, datasets/generated/ccard.rst, datasets/generated/china_smoking.rst, ..., rlm.rst, rlm_techn1.rst, sandbox.rst, statespace.rst, stats.rst, tools.rst, treatment.rst, tsa.rst, user-guide.rst, vector_ar.rst
+[autosummary] generating autosummary for: /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/datasets/statsmodels.datasets.clear_data_home.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/datasets/statsmodels.datasets.get_data_home.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/datasets/statsmodels.datasets.get_rdataset.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/datasets/statsmodels.datasets.webuse.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.__init__.test.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModelResults.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.LikelihoodModel.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.LikelihoodModelResults.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.Model.rst, ..., /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.var_model.VARResults.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.CointRankResults.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.JohansenTestResult.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECM.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.coint_johansen.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.select_coint_rank.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.select_order.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.x13.x13_arima_analysis.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.x13.x13_arima_select_order.rst
+[autosummary] generating autosummary for: /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.endog_names.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.exog_names.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.expandparams.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.fit.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.from_formula.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.hessian.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.hessian_factor.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.information.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.initialize.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/generated/statsmodels.base.model.GenericLikelihoodModel.loglike.rst, ..., /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.test_granger_causality.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.test_inst_causality.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.test_normality.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.test_whiteness.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_alpha.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_beta.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_det_coef.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_det_coef_coint.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_gamma.rst, /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.vecm.VECMResults.var_rep.rst
+building [mo]: targets for 0 po files that are out of date
+writing output... 
+building [html]: targets for 187 source files that are out of date
+updating environment: [new config] 6467 added, 0 changed, 0 removed
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+
+WARNING: Summarised items should not include the current module. Replace 'statsmodels.tools.print_version.show_versions' with 'tools.print_version.show_versions'. [autosummary.import_cycle]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/anova.rst:6: WARNING: Duplicate explicit target name: "anova". [docutils]
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 50
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
 ConnectionRefusedError                    Traceback (most recent call last)
 File /usr/lib/python3.13/urllib/request.py:1319, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
    1318 try:
@@ -11946,9 +21634,8 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[10], line 2
-      1 url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
-----> 2 df = pd.read_csv(url)
+Cell In[3], line 1
+----> 1 hsb2 = pandas.read_csv(url)
 
 File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    1013 kwds_defaults = _refine_defaults_read(
@@ -12059,13 +21746,429 @@
 
 URLError: <urlopen error [Errno 111] Connection refused>
 
-An error occurred while executing the following cell:
-------------------
-url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
-df = pd.read_csv(url)
-------------------
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 56
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[4], line 1
+----> 1 hsb2.groupby('race')['write'].mean()
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 74
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[9], line 1
+----> 1 contrast.matrix[hsb2.race-1, :][:20]
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 83
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[11], line 1
+----> 1 mod = ols("write ~ C(race, Treatment)", data=hsb2)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 83
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[12], line 1
+----> 1 res = mod.fit()
+
+NameError: name 'mod' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 83
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[13], line 1
+----> 1 print(res.summary())
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 100
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[16], line 1
+----> 1 mod = ols("write ~ C(race, Simple)", data=hsb2)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 100
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[17], line 1
+----> 1 res = mod.fit()
+
+NameError: name 'mod' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 100
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[18], line 1
+----> 1 print(res.summary())
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 115
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[22], line 1
+----> 1 mod = ols("write ~ C(race, Sum)", data=hsb2)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 115
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[23], line 1
+----> 1 res = mod.fit()
+
+NameError: name 'mod' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 115
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[24], line 1
+----> 1 print(res.summary())
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 121
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[25], line 1
+----> 1 hsb2.groupby('race')['write'].mean().mean()
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 136
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[29], line 1
+----> 1 mod = ols("write ~ C(race, Diff)", data=hsb2)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 136
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[30], line 1
+----> 1 res = mod.fit()
+
+NameError: name 'mod' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 136
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[31], line 1
+----> 1 print(res.summary())
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 144
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[32], line 1
+----> 1 res.params["C(race, Diff)[D.1]"]
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 144
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[33], line 1
+----> 1 hsb2.groupby('race').mean()["write"].loc[2] - \
+      2     hsb2.groupby('race').mean()["write"].loc[1]
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 159
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[37], line 1
+----> 1 mod = ols("write ~ C(race, Helmert)", data=hsb2)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 159
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[38], line 1
+----> 1 res = mod.fit()
+
+NameError: name 'mod' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 159
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[39], line 1
+----> 1 print(res.summary())
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 166
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[40], line 1
+----> 1 grouped = hsb2.groupby('race')
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 166
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[41], line 1
+----> 1 grouped.mean()["write"].loc[4] - grouped.mean()["write"].loc[:3].mean()
+
+NameError: name 'grouped' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 176
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[43], line 1
+----> 1 1./k * (grouped.mean()["write"].loc[k] - grouped.mean()["write"].loc[:k-1].mean())
+
+NameError: name 'grouped' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 176
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[45], line 1
+----> 1 1./k * (grouped.mean()["write"].loc[k] - grouped.mean()["write"].loc[:k-1].mean())
+
+NameError: name 'grouped' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 191
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[46], line 1
+----> 1 _, bins = np.histogram(hsb2.read, 3)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 191
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[47], line 2
+      1 try: # requires numpy main
+----> 2     readcat = np.digitize(hsb2.read, bins, True)
+      3 except:
 
+NameError: name 'hsb2' is not defined
 
+During handling of the above exception, another exception occurred:
+
+NameError                                 Traceback (most recent call last)
+Cell In[47], line 4
+      2     readcat = np.digitize(hsb2.read, bins, True)
+      3 except:
+----> 4     readcat = np.digitize(hsb2.read, bins)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 191
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[48], line 1
+----> 1 hsb2['readcat'] = readcat
+
+NameError: name 'readcat' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 191
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[49], line 1
+----> 1 hsb2.groupby('readcat').mean()['write']
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 202
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[51], line 1
+----> 1 levels = hsb2.readcat.unique().tolist()
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 202
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[54], line 1
+----> 1 mod = ols("write ~ C(readcat, Poly)", data=hsb2)
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 202
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[55], line 1
+----> 1 res = mod.fit()
+
+NameError: name 'mod' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 202
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[56], line 1
+----> 1 print(res.summary())
+
+NameError: name 'res' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/contrasts.rst at block ending on line 237
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[60], line 1
+----> 1 mod = ols("write ~ C(race, Simple)", data=hsb2)
+      2 res = mod.fit()
+      3 print(res.summary())
+
+NameError: name 'hsb2' is not defined
+
+<<<-------------------------------------------------------------------------
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.GenericLikelihoodModelResults.aic:1: WARNING: duplicate object description of statsmodels.base.model.GenericLikelihoodModelResults.aic, other instance in dev/generated/statsmodels.base.model.GenericLikelihoodModelResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.GenericLikelihoodModelResults.bic:1: WARNING: duplicate object description of statsmodels.base.model.GenericLikelihoodModelResults.bic, other instance in dev/generated/statsmodels.base.model.GenericLikelihoodModelResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.GenericLikelihoodModelResults.bse:1: WARNING: duplicate object description of statsmodels.base.model.GenericLikelihoodModelResults.bse, other instance in dev/generated/statsmodels.base.model.GenericLikelihoodModelResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.GenericLikelihoodModelResults.llf:1: WARNING: duplicate object description of statsmodels.base.model.GenericLikelihoodModelResults.llf, other instance in dev/generated/statsmodels.base.model.GenericLikelihoodModelResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.LikelihoodModelResults.tvalues:1: WARNING: duplicate object description of statsmodels.base.model.LikelihoodModelResults.tvalues, other instance in dev/generated/statsmodels.base.model.LikelihoodModelResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.Model.endog_names:1: WARNING: duplicate object description of statsmodels.base.model.Model.endog_names, other instance in dev/generated/statsmodels.base.model.Model, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.Model.exog_names:1: WARNING: duplicate object description of statsmodels.base.model.Model.exog_names, other instance in dev/generated/statsmodels.base.model.Model, use :no-index: for one of them
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
 ---------------------------------------------------------------------------
 ConnectionRefusedError                    Traceback (most recent call last)
 File /usr/lib/python3.13/urllib/request.py:1319, in AbstractHTTPHandler.do_open(self, http_class, req, **http_conn_args)
@@ -12124,76 +22227,30 @@
 During handling of the above exception, another exception occurred:
 
 URLError                                  Traceback (most recent call last)
-Cell In[10], line 2
-      1 url = "https://raw.githubusercontent.com/tidyverts/tsibbledata/master/data-raw/vic_elec/VIC2015/demand.csv"
-----> 2 df = pd.read_csv(url)
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
-   1013 kwds_defaults = _refine_defaults_read(
-   1014     dialect,
-   1015     delimiter,
-   (...)
-   1022     dtype_backend=dtype_backend,
-   1023 )
-   1024 kwds.update(kwds_defaults)
--> 1026 return _read(filepath_or_buffer, kwds)
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
-    617 _validate_names(kwds.get("names", None))
-    619 # Create the parser.
---> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
-    622 if chunksize or iterator:
-    623     return parser
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
-   1617     self.options["has_index_names"] = kwds["has_index_names"]
-   1619 self.handles: IOHandles | None = None
--> 1620 self._engine = self._make_engine(f, self.engine)
-
-File /usr/lib/python3/dist-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
-   1878     if "b" not in mode:
-   1879         mode += "b"
--> 1880 self.handles = get_handle(
-   1881     f,
-   1882     mode,
-   1883     encoding=self.options.get("encoding", None),
-   1884     compression=self.options.get("compression", None),
-   1885     memory_map=self.options.get("memory_map", False),
-   1886     is_text=is_text,
-   1887     errors=self.options.get("encoding_errors", "strict"),
-   1888     storage_options=self.options.get("storage_options", None),
-   1889 )
-   1890 assert self.handles is not None
-   1891 f = self.handles.handle
-
-File /usr/lib/python3/dist-packages/pandas/io/common.py:728, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
-    725     codecs.lookup_error(errors)
-    727 # open URLs
---> 728 ioargs = _get_filepath_or_buffer(
-    729     path_or_buf,
-    730     encoding=encoding,
-    731     compression=compression,
-    732     mode=mode,
-    733     storage_options=storage_options,
-    734 )
-    736 handle = ioargs.filepath_or_buffer
-    737 handles: list[BaseBuffer]
-
-File /usr/lib/python3/dist-packages/pandas/io/common.py:384, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
-    382 # assuming storage_options is to be interpreted as headers
-    383 req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
---> 384 with urlopen(req_info) as req:
-    385     content_encoding = req.headers.get("Content-Encoding", None)
-    386     if content_encoding == "gzip":
-    387         # Override compression based on Content-Encoding header
+Cell In[2], line 1
+----> 1 data = sm.datasets.get_rdataset("flchain", "survival", cache=True).data
 
-File /usr/lib/python3/dist-packages/pandas/io/common.py:289, in urlopen(*args, **kwargs)
-    283 """
-    284 Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
-    285 the stdlib.
-    286 """
-    287 import urllib.request
---> 289 return urllib.request.urlopen(*args, **kwargs)
+File /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/datasets/utils.py:237, in get_rdataset(dataname, package, cache)
+    234 docs_base_url = ("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/"
+    235                  "master/doc/"+package+"/rst/")
+    236 cache = _get_cache(cache)
+--> 237 data, from_cache = _get_data(data_base_url, dataname, cache)
+    238 data = read_csv(data, index_col=0)
+    239 data = _maybe_reset_index(data)
+
+File /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/datasets/utils.py:166, in _get_data(base_url, dataname, cache, extension)
+    164 url = base_url + (dataname + ".%s") % extension
+    165 try:
+--> 166     data, from_cache = _urlopen_cached(url, cache)
+    167 except HTTPError as err:
+    168     if '404' in str(err):
+
+File /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/datasets/utils.py:157, in _urlopen_cached(url, cache)
+    155 # not using the cache or did not find it in cache
+    156 if not from_cache:
+--> 157     data = urlopen(url, timeout=3).read()
+    158     if cache is not None:  # then put it in the cache
+    159         _cache_it(data, cache_path)
 
 File /usr/lib/python3.13/urllib/request.py:189, in urlopen(url, data, timeout, context)
     187 else:
@@ -12237,91 +22294,26462 @@
 
 URLError: <urlopen error [Errno 111] Connection refused>
 
-******************************************************************************
-
-
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[3], line 1
+----> 1 df = data.loc[data.sex == "F", :]
 
-******************************************************************************
-ERROR: Error occurred when running /build/reproducible-path/statsmodels-0.14.5+dfsg/examples/notebooks/statespace_tvpvar_mcmc_cfa.ipynb
-An error occurred while executing the following cell:
-------------------
-import arviz as az
+AttributeError: 'Dataset' object has no attribute 'loc'
 
-# Collect the observation error covariance parameters
-az_obs_cov = az.convert_to_inference_data({
-    ('Var[%s]' % mod.endog_names[i] if i == j else
-     'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
-    store_obs_cov[nburn + 1:, i, j]
-    for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+KeyError                                  Traceback (most recent call last)
+File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
+   3811 try:
+-> 3812     return self._engine.get_loc(casted_key)
+   3813 except KeyError as err:
 
-# Plot the credible intervals
-az.plot_forest(az_obs_cov, figsize=(8, 7));
-------------------
+File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()
 
+File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()
 
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[16], line 1
-----> 1 import arviz as az
-      3 # Collect the observation error covariance parameters
-      4 az_obs_cov = az.convert_to_inference_data({
-      5     ('Var[%s]' % mod.endog_names[i] if i == j else
-      6      'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
-      7     store_obs_cov[nburn + 1:, i, j]
-      8     for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+File pandas/_libs/hashtable_class_helper.pxi:7088, in pandas._libs.hashtable.PyObjectHashTable.get_item()
 
-ModuleNotFoundError: No module named 'arviz'
+File pandas/_libs/hashtable_class_helper.pxi:7096, in pandas._libs.hashtable.PyObjectHashTable.get_item()
 
-An error occurred while executing the following cell:
-------------------
-import arviz as az
+KeyError: 'futime'
 
-# Collect the observation error covariance parameters
-az_obs_cov = az.convert_to_inference_data({
-    ('Var[%s]' % mod.endog_names[i] if i == j else
-     'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
-    store_obs_cov[nburn + 1:, i, j]
-    for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+The above exception was the direct cause of the following exception:
 
-# Plot the credible intervals
-az.plot_forest(az_obs_cov, figsize=(8, 7));
-------------------
+KeyError                                  Traceback (most recent call last)
+Cell In[4], line 1
+----> 1 sf = sm.SurvfuncRight(df["futime"], df["death"])
+
+File /usr/lib/python3/dist-packages/pandas/core/frame.py:4107, in DataFrame.__getitem__(self, key)
+   4105 if self.columns.nlevels > 1:
+   4106     return self._getitem_multilevel(key)
+-> 4107 indexer = self.columns.get_loc(key)
+   4108 if is_integer(indexer):
+   4109     indexer = [indexer]
+
+File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:3819, in Index.get_loc(self, key)
+   3814     if isinstance(casted_key, slice) or (
+   3815         isinstance(casted_key, abc.Iterable)
+   3816         and any(isinstance(x, slice) for x in casted_key)
+   3817     ):
+   3818         raise InvalidIndexError(key)
+-> 3819     raise KeyError(key) from err
+   3820 except TypeError:
+   3821     # If we have a listlike key, _check_indexing_error will raise
+   3822     #  InvalidIndexError. Otherwise we fall through and re-raise
+   3823     #  the TypeError.
+   3824     self._check_indexing_error(key)
+
+KeyError: 'futime'
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[5], line 1
+----> 1 sf.summary().head()
+
+NameError: name 'sf' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[6], line 1
+----> 1 sf.quantile(0.25)
+
+NameError: name 'sf' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[7], line 1
+----> 1 sf.quantile_ci(0.25)
+
+NameError: name 'sf' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[8], line 1
+----> 1 sf.plot()
+
+NameError: name 'sf' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[9], line 1
+----> 1 fig = sf.plot()
+
+NameError: name 'sf' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+Cell In[10], line 1
+----> 1 ax = fig.get_axes()[0]
+
+IndexError: list index out of range
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[11], line 1
+----> 1 pt = ax.get_lines()[1]
+
+NameError: name 'ax' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[12], line 1
+----> 1 pt.set_visible(False)
+
+NameError: name 'pt' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[13], line 1
+----> 1 fig = sf.plot()
+
+NameError: name 'sf' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[14], line 1
+----> 1 lcb, ucb = sf.simultaneous_cb()
 
+NameError: name 'sf' is not defined
 
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
 ---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
+IndexError                                Traceback (most recent call last)
+Cell In[15], line 1
+----> 1 ax = fig.get_axes()[0]
+
+IndexError: list index out of range
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
 Cell In[16], line 1
-----> 1 import arviz as az
-      3 # Collect the observation error covariance parameters
-      4 az_obs_cov = az.convert_to_inference_data({
-      5     ('Var[%s]' % mod.endog_names[i] if i == j else
-      6      'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):
-      7     store_obs_cov[nburn + 1:, i, j]
-      8     for i in range(mod.k_endog) for j in range(i, mod.k_endog)})
+----> 1 ax.fill_between(sf.surv_times, lcb, ucb, color='lightgrey')
 
-ModuleNotFoundError: No module named 'arviz'
+NameError: name 'ax' is not defined
 
-******************************************************************************
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[17], line 1
+----> 1 ax.set_xlim(365, 365*10)
+
+NameError: name 'ax' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[18], line 1
+----> 1 ax.set_ylim(0.7, 1)
+
+NameError: name 'ax' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[19], line 1
+----> 1 ax.set_ylabel("Proportion alive")
 
+NameError: name 'ax' is not defined
 
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/linear_regression_diagnostics_plots.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/regression_diagnostics.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_autoregression.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/mediation_survival.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/generic_mle.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/pca_fertility_factors.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/formulas.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/plots_boxplots.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/stl_decomposition.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/rolling_ls.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/wls.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_sarimax_stata.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/markov_regression.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/count_hurdle.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/glm_formula.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_concentrated_scale.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/statespace_structural_harvey_jaeger.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/quantile_regression.ipynb
-Finished /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/tsa_arma_1.ipynb
-Wed Sep 24 06:45:52 UTC 2025 - pbuilder was killed by timeout after 18h.
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[20], line 1
+----> 1 ax.set_xlabel("Days since enrollment")
+
+NameError: name 'ax' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[22], line 1
+----> 1 gb = data.groupby("sex")
+
+AttributeError: 'Dataset' object has no attribute 'groupby'
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Cell In[25], line 1
+----> 1 for g in gb:
+      2     sexes.append(g[0])
+      3     sf = sm.SurvfuncRight(g[1]["futime"], g[1]["death"])
+
+NameError: name 'gb' is not defined
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+Cell In[27], line 1
+----> 1 li[1].set_visible(False)
+
+IndexError: list index out of range
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+Cell In[28], line 1
+----> 1 li[3].set_visible(False)
+
+IndexError: list index out of range
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+IndexError                                Traceback (most recent call last)
+Cell In[29], line 1
+----> 1 plt.figlegend((li[0], li[2]), sexes, loc="center right")
+
+IndexError: list index out of range
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[33], line 1
+----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex)
+
+AttributeError: 'Dataset' object has no attribute 'futime'
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[34], line 1
+----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='fh', fh_p=1)
+
+AttributeError: 'Dataset' object has no attribute 'futime'
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[35], line 1
+----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='gb')
+
+AttributeError: 'Dataset' object has no attribute 'futime'
+
+<<<-------------------------------------------------------------------------
+WARNING: 
+>>>-------------------------------------------------------------------------
+Exception in /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/duration.rst at block ending on line None
+Specify :okexcept: as an option in the ipython:: block to suppress this message
+---------------------------------------------------------------------------
+AttributeError                            Traceback (most recent call last)
+Cell In[36], line 1
+----> 1 stat, pv = sm.duration.survdiff(data.futime, data.death, data.sex, weight_type='tw')
+
+AttributeError: 'Dataset' object has no attribute 'futime'
+
+<<<-------------------------------------------------------------------------
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/formulas.ipynb:211: WARNING: File not found: 'examples/notebooks/generated/regression_diagnostics.html' [nbsphinx.localfile]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/examples/notebooks/generated/formulas.ipynb:564: WARNING: File not found: 'examples/notebooks/generated/contrasts.html' [nbsphinx.localfile]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/count_model.py:docstring of statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.llf, other instance in generated/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/count_model.py:docstring of statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.llf, other instance in generated/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/count_model.py:docstring of statsmodels.discrete.count_model.ZeroInflatedPoissonResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.count_model.ZeroInflatedPoissonResults.llf, other instance in generated/statsmodels.discrete.count_model.ZeroInflatedPoissonResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.BinaryResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.BinaryResults.llf, other instance in generated/statsmodels.discrete.discrete_model.BinaryResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.CountResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.CountResults.llf, other instance in generated/statsmodels.discrete.discrete_model.CountResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.DiscreteResults.llf, other instance in generated/statsmodels.discrete.discrete_model.DiscreteResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.GeneralizedPoissonResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.GeneralizedPoissonResults.llf, other instance in generated/statsmodels.discrete.discrete_model.GeneralizedPoissonResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.LogitResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.LogitResults.llf, other instance in generated/statsmodels.discrete.discrete_model.LogitResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.MultinomialResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.MultinomialResults.llf, other instance in generated/statsmodels.discrete.discrete_model.MultinomialResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.NegativeBinomialResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.NegativeBinomialResults.llf, other instance in generated/statsmodels.discrete.discrete_model.NegativeBinomialResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.ProbitResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.discrete_model.ProbitResults.llf, other instance in generated/statsmodels.discrete.discrete_model.ProbitResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/truncated_model.py:docstring of statsmodels.discrete.truncated_model.HurdleCountResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.truncated_model.HurdleCountResults.llf, other instance in generated/statsmodels.discrete.truncated_model.HurdleCountResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/truncated_model.py:docstring of statsmodels.discrete.truncated_model.TruncatedLFPoissonResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.truncated_model.TruncatedLFPoissonResults.llf, other instance in generated/statsmodels.discrete.truncated_model.TruncatedLFPoissonResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/truncated_model.py:docstring of statsmodels.discrete.truncated_model.TruncatedNegativeBinomialResults.llf:1: WARNING: duplicate object description of statsmodels.discrete.truncated_model.TruncatedNegativeBinomialResults.llf, other instance in generated/statsmodels.discrete.truncated_model.TruncatedNegativeBinomialResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/distributions/copula/elliptical.py:docstring of statsmodels.distributions.copula.elliptical.GaussianCopula:46: WARNING: Footnote [1] is not referenced. [ref.footnote]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/distributions/copula/elliptical.py:docstring of statsmodels.distributions.copula.elliptical.StudentTCopula:24: WARNING: Footnote [1] is not referenced. [ref.footnote]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/duration/hazard_regression.py:docstring of statsmodels.duration.hazard_regression.PHRegResults.bse:1: WARNING: duplicate object description of statsmodels.duration.hazard_regression.PHRegResults.bse, other instance in generated/statsmodels.duration.hazard_regression.PHRegResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.LikelihoodModelResults.normalized_cov_params:1: WARNING: duplicate object description of statsmodels.duration.hazard_regression.PHRegResults.normalized_cov_params, other instance in generated/statsmodels.duration.hazard_regression.PHRegResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/gam/generalized_additive_model.py:docstring of statsmodels.gam.generalized_additive_model.GLMGamResults.cv:1: WARNING: duplicate object description of statsmodels.gam.generalized_additive_model.GLMGamResults.cv, other instance in generated/statsmodels.gam.generalized_additive_model.GLMGamResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/gam/generalized_additive_model.py:docstring of statsmodels.gam.generalized_additive_model.GLMGamResults.edf:1: WARNING: duplicate object description of statsmodels.gam.generalized_additive_model.GLMGamResults.edf, other instance in generated/statsmodels.gam.generalized_additive_model.GLMGamResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/gam/generalized_additive_model.py:docstring of statsmodels.gam.generalized_additive_model.GLMGamResults.gcv:1: WARNING: duplicate object description of statsmodels.gam.generalized_additive_model.GLMGamResults.gcv, other instance in generated/statsmodels.gam.generalized_additive_model.GLMGamResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/gam/generalized_additive_model.py:docstring of statsmodels.gam.generalized_additive_model.GLMGamResults.hat_matrix_diag:1: WARNING: duplicate object description of statsmodels.gam.generalized_additive_model.GLMGamResults.hat_matrix_diag, other instance in generated/statsmodels.gam.generalized_additive_model.GLMGamResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:docstring of statsmodels.genmod.generalized_linear_model.GLMResults.plot_ceres_residuals:47: WARNING: Footnote [1] is not referenced. [ref.footnote]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:docstring of statsmodels.genmod.generalized_linear_model.GLMResults.plot_ceres_residuals:51: WARNING: Footnote [2] is not referenced. [ref.footnote]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/families/family.py:docstring of statsmodels.genmod.families.family.Binomial.link:1: WARNING: duplicate object description of statsmodels.genmod.families.family.Binomial.link, other instance in generated/statsmodels.genmod.families.family.Binomial, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/families/family.py:docstring of statsmodels.genmod.families.family.Binomial.variance:1: WARNING: duplicate object description of statsmodels.genmod.families.family.Binomial.variance, other instance in generated/statsmodels.genmod.families.family.Binomial, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/svar_model.py:docstring of statsmodels.tsa.vector_ar.svar_model.SVARResults:139: WARNING: duplicate object description of statsmodels.tsa.vector_ar.svar_model.SVARResults.params, other instance in generated/statsmodels.tsa.vector_ar.svar_model.SVARResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/svar_model.py:docstring of statsmodels.tsa.vector_ar.svar_model.SVARResults.aic:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.svar_model.SVARResults.aic, other instance in generated/statsmodels.tsa.vector_ar.svar_model.SVARResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/svar_model.py:docstring of statsmodels.tsa.vector_ar.svar_model.SVARResults.bse:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.svar_model.SVARResults.bse, other instance in generated/statsmodels.tsa.vector_ar.svar_model.SVARResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/svar_model.py:docstring of statsmodels.tsa.vector_ar.svar_model.SVARResults.detomega:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.svar_model.SVARResults.detomega, other instance in generated/statsmodels.tsa.vector_ar.svar_model.SVARResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/svar_model.py:docstring of statsmodels.tsa.vector_ar.svar_model.SVARResults.df_resid:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.svar_model.SVARResults.df_resid, other instance in generated/statsmodels.tsa.vector_ar.svar_model.SVARResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/svar_model.py:docstring of statsmodels.tsa.vector_ar.svar_model.SVARResults.fittedvalues:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.svar_model.SVARResults.fittedvalues, other instance in generated/statsmodels.tsa.vector_ar.svar_model.SVARResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/var_model.py:docstring of statsmodels.tsa.vector_ar.var_model.VARResults.irf_resim:21: WARNING: Footnote [*] is not referenced. [ref.footnote]
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.cov_params_wo_det:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.cov_params_wo_det, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.cov_var_repr:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.cov_var_repr, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.fittedvalues:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.fittedvalues, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_alpha:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_alpha, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_beta:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_beta, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_det_coef_coint:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_det_coef_coint, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_gamma:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.pvalues_gamma, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.resid:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.resid, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_alpha:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_alpha, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_beta:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_beta, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_coint:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_coint, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_det_coef:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_det_coef, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_det_coef_coint:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_det_coef_coint, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_gamma:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_gamma, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_params:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.stderr_params, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_alpha:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_alpha, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_beta:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_beta, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_det_coef:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_det_coef, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_det_coef_coint:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_det_coef_coint, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_gamma:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_gamma, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.VECMResults.var_rep:1: WARNING: duplicate object description of statsmodels.tsa.vector_ar.vecm.VECMResults.var_rep, other instance in generated/statsmodels.tsa.vector_ar.vecm.VECMResults, use :no-index: for one of them
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/vecm.py:docstring of statsmodels.tsa.vector_ar.vecm.coint_johansen:29: WARNING: Footnote [1] is not referenced. [ref.footnote]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/glm.rst:6: WARNING: Duplicate explicit target name: "glm". [docutils]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/glm.rst:144: WARNING: duplicate object description of statsmodels.genmod.families.family, other instance in gee, use :no-index: for one of them
+looking for now-outdated files... none found
+pickling environment... done
+checking consistency... /build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.Binomial.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.Binomial
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.Family.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.Family
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.Gamma.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.Gamma
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.Gaussian.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.Gaussian
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.InverseGaussian.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.InverseGaussian
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.NegativeBinomial.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.NegativeBinomial
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.Poisson.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.Poisson
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.family.Tweedie.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.family.Tweedie
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.CDFLink.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.CDFLink
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.CLogLog.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.CLogLog
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Cauchy.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Cauchy
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Identity.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Identity
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.InversePower.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.InversePower
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.InverseSquared.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.InverseSquared
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Link.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Link
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Log.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Log
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Logit.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Logit
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.NegativeBinomial.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.NegativeBinomial
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Power.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Power
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.genmod.families.links.Probit.rst: document is referenced in multiple toctrees: ['gee', 'glm'], selecting: glm <- generated/statsmodels.genmod.families.links.Probit
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.stats.knockoff_regeffects.OLSEffects.rst: document is referenced in multiple toctrees: ['stats', 'stats'], selecting: stats <- generated/statsmodels.stats.knockoff_regeffects.OLSEffects
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.stats.proportion.proportion_effectsize.rst: document is referenced in multiple toctrees: ['stats', 'stats'], selecting: stats <- generated/statsmodels.stats.proportion.proportion_effectsize
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.rst: document is referenced in multiple toctrees: ['statespace', 'tsa'], selecting: tsa <- generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.rst: document is referenced in multiple toctrees: ['statespace', 'tsa'], selecting: tsa <- generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.hypothesis_test_results.HypothesisTestResults.rst: document is referenced in multiple toctrees: ['vector_ar', 'vector_ar'], selecting: vector_ar <- generated/statsmodels.tsa.vector_ar.hypothesis_test_results.HypothesisTestResults
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.hypothesis_test_results.NormalityTestResults.rst: document is referenced in multiple toctrees: ['vector_ar', 'vector_ar'], selecting: vector_ar <- generated/statsmodels.tsa.vector_ar.hypothesis_test_results.NormalityTestResults
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.tsa.vector_ar.hypothesis_test_results.WhitenessTestResults.rst: document is referenced in multiple toctrees: ['vector_ar', 'vector_ar'], selecting: vector_ar <- generated/statsmodels.tsa.vector_ar.hypothesis_test_results.WhitenessTestResults
+done
+preparing documents... WARNING: unsupported theme option 'icon' given
+WARNING: unsupported theme option 'site_url' given
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+WARNING: unsupported theme option 'toc_title_is_page_title' given
+WARNING: unsupported theme option 'social' given
+done
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+writing output... [ 63%] generated/statsmodels.stats.outliers_influence.MLEInfluence
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+writing output... [ 63%] generated/statsmodels.stats.outliers_influence.OLSInfluence
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+writing output... [ 63%] generated/statsmodels.stats.outliers_influence.OLSInfluence.influence
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+writing output... [ 63%] generated/statsmodels.stats.power.FTestAnovaPower.power
+writing output... [ 63%] generated/statsmodels.stats.power.FTestAnovaPower.solve_power
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+writing output... [ 63%] generated/statsmodels.stats.power.FTestPower.plot_power
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+writing output... [ 63%] generated/statsmodels.stats.power.FTestPower.solve_power
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+writing output... [ 63%] generated/statsmodels.stats.power.GofChisquarePower.plot_power
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+writing output... [ 63%] generated/statsmodels.stats.power.GofChisquarePower.solve_power
+writing output... [ 63%] generated/statsmodels.stats.power.NormalIndPower
+writing output... [ 63%] generated/statsmodels.stats.power.NormalIndPower.plot_power
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+writing output... [ 63%] generated/statsmodels.stats.power.NormalIndPower.solve_power
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+writing output... [ 63%] generated/statsmodels.stats.power.TTestIndPower.plot_power
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+writing output... [ 63%] generated/statsmodels.stats.power.TTestIndPower.solve_power
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+writing output... [ 64%] generated/statsmodels.stats.power.TTestPower.solve_power
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+writing output... [ 64%] generated/statsmodels.stats.power.zt_ind_solve_power
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+writing output... [ 64%] generated/statsmodels.stats.proportion.power_binom_tost
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+writing output... [ 64%] generated/statsmodels.stats.proportion.score_test_proportions_2indep
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+writing output... [ 64%] generated/statsmodels.stats.rates.confint_quantile_poisson
+writing output... [ 64%] generated/statsmodels.stats.rates.etest_poisson_2indep
+writing output... [ 64%] generated/statsmodels.stats.rates.nonequivalence_poisson_2indep
+writing output... [ 64%] generated/statsmodels.stats.rates.power_equivalence_neginb_2indep
+writing output... [ 64%] generated/statsmodels.stats.rates.power_equivalence_poisson_2indep
+writing output... [ 64%] generated/statsmodels.stats.rates.power_negbin_ratio_2indep
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+writing output... [ 64%] generated/statsmodels.stats.rates.tost_poisson_2indep
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+writing output... [ 64%] generated/statsmodels.stats.robust_compare.scale_transform
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+writing output... [ 64%] generated/statsmodels.stats.sandwich_covariance.cov_cluster_2groups
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+writing output... [ 65%] generated/statsmodels.stats.sandwich_covariance.cov_hc2
+writing output... [ 65%] generated/statsmodels.stats.sandwich_covariance.cov_hc3
+writing output... [ 65%] generated/statsmodels.stats.sandwich_covariance.cov_nw_groupsum
+writing output... [ 65%] generated/statsmodels.stats.sandwich_covariance.cov_nw_panel
+writing output... [ 65%] generated/statsmodels.stats.sandwich_covariance.cov_white_simple
+writing output... [ 65%] generated/statsmodels.stats.sandwich_covariance.se_cov
+writing output... [ 65%] generated/statsmodels.stats.stattools.durbin_watson
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+writing output... [ 65%] generated/statsmodels.stats.weightstats.CompareMeans.dof_satt
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+writing output... [ 65%] generated/statsmodels.stats.weightstats.CompareMeans.summary
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+writing output... [ 65%] generated/statsmodels.stats.weightstats.DescrStatsW.ztest_mean
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+writing output... [ 65%] generated/statsmodels.stats.weightstats._tstat_generic
+writing output... [ 65%] generated/statsmodels.stats.weightstats._zconfint_generic
+writing output... [ 65%] generated/statsmodels.stats.weightstats._zstat_generic
+writing output... [ 65%] generated/statsmodels.stats.weightstats._zstat_generic2
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+writing output... [ 66%] generated/statsmodels.tools.numdiff.approx_hess2
+writing output... [ 66%] generated/statsmodels.tools.numdiff.approx_hess3
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+writing output... [ 79%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.aicc
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.plot_diagnostics
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.summary
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.t_test
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.test_heteroskedasticity
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.test_normality
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.test_serial_correlation
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.tvalues
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.use_t
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.wald_test
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.wald_test_terms
+writing output... [ 80%] generated/statsmodels.tsa.statespace.dynamic_factor_mq.DynamicFactorMQResults.zvalues
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.clone
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.endog_names
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.filter
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.fix_params
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.from_formula
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.handle_params
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.initialize
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.initialize_approximate_diffuse
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.initialize_known
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.initialize_statespace
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.initialize_stationary
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.k_params
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.loglike
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+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.loglikeobs
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.observed_information_matrix
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.opg_information_matrix
+writing output... [ 80%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.param_names
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.predict
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+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.set_filter_method
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.set_inversion_method
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.set_smoother_output
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.set_stability_method
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.simulate
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.simulation_smoother
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.smooth
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+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.state_names
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.tolerance
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.transform_jacobian
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.transform_params
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.untransform_params
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothing.update
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.aic
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.aicc
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.append
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.apply
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.bic
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.bse
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.conf_int
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.cov_params
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.cov_params_approx
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.cov_params_oim
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.cov_params_opg
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.cov_params_robust
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.cov_params_robust_approx
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+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.extend
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+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.fittedvalues
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.forecast
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+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.get_prediction
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.get_smoothed_decomposition
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.hqic
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.impulse_responses
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.info_criteria
+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.initialize
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+writing output... [ 81%] generated/statsmodels.tsa.statespace.exponential_smoothing.ExponentialSmoothingResults.news
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.clone
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.extend
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter_chandrasekhar
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter_collapsed
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter_conventional
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter_exact_initial
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter_timing
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter_unscented
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.initialize_components
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.initialize_known
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.initialize_stationary
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.inversion_method
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.inversion_methods
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.invert_lu
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.invert_univariate
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_no_filtered
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_no_filtered_cov
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_no_forecast_cov
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_no_gain
+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_no_likelihood
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+writing output... [ 82%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_no_smoothing
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+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_options
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.memory_store_all
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.obs
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+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.obs_intercept
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.prefix
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.selection
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.set_conserve_memory
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.set_filter_method
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.set_filter_timing
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.set_inversion_method
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.set_stability_method
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.simulate
+writing output... [ 83%] generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.solve_cholesky
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+writing output... [ 90%] generated/statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother.memory_no_forecast_cov
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+writing output... [ 90%] generated/statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother.memory_no_likelihood
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+writing output... [ 90%] generated/statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother.memory_no_predicted_cov
+writing output... [ 90%] generated/statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother.memory_no_predicted_mean
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+writing output... [ 91%] generated/statsmodels.tsa.statespace.structural.UnobservedComponents
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+writing output... [ 98%] generated/statsmodels.tsa.vector_ar.var_model.VARResults.test_whiteness
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+writing output... [ 98%] generated/statsmodels.tsa.vector_ar.vecm.CointRankResults
+writing output... [ 98%] generated/statsmodels.tsa.vector_ar.vecm.CointRankResults.summary
+writing output... [ 98%] generated/statsmodels.tsa.vector_ar.vecm.JohansenTestResult
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+writing output... [ 98%] generated/statsmodels.tsa.vector_ar.vecm.VECM
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+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.VECMResults.test_normality
+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.VECMResults.test_whiteness
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+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_beta
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+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.VECMResults.tvalues_gamma
+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.VECMResults.var_rep
+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.coint_johansen
+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.select_coint_rank
+writing output... [ 99%] generated/statsmodels.tsa.vector_ar.vecm.select_order
+writing output... [ 99%] generated/statsmodels.tsa.x13.x13_arima_analysis
+writing output... [ 99%] generated/statsmodels.tsa.x13.x13_arima_select_order
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+writing output... [ 99%] nonparametric
+writing output... [ 99%] optimization
+writing output... [ 99%] other_models
+writing output... [ 99%] pitfalls
+writing output... [ 99%] regression
+writing output... [ 99%] release/index
+writing output... [ 99%] release/old_changes
+writing output... [100%] release/version0.10
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+writing output... [100%] release/version0.13.0
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+writing output... [100%] user-guide
+writing output... [100%] vector_ar
+
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/git_notes.rst:213: WARNING: undefined label: 'pydagogue:actual-rebase' [ref.ref]
+WARNING: dot command 'dot' cannot be run (needed for graphviz output), check the graphviz_dot setting
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/maintainer_notes.rst:65: WARNING: undefined label: 'pydagogue:git-log-dots' [ref.ref]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/dev/maintainer_notes.rst:65: WARNING: undefined label: 'pydagogue:git-diff-dots' [ref.ref]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/count_model.py:docstring of statsmodels.discrete.count_model.ZeroInflatedResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.count_model.ZeroInflatedGeneralizedPoissonResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/count_model.py:docstring of statsmodels.discrete.count_model.ZeroInflatedResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.count_model.ZeroInflatedNegativeBinomialResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/count_model.py:docstring of statsmodels.discrete.count_model.ZeroInflatedResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.count_model.ZeroInflatedPoissonResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.count_model.ZeroInflatedPoissonResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.BinaryResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.BinaryResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.CountResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.CountResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.DiscreteResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.DiscreteResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.GeneralizedPoissonResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.GeneralizedPoissonResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.LogitResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.LogitResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.MultinomialResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.NegativeBinomialResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.NegativeBinomialResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.ProbitResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.discrete_model.ProbitResults.summary2.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
+/build/reproducible-path/statsmodels-0.14.5+dfsg/docs/source/generated/statsmodels.discrete.truncated_model.HurdleCountResults.summary.rst:4: WARNING: more than one target found for cross-reference 'Summary': statsmodels.iolib.summary.Summary, statsmodels.iolib.summary2.Summary [ref.python]
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:docstring of statsmodels.discrete.discrete_model.DiscreteResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
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+/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:docstring of statsmodels.base.model.GenericLikelihoodModelResults.get_prediction:: WARNING: more than one target found for cross-reference 'PredictionResults': statsmodels.regression.linear_model.PredictionResults, statsmodels.regression._prediction.PredictionResults, statsmodels.tsa.base.prediction.PredictionResults, statsmodels.tsa.statespace.kalman_filter.PredictionResults, statsmodels.tsa.statespace.mlemodel.PredictionResults [ref.python]
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+
+dumping search index in English (code: en)... done
+dumping object inventory... done
+build succeeded, 491 warnings.
+
+The HTML pages are in build/html.
+make[2]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg/docs'
+mv docs/build/* build/
+: # replace timestamps and build paths in examples output for reproducibility
+for html in `find build/html examples -name _modules -prune -o -name "*.html" -o -name "*.ipynb" -o -name "*.ipynb.txt"` ; do \
+    sed -i -e 's#/build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/[^/]*/build/statsmodels/#/usr/lib/python3/dist-packages/statsmodels/#g' \
+    -e 's# at 0x[0-9a-f]\{8,16\}\(&gt;\|>\)# at 0xadde5de1e8ed\1#g' \
+    -e 's#tmp/ipykernel_[0-9]\+#tmp/ipykernel_nnnnnnn#g' \
+    -e 's#^\s\+.\(iopub.execute_input\|iopub.status.busy\|iopub.status.idle\|shell.execute_reply\).:.*# #g' \
+    -e 's#\(Date:.*\)[A-Z][a-z]\+, \+[0-9]\+,\? \+[A-Z][a-z]\+,\? \+[0-9]\+#\1Sun, 10 Aug 2025#g' \
+    -e 's#\(Time:.*\)[0-9][0-9]:[0-9][0-9]:[0-9][0-9]#\113:13:47#g' ${html} ; \
+done
+sed: couldn't edit build/html/_modules: not a regular file
+rm -rf docs/source/generated/
+make[1]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+   debian/rules override_dh_auto_test
+make[1]: Entering directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+TEST_SUCCESS=true ; cd tools && for testpy3ver in `py3versions -vs` ; do \
+for testpath in ../.pybuild/*${testpy3ver}*/*/statsmodels ; do \
+PYTHONPATH=${testpath}/.. python${testpy3ver} -m pytest -v ${testpath} || TEST_SUCCESS=false ; \
+rm -rf ${testpath}/.pytest_cache ; \
+done ; done ; ${TEST_SUCCESS}
+============================= test session starts ==============================
+platform linux -- Python 3.13.7, pytest-8.4.2, pluggy-1.6.0 -- /usr/bin/python3.13
+cachedir: .pytest_cache
+rootdir: /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels
+configfile: setup.cfg
+plugins: typeguard-4.4.4, forked-1.6.0
+collecting ... collected 17988 items
+
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/tests/test_data.py::TestArrays::test_orig PASSED [  0%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/tests/test_data.py::TestArrays::test_endogexog PASSED [  0%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/tests/test_data.py::TestArrays::test_attach PASSED [  0%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/tests/test_data.py::TestArrays::test_names PASSED [  0%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct1-fam0] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct1-fam1] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct1-fam2] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct2-fam0] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct2-fam1] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_qif_fit[cov_struct2-fam2] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_formula[cov_struct0] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_formula[cov_struct1] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_qif.py::test_formula[cov_struct2] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTest::test_wald_score PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTest::test_dispersion PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestDispersed::test_wald_score PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestDispersed::test_dispersion PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoisson::test_dispersion PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoisson::test_wald_score PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoissonDispersed::test_dispersion PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestPoissonDispersed::test_wald_score PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/tests/test_score_test.py::TestScoreTestGaussian::test_wald_score PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_agreement.py::test_mean_diff_plot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_violinplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_violinplot_bw_factor PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_beanplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_beanplot_jitter PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_beanplot_side_right PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_beanplot_side_left PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_boxplots.py::test_beanplot_legend_text PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_correlation.py::test_plot_corr PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_correlation.py::test_plot_corr_grid PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_dotplot.py::test_all PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_both PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_rainbow PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_pandas[str] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plot_pandas[int] PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_formatting PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_formatting_errors PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_plottype PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_factorplots.py::TestInteractionPlot::test_recode_series PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_basic PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_basic_brute PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_plot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_alpha PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_multiple_alpha PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_threshold PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_bw PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_hdr_ncomp PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_banddepth_BD2 PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_banddepth_MBD PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_functional.py::test_fboxplot_rainbowplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_exceed PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_other_array XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_other_prbplt XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_qqplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_ppplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_probplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyNoFit::test_fit_params PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_exceed PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_other_array XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_other_prbplt XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_qqplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_ppplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_probplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotLongelyWithFit::test_fit_params PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_exceed PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_other_array XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_other_prbplt XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_qqplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_ppplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_probplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalMinimal::test_fit_params PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_exceed PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_other_array XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_other_prbplt XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_qqplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_ppplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_probplot_pltkwargs PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalWithFit::test_fit_params PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_exceed PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_other_array PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_other_array XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_other_prbplt PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_other_prbplt XFAIL [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_custom_labels PASSED [ 15%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_custom_labels PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_qqplot_pltkwargs PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_ppplot_pltkwargs PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_probplot_pltkwargs PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_fit_params PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_loc_set PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_scale_set PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalFullDist::test_exceptions PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestCompareSamplesDifferentSize::test_qqplot PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestCompareSamplesDifferentSize::test_ppplot PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_qqplot PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_ppplot PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot PASSED [ 16%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/graphics/tests/test_gofplots.py::TestProbPlotRandomNormalLocScaleDist::test_probplot_exceed PASSED [ 16%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_gls_and_weights PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_wide PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_projection PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_replace_missing PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_rsquare PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_equivalence PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::TestPCA::test_equivalence_full_matrices PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::test_missing PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::test_too_many_missing PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/multivariate/tests/test_pca.py::test_gls_warning PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case0] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case1] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case2] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case3] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case4] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case5] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case6] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels[case7] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case0] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case1] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case2] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case3] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case4] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case5] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case6] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_vectorized[case7] PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case3] PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case5] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case6] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsRplus::test_kernels_weights[case7] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels[case0] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels[case1] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_asymmetric.py::TestKernelsUnit::test_kernels_vectorized[case0] PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussPandas::test_cdf_gridded PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDETriangular::test_density PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDETriangular::test_evaluate PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEBiweight::test_evaluate PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKdeWeights::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussFFT::test_density PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEGaussFFT::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_density XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWGauss::test_compare PASSED [ 18%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_density XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_compare PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWEpa::test_kernel_constants PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_density XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_compare PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWTri::test_kernel_constants PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_density XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_compare PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWBiw::test_kernel_constants PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_density XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_evaluate SKIPPED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_compare PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos::test_kernel_constants PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_density XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_evaluate PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_compare PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDEWCos2::test_kernel_constants PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestNormConstant::test_norm_constant_calculation PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::test_kde_bw_positive PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::test_fit_self PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_custom_bandwidth PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_standard_custom_bandwidth PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_float_bandwidth[True] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kde.py::TestKDECustomBandwidth::test_check_is_fit_ok_with_float_bandwidth[False] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_pdf_non_fft PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_weighted_pdf_non_fft PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_all_samples_same_location_bw PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEUnivariate::test_int PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_mixeddata_CV_LS PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_mixeddata_LS_vs_ML PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_mixeddata_CV_ML PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_continuous PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_pdf_ordered PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_unordered_CV_LS PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_continuous_cdf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_mixeddata_cdf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_continuous_cvls_efficient PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_continuous_cvml_efficient PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_efficient_notrandom PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariate::test_efficient_user_specified_bw PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_mixeddata_CV_LS PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_CV_ML PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_unordered_CV_LS PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_pdf_continuous PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_pdf_mixeddata PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_normal_ref PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_cdf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_mixeddata_cdf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_continuous_cvml_efficient PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_efficient_user_specified_bw PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[biw] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[cos] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[epa] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[gau] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[tri] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[triw] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_density.py::test_all_kernels[uni] PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_ordered_lc_cvls PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_lc_cvls PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_ll_cvls PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuous_mfx_ll_cvls PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_mixed_mfx_ll_cvls PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_mfx_nonlinear_ll_cvls XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuous_cvls_efficient PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_censored_ll_cvls PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuous_lc_aic PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_significance_continuous PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_significance_discrete PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_user_specified_kernel PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_censored_user_specified_kernel PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_efficient_user_specificed_bw PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_censored_efficient_user_specificed_bw PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::test_invalid_bw PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernel_regression.py::test_invalid_kernel PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestEpan::test_smoothconf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestEpan::test_smoothconf_data PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestGau::test_smoothconf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestGau::test_smoothconf_data PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestUniform::test_smoothconf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestUniform::test_smoothconf_data PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestTriangular::test_smoothconf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestTriangular::test_smoothconf_data PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestCosine::test_smoothconf_data PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestCosine::test_smoothconf XFAIL [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestBiweight::test_smoothconf PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::TestBiweight::test_smoothconf_data PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_kernels.py::test_tricube PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_import PASSED [ 18%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_flat[False] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_flat[True] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_range PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_simple PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_iter_0 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_iter_0_3 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_frac_2_3 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_frac_1_5 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_delta_0 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_delta_rdef PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_delta_1 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_options PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_duplicate_xs PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_spike PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::TestLowess::test_exog_predict PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::test_returns_inputs PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/tests/test_lowess.py::test_xvals_dtype PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_income_coefficients PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_income_precision PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_methylation_coefficients PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_methylation_precision PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_precision_formula PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_scores PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaModel::test_results_other PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_basic PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_resid PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_oim PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaMeth::test_predict_distribution PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaIncome::test_score_test PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/othermod/tests/test_beta.py::TestBetaIncome::test_influence PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_cov.py::test_HC_use PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_dimred.py::test_poisson PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_dimred.py::test_sir_regularized_numdiff PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_dimred.py::test_sir_regularized_1d PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_dimred.py::test_sir_regularized_2d PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_dimred.py::test_covreduce PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_gretl.py::TestGLSARGretl::test_all PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_gretl.py::test_GLSARlag PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_params_table PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_predicted PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_rho PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_glsar_arima PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_glsar_stata.py::TestGLSARCorc::test_glsar_iter0 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[False-False-False] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[False-False-True] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[False-True-False] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[False-True-True] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[True-False-False] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[True-False-True] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[True-True-False] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_compare_numdiff[True-True-True] PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_default_re PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_history PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_profile_inference PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_vcomp_1 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_vcomp_2 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_vcomp_3 PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_sparse PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_dietox PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_dietox_slopes PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_pastes_vcomp PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_vcomp_formula PASSED [ 19%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/tests/test_lme.py::TestMixedLM::test_formulas PASSED [ 19%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_standarderrors PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_confidenceintervals SKIPPED [ 26%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_weights PASSED [ 26%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_bcov_scaled PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_tvalues PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::TestRlmSresid::test_tpvalues PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_missing PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_rlm_start_values PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_rlm_start_values_errors PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[AndrewWave] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[AndrewWave] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[LeastSquares] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[LeastSquares] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[HuberT] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[HuberT] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[TrimmedMean] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[TrimmedMean] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[TukeyBiweight] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[TukeyBiweight] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[Hampel] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[Hampel] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_fit[RamsayE] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_perfect_const[RamsayE] PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/tests/test_rlm.py::test_alt_criterion[weights] PASSED [ 26%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_2 PASSED [ 26%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/distributions/tests/test_multivariate.py::Test_MVN_MVT_prob::test_mvn_mvt_3 PASSED [ 26%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_use_t PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO::test_other XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_hypothesis PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_use_t PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Linear::test_other XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_hypothesis PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_use_t PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_other XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterNO_Nonlinear::test_score PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_hypothesis PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_use_t PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMStOneiterOLS_Linear::test_other XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestGMMSt2::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_hypothesis PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_hausman PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::TestIV2SLSSt1::test_input_dimensions PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::test_noconstant PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm.py::test_gmm_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddOnestep::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddOnestep::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddOnestep::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddTwostep::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddTwostep::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMAddTwostep::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultOnestep::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultOnestep::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultOnestep::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostep::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostep::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostep::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepDefault::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepDefault::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepDefault::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_basic PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_other PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_summary PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/regression/tests/test_gmm_poisson.py::TestGMMMultTwostepCenter::test_more PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/stats/tests/test_multicomp.py::test_tukey_pvalues PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/stats/tests/test_runs.py::test_mean_cutoff PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/stats/tests/test_runs.py::test_median_cutoff PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/stats/tests/test_runs.py::test_numeric_cutoff PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_predict PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_params PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_df XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestAdditiveModel::test_fitted PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_predict PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_fitted XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_params PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_df XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_mu PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMPoisson::test_prediction PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_predict PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_fitted XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_params PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_df XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_mu PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMBinomial::test_prediction PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_predict XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_fitted XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_params XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_df XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_mu XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_prediction XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_predict PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_fitted XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_params PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_df XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_mu PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMGamma::test_prediction PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_predict XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_params XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_mu XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_prediction XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_fitted XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_df XFAIL [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_pca.py::test_pca_princomp PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_pca.py::test_pca_svd PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_formula PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_lm_contrast PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_glm_formula_contrast PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_scb PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_glm_formula PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/tests/test_predict_functional.py::TestPredFunc::test_noformula_prediction PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_scalar PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_vector PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_invalid_parameters PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_handful_to_tbl PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_all_to_tbl SKIPPED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_handful_to_ch PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestQsturng::test_10000_to_ch PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_scalar PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_vector PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_v_equal_one PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_invalid_parameters PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_handful_to_known_values PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/libqsturng/tests/test_qsturng.py::TestPsturng::test_100_random_values PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnovaLM::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnovaLMNoconstant::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnovaLMCompare::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnovaLMCompareNoconstant::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova2::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova2Noconstant::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova2HC0::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova2HC1::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova2HC2::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova2HC3::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova3::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova3HC0::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova3HC1::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova3HC2::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova.py::TestAnova3HC3::test_results PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_single_factor_repeated_measures_anova PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_two_factors_repeated_measures_anova PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_three_factors_repeated_measures_anova PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_invalid_factor_name PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_collinearity PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_unbalanced_data PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregation PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregation_one_subject_duplicated PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregate_func PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregate_func_mean PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_anova_rm.py::test_repeated_measures_aggregate_compare_with_ezANOVA PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_base.py::test_holdertuple PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_base.py::test_holdertuple2 PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_homogeneity PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_SquareTable_from_data PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_SquareTable_nonsquare PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_cumulative_odds PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_local_odds PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_shifting PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_stratified_table_cube PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_resids PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_ordinal_association PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_chi2_association PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_symmetry PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_mcnemar PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_from_data_stratified PASSED [ 27%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_from_data_2x2 PASSED [ 28%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::test_cochranq PASSED [ 28%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_oddsratio_pooled PASSED [ 28%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_logodds_pooled PASSED [ 28%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_null_odds PASSED [ 28%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_oddsratio_pooled_confint PASSED [ 28%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_contingency_tables.py::TestStratified1::test_logodds_pooled_confint PASSED [ 28%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_confint_proportion_ndim[agresti_coull] PASSED [ 33%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_multinomial_proportions_errors PASSED [ 33%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::TestProportion::test_default_values PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::TestProportion::test_scalar PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_binom_test PASSED [ 33%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_score_test_2indep PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_test_2indep PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_equivalence_2indep PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_score_confint_koopman_nam PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_power_2indep PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count0] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count1] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count2] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count3] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count4] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count5] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count6] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count7] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count8] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count9] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count10] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count11] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count12] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count13] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count14] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-agresti_coull-count15] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count0] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count1] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count2] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count3] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count4] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count5] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count6] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count7] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count8] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count9] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count10] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count11] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count12] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count13] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count14] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-normal-count15] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count0] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count1] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count2] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count3] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count4] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count5] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count6] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count7] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count8] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count9] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count10] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count11] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count12] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count13] PASSED [ 33%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-beta-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-wilson-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-jeffreys-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[False-binom_test-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-agresti_coull-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-normal-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-beta-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-wilson-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-jeffreys-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count0] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count1] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count2] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count3] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count4] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count5] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count6] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count7] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count8] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count9] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count10] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count11] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count12] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count13] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count14] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry[True-binom_test-count15] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count0-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count0-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count1-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count1-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count2-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count2-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count3-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count3-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count4-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count4-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count5-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count5-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count6-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count6-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count7-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count7-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count8-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count8-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count9-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count9-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count10-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count10-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count11-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count11-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count12-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count12-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count13-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count13-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count14-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count14-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count15-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count15-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count16-47] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count16-50] PASSED [ 34%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count17-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count17-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count18-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count18-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count19-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count19-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count20-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count20-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count21-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count21-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count22-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count22-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count23-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count23-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count24-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count24-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count25-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count25-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count26-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count26-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count27-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count27-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count28-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count28-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count29-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count29-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count30-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count30-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count31-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count31-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count32-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count32-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count33-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count33-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count34-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count34-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count35-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count35-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count36-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count36-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count37-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count37-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count38-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count38-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count39-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count39-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count40-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count40-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count41-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count41-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count42-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count42-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count43-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count43-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count44-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count44-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count45-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count45-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count46-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count46-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count47-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[False-count47-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count0-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count0-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count1-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count1-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count2-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count2-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count3-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count3-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count4-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count4-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count5-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count5-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count6-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count6-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count7-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count7-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count8-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count8-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count9-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count9-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count10-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count10-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count11-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count11-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count12-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count12-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count13-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count13-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count14-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count14-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count15-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count15-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count16-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count16-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count17-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count17-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count18-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count18-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count19-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count19-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count20-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count20-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count21-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count21-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count22-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count22-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count23-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count23-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count24-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count24-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count25-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count25-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count26-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count26-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count27-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count27-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count28-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count28-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count29-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count29-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count30-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count30-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count31-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count31-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count32-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count32-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count33-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count33-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count34-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count34-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count35-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count35-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count36-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count36-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count37-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count37-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count38-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count38-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count39-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count39-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count40-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count40-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count41-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count41-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count42-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count42-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count43-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count43-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count44-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count44-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count45-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count45-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count46-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count46-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count47-47] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_binom_test[True-count47-50] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_int_check PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count0] PASSED [ 35%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count1] PASSED [ 35%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/tests/test_proportion.py::test_ci_symmetry_array[agresti_coull-count3] PASSED [ 35%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py::test_validate_fixed_params[ar_order8-3-fixed_params8-None] PASSED [ 47%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py::test_package_fixed_and_free_params_info[fixed_params1-spec_ar_lags1-spec_ma_lags1-expected_bunch1] PASSED [ 47%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py::test_stitch_fixed_and_free_params[fixed_lags0-free_lags0-fixed_params0-free_params0-spec_lags0-expected_all_params0] PASSED [ 47%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py::test_stitch_fixed_and_free_params[fixed_lags5-free_lags5-fixed_params5-free_params5-spec_lags5-expected_all_params5] PASSED [ 47%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py::test_stitch_fixed_and_free_params[fixed_lags6-free_lags6-fixed_params6-free_params6-spec_lags6-expected_all_params6] PASSED [ 47%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/estimators/tests/test_hannan_rissanen.py::test_stitch_fixed_and_free_params[fixed_lags7-free_lags7-fixed_params7-free_params7-spec_lags7-expected_all_params7] PASSED [ 47%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/tests/test_model.py::test_hannan_rissanen_with_fixed_params[1-1-fixed_params1] PASSED [ 48%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/tests/test_model.py::test_hannan_rissanen_with_fixed_params[2-3-fixed_params2] PASSED [ 48%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/tests/test_model.py::test_hannan_rissanen_with_fixed_params[ar_order3-0-fixed_params3] PASSED [ 48%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/arima/tests/test_model.py::test_hannan_rissanen_with_fixed_params[ar_order4-ma_order4-fixed_params4] PASSED [ 48%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_monthly[True-2-True] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_monthly[True-2-False] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_monthly[False-1-True] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_monthly[False-1-False] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_monthly[False-2-True] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_monthly[False-2-False] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_MQ[True] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py::test_standardized_MQ[False] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[111] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[112] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[11F] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[221] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[222] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[22F] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[block_111] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[block_112] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[block_221] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_known[block_222] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep1[11] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep1[22] PASSED [ 58%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep1[block_11] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep1[block_22] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[1-1-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[3-1-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[1-6-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[3-6-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[1-1-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[3-1-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[1-6-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[3-6-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[k_factors8-factor_orders8-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[k_factors9-factor_orders9-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[k_factors10-factor_orders10-factor_multiplicities10-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_missing[k_factors11-factor_orders11-factor_multiplicities11-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[1-1-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-1-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[1-6-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-factor_orders3-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[1-1-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-1-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[1-6-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[3-factor_orders7-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors8-factor_orders8-1-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors9-factor_orders9-1-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors10-factor_orders10-factor_multiplicities10-True] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_emstep_methods_nonmissing[k_factors11-factor_orders11-factor_multiplicities11-False] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_112] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_222] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_block_112] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py::test_news[news_block_222] PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_k_factor1 SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_k_factor1_factor_order_6 PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_k_factor2_factor_order_6 SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_two_blocks_factor_orders_6 SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestLocalLevelAnalytic::test_results PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestLocalLevelAnalyticDirect::test_results PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestLocalLinearTrendAnalytic::test_results PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestLocalLinearTrendAnalyticDirect::test_results PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestLocalLinearTrendAnalyticMissing::test_results PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_common_level_analytic PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_common_level_restricted_analytic PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts_error PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts_error_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_filtered_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_filtered_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_predicted_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_predicted_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_kalman_gain PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_loglike PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_autocov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_measurement_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothed_state_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_smoothing_error SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_forecasts_error_diffuse_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_predicted_diffuse_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_simulation_smoothed_state XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_nobs_diffuse PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_initialization PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_Approx::test_initialization_approx PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts_error PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts_error_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_filtered_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_filtered_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_predicted_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_predicted_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_kalman_gain PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_loglike PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_autocov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_measurement_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothed_state_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_smoothing_error SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_forecasts_error_diffuse_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_predicted_diffuse_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_simulation_smoothed_state XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_nobs_diffuse PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1_KFAS::test_initialization PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts_error PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts_error_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_filtered_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_filtered_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_predicted_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_predicted_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_kalman_gain PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_loglike PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_autocov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_measurement_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_state_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothing_error SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_forecasts_error_diffuse_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_predicted_diffuse_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_simulation_smoothed_state XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_nobs_diffuse PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_initialization PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_initialization_approx PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts_error PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts_error_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_filtered_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_filtered_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_predicted_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_predicted_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_kalman_gain PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_loglike PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_autocov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_measurement_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothed_state_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_smoothing_error SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_forecasts_error_diffuse_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_predicted_diffuse_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_simulation_smoothed_state XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_nobs_diffuse PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1MeasurementError_KFAS::test_initialization PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts_error PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts_error_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_filtered_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_filtered_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_predicted_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_predicted_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_kalman_gain PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_loglike PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_autocov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_measurement_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_disturbance PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_disturbance_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothing_error SKIPPED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_estimator PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_estimator_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_forecasts_error_diffuse_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_predicted_diffuse_state_cov PASSED [ 59%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_simulation_smoothed_state XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_initialization PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_nobs_diffuse PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_initialization_approx PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_Approx::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts_error PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_filtered_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_filtered_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_predicted_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_predicted_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_kalman_gain PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_loglike PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_autocov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_measurement_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothed_state_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_smoothing_error SKIPPED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts_error_diffuse_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_predicted_diffuse_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_simulation_smoothed_state XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_initialization PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_nobs_diffuse PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Missing_KFAS::test_forecasts_error_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts_error PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts_error_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_filtered_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_filtered_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_predicted_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_predicted_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_kalman_gain PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_loglike PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_autocov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_measurement_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothed_state_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_smoothing_error SKIPPED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_forecasts_error_diffuse_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_predicted_diffuse_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_simulation_smoothed_state XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_nobs_diffuse PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_initialization PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_Approx::test_initialization_approx PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts_error PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts_error_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_filtered_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_predicted_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_kalman_gain PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_loglike PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_autocov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_measurement_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothing_error SKIPPED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_forecasts_error_diffuse_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_predicted_diffuse_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_simulation_smoothed_state XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_nobs_diffuse PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_initialization PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_predicted_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_filtered_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestVAR1Mixed_KFAS::test_smoothed_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts_error PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts_error_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_filtered_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_filtered_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_predicted_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_predicted_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_kalman_gain PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_loglike PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_autocov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_measurement_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothed_state_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_smoothing_error SKIPPED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_forecasts_error_diffuse_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_predicted_diffuse_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_simulation_smoothed_state XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_nobs_diffuse PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_initialization PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_Approx::test_initialization_approx PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts_error PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts_error_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_filtered_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_predicted_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_kalman_gain PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_loglike PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_autocov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_measurement_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_measurement_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothing_error SKIPPED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_forecasts_error_diffuse_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_predicted_diffuse_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_simulation_smoothed_state XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_simulation_smoothed_measurement_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_simulation_smoothed_state_disturbance XFAIL [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_nobs_diffuse PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_initialization PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_predicted_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_filtered_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFM_KFAS::test_smoothed_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts_error PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts_error_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_filtered_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_filtered_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_predicted_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_predicted_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_kalman_gain PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_loglike PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_autocov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_measurement_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_measurement_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_disturbance PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothed_state_disturbance_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_smoothing_error SKIPPED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_forecasts_error_diffuse_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_predicted_diffuse_state_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_diffuse_estimator PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_diffuse1_estimator_cov PASSED [ 60%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_scaled_smoothed_diffuse2_estimator_cov PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_simulation_smoothed_state XFAIL [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_simulation_smoothed_measurement_disturbance XFAIL [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_simulation_smoothed_state_disturbance XFAIL [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_nobs_diffuse PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_initialization PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::TestDFMCollapsed_Approx::test_initialization_approx PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_irrelevant_state XFAIL [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py::test_nondiagonal_obs_cov PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed02::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPFixed06::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESFPPEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestSESETSEstimated::test_mle_estimates PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltFPPFixed::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedFPPEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltDampedETSEstimated::test_mle_estimates PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersFPPEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersETSEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_conf_int PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersDampedETSEstimated::test_mle_estimates PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_fitted PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_output PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_forecasts PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_initial_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_states PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_misc PASSED [ 61%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/tests/test_exponential_smoothing.py::TestHoltWintersNoTrendETSEstimated::test_conf_int PASSED [ 61%]
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+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 73%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 74%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 0, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 75%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 76%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 77%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: 3, Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 78%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 79%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 80%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [1, 3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 81%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: True, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: None, Cov Type: HC0] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-params] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: nonrobust] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-bse] PASSED [ 82%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: n, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: c, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: t, Exog: 2, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: nonrobust] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-fittedvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-llf] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-nobs] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-scale] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-tvalues] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0-use_t] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: None, Cov Type: HC0] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-bse] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-cov_params] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_model] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-df_resid] PASSED [ 83%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-fittedvalues] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-llf] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-nobs] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-params] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-resid] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-scale] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-tvalues] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust-use_t] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: nonrobust] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-bse] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-cov_params] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_model] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-df_resid] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-fittedvalues] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-llf] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-nobs] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-params] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-resid] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-scale] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-tvalues] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_equiv_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0-use_t] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_conf_int_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_f_test_ols_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_other_tests_autoreg[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_apply[AR: [3], Seasonal: False, Trend: ct, Exog: 2, Cov Type: HC0] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back: None] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_smoke_plots[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back: 12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 84%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 85%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 0, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 86%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 87%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 88%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 1, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 89%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 90%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: 3, trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 91%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: n, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: c, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 92%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: t, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: True, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 0, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: None, missing: drop, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: none, pandas: False, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: True, hold_back12] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 93%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_backNone] PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_autoreg_predict_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_dynamic_forecast_smoke[lags: [1, 3], trend: ct, seasonal: False, nexog: 2, periods: 11, missing: drop, pandas: False, hold_back12] PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_parameterless_autoreg PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_predict_errors PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_spec_errors PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::test_ar_select_order_smoke PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_params PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_llf PASSED [ 94%]
+../.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_ar.py::TestAutoRegOLSConstant::test_fpe PASSED [ 94%]
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+
+=============================== warnings summary ===============================
+base/tests/test_penalized.py: 2 warnings
+base/tests/test_shrink_pickle.py: 3 warnings
+discrete/tests/test_count_model.py: 2 warnings
+discrete/tests/test_discrete.py: 9 warnings
+tsa/statespace/tests/test_fixed_params.py: 1 warning
+tsa/statespace/tests/test_impulse_responses.py: 2 warnings
+tsa/statespace/tests/test_sarimax.py: 1 warning
+tsa/vector_ar/tests/test_svar.py: 101 warnings
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
+    warnings.warn("Maximum Likelihood optimization failed to "
+
+discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized
+discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized
+discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized
+discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 4 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py: 8 warnings
+discrete/tests/test_discrete.py: 2 warnings
+discrete/tests/test_truncated_model.py: 1 warning
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:144: ConvergenceWarning: Could not trim params automatically due to failed QC check. Trimming using trim_mode == 'size' will still work.
+    warnings.warn(msg, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedModel_logit::test_fit_regularized
+discrete/tests/test_count_model.py::TestZeroInflatedModel_probit::test_fit_regularized
+discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized
+discrete/tests/test_count_model.py::TestZeroInflatedModelPandas::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 6 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedModel_offset::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 3 out of 4 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 5 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedGeneralizedPoisson::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 3 out of 7 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_null
+discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP_predict2::test_mean
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/model.py:595: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
+    warnings.warn('Inverting hessian failed, no bse or cov_params '
+
+discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 2 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 3 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_count_model.py::TestZeroInflatedNegativeBinomialP::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 1 out of 5 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_discrete.py::TestPoissonL1Compatability::test_params
+discrete/tests/test_discrete.py::TestNegativeBinomialGeoL1Compatability::test_params
+discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
+discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
+discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
+  /usr/lib/python3/dist-packages/numpy/_core/fromnumeric.py:86: RuntimeWarning: overflow encountered in reduce
+    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
+
+discrete/tests/test_discrete.py::test_perfect_prediction
+discrete/tests/test_discrete.py::test_perfect_prediction
+discrete/tests/test_discrete.py::test_perfect_prediction
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/discrete/discrete_model.py:227: PerfectSeparationWarning: Perfect separation or prediction detected, parameter may not be identified
+    warnings.warn(msg, category=PerfectSeparationWarning)
+
+discrete/tests/test_discrete.py::test_negative_binomial_default_alpha_param
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/families/links.py:13: FutureWarning: The nbinom link alias is deprecated. Use NegativeBinomial instead. The nbinom link alias will be removed after the 0.15.0 release.
+    warnings.warn(
+
+discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 4 out of 10 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_discrete.py::TestGeneralizedPoisson_p1::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 5 out of 11 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_influence
+discrete/tests/test_predict.py::TestNegativeBinomialPPredict::test_predict
+discrete/tests/test_predict.py::TestGeneralizedPoissonPredict::test_influence
+discrete/tests/test_truncated_model.py::TestHurdlePoissonR::test_predict
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/_prediction_inference.py:782: UserWarning: using default log-link in get_prediction
+    warnings.warn("using default log-link in get_prediction")
+
+discrete/tests/test_predict.py::test_distr[case9]
+discrete/tests/test_predict.py::test_distr[case10]
+discrete/tests/test_predict.py::test_distr[case11]
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/outliers_influence.py:545: RuntimeWarning: invalid value encountered in sqrt
+    return sf / np.sqrt(hf) / np.sqrt(1 - self.hat_matrix_diag)
+
+discrete/tests/test_truncated_model.py::TestZeroTruncatedNBPModel::test_fit_regularized
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/base/l1_solvers_common.py:71: ConvergenceWarning: QC check did not pass for 2 out of 4 parameters
+  Try increasing solver accuracy or number of iterations, decreasing alpha, or switch solvers
+    warnings.warn(message, ConvergenceWarning)
+
+formula/tests/test_formula.py::TestFormulaPandas::test_summary
+formula/tests/test_formula.py::TestFormulaDict::test_summary
+regression/tests/test_regression.py::TestOLS::test_summary_slim
+  /usr/lib/python3/dist-packages/scipy/stats/_axis_nan_policy.py:430: UserWarning: `kurtosistest` p-value may be inaccurate with fewer than 20 observations; only n=16 observations were given.
+    return hypotest_fun_in(*args, **kwds)
+
+genmod/tests/test_gee.py::TestGEE::test_invalid_args[False-True]
+genmod/tests/test_gee.py::TestGEE::test_invalid_args[True-True]
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:314: RuntimeWarning: divide by zero encountered in log
+    exposure = np.log(exposure)
+
+graphics/tests/test_functional.py: 840 warnings
+  /usr/lib/python3.13/multiprocessing/popen_fork.py:67: DeprecationWarning: This process (pid=707987) is multi-threaded, use of fork() may lead to deadlocks in the child.
+    self.pid = os.fork()
+
+nonparametric/tests/test_kernel_density.py::TestKDEMultivariateConditional::test_unordered_CV_LS
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/kernel_density.py:679: RuntimeWarning: invalid value encountered in scalar divide
+    CV += (G / m_x ** 2) - 2 * (f_X_Y / m_x)
+
+nonparametric/tests/test_kernel_regression.py::TestKernelReg::test_continuousdata_lc_cvls
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/kernel_regression.py:251: RuntimeWarning: invalid value encountered in divide
+    B_x = (G_numer * d_fx - G_denom * d_mx) / (G_denom**2)
+
+nonparametric/tests/test_lowess.py::TestLowess::test_duplicate_xs
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/nonparametric/smoothers_lowess.py:226: RuntimeWarning: invalid value encountered in divide
+    res, _ = _lowess(y, x, x, np.ones_like(x),
+
+regression/tests/test_dimred.py::test_covreduce
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/dimred.py:694: ConvergenceWarning: CovReduce optimization did not converge, |g|=1.287955
+    warnings.warn(msg, ConvergenceWarning)
+
+robust/tests/test_scale.py::TestHuberAxes::test_axis1
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/robust/scale.py:292: RuntimeWarning: divide by zero encountered in divide
+    subset = np.less_equal(np.abs((a - mu) / scale), self.c)
+
+sandbox/tests/test_gam.py::TestGAMGaussianLogLink::test_predict
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/sandbox/gam.py:327: IterationLimitWarning: 
+  Maximum iteration reached.
+  
+    warnings.warn(iteration_limit_doc, IterationLimitWarning)
+
+sandbox/tests/test_gam.py::TestGAMNegativeBinomial::test_predict
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/families/family.py:1367: ValueWarning: Negative binomial dispersion parameter alpha not set. Using default value alpha=1.0.
+    warnings.warn("Negative binomial dispersion parameter alpha not "
+
+stats/tests/test_corrpsd.py::TestCovPSD::test_cov_nearest
+stats/tests/test_corrpsd.py::TestCorrPSD1::test_nearest
+stats/tests/test_corrpsd.py::test_corrpsd_threshold[0]
+stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-15]
+stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-10]
+stats/tests/test_corrpsd.py::test_corrpsd_threshold[1e-06]
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/correlation_tools.py:89: IterationLimitWarning: 
+  Maximum iteration reached.
+  
+    warnings.warn(iteration_limit_doc, IterationLimitWarning)
+
+stats/tests/test_descriptivestats.py::test_description_basic
+stats/tests/test_descriptivestats.py::test_empty_columns
+stats/tests/test_descriptivestats.py::test_empty_columns
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/descriptivestats.py:406: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.
+    mode_res = stats.mode(ser_no_missing, **kwargs)
+
+stats/tests/test_power.py::test_power_solver_warn
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/power.py:132: RuntimeWarning: invalid value encountered in sqrt
+    pow_ = stats.norm.sf(crit - d*np.sqrt(nobs)/sigma)
+
+stats/tests/test_tost.py::test_tost_asym
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/stats/weightstats.py:1479: RuntimeWarning: invalid value encountered in log
+    low = transform(low)
+
+tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid
+tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid
+tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid
+tsa/base/tests/test_tsa_indexes.py::test_instantiation_valid
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/base/tsa_model.py:559: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
+    _index = to_datetime(index)
+
+tsa/holtwinters/tests/test_holtwinters.py::TestHoltWinters::test_holt_damp_r
+tsa/holtwinters/tests/test_holtwinters.py::test_no_params_to_optimize
+  /usr/lib/python3/dist-packages/pandas/util/_decorators.py:213: EstimationWarning: Model has no free parameters to estimate. Set optimized=False to suppress this warning
+    return func(*args, **kwargs)
+
+tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[TNC]
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/holtwinters/model.py:903: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
+    warnings.warn(
+
+tsa/holtwinters/tests/test_holtwinters.py::test_alternative_minimizers[trust-constr]
+  /usr/lib/python3/dist-packages/scipy/optimize/_differentiable_functions.py:317: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.
+    self.H.update(self.x - self.x_prev, self.g - self.g_prev)
+
+tsa/holtwinters/tests/test_holtwinters.py::test_forecast_index_types[irregular]
+tsa/holtwinters/tests/test_holtwinters.py::test_invalid_index
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/base/tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.
+    return get_prediction_index(
+
+tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary
+tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:898: RuntimeWarning: divide by zero encountered in scalar divide
+    return np.sum(resid / self.family.variance(mu)) / self.df_resid
+
+tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py::test_em_nonstationary
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/dynamic_factor_mq.py:2695: UserWarning: EM reached maximum number of iterations (2), without achieving convergence: llf=-23.123, convergence criterion=1.8362 (while specified tolerance was 1e-06)
+    warn(f'EM reached maximum number of iterations ({maxiter}),'
+
+tsa/statespace/tests/test_mlemodel.py::test_integer_params
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/kalman_filter.py:1762: RuntimeWarning: invalid value encountered in scalar divide
+    self.scale = np.sum(scale_obs[d:]) / nobs_k_endog
+
+tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/mlemodel.py:1235: RuntimeWarning: invalid value encountered in divide
+    np.inner(score_obs, score_obs) /
+
+tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
+  /usr/lib/python3/dist-packages/numpy/_core/fromnumeric.py:4268: RuntimeWarning: Degrees of freedom <= 0 for slice
+    return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
+
+tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
+  /usr/lib/python3/dist-packages/numpy/_core/_methods.py:181: RuntimeWarning: invalid value encountered in divide
+    arrmean = um.true_divide(arrmean, div, out=arrmean,
+
+tsa/statespace/tests/test_sarimax.py::test_plot_too_few_obs
+  /usr/lib/python3/dist-packages/numpy/_core/_methods.py:215: RuntimeWarning: invalid value encountered in scalar divide
+    ret = ret.dtype.type(ret / rcount)
+
+tsa/statespace/tests/test_sarimax.py::test_sarimax_starting_values_few_obsevations_long_ma
+tsa/tests/test_stattools.py::test_arma_order_select_ic
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/statespace/sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
+    warn('Non-invertible starting MA parameters found.'
+
+tsa/stl/tests/test_mstl.py::test_number_of_seasonal_components[data-periods2-None-2]
+tsa/stl/tests/test_mstl.py::test_output_invariant_to_period_order[data-periods_ordered1-windows_ordered1-periods_not_ordered1-windows_not_ordered1]
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/stl/mstl.py:218: UserWarning: A period(s) is larger than half the length of time series. Removing these period(s).
+    warnings.warn(
+
+tsa/tests/test_exponential_smoothing.py::test_hessian
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_exponential_smoothing.py:669: PrecisionWarning: Calculation of the Hessian using finite differences is usually subject to substantial approximation errors.
+    austourists_model_fit.model.hessian(
+
+tsa/tests/test_exponential_smoothing.py::test_seasonal_order[heuristic]
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/exponential_smoothing/ets.py:1448: RuntimeWarning: invalid value encountered in divide
+    self.standardized_forecasts_error = (
+
+tsa/tests/test_stattools.py::TestBreakvarHeteroskedasticityTest::test_2d_input_with_missing_values
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_stattools.py:461: UserWarning: Later subset of data for variable 2 has too few non-missing observations to calculate test statistic.
+    actual_statistic, actual_pvalue = breakvar_heteroskedasticity_test(
+
+tsa/tests/test_stattools.py::TestKPSS::test_none
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/tests/test_stattools.py:890: InterpolationWarning: The test statistic is outside of the range of p-values available in the
+  look-up table. The actual p-value is smaller than the p-value returned.
+  
+    kpss(self.x, nlags=None)
+
+tsa/tests/test_stattools.py::test_pacf_1_obs
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/linear_model.py:1483: RuntimeWarning: invalid value encountered in scalar divide
+    r[k] = (x[0:-k] * x[k:]).sum() / (n - k * adj_needed)
+
+tsa/tests/test_stattools.py::test_pacf_1_obs
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/regression/linear_model.py:1490: ValueWarning: Matrix is singular. Using pinv.
+    warnings.warn("Matrix is singular. Using pinv.", ValueWarning)
+
+tsa/vector_ar/tests/test_var.py::test_irf_err_bands
+tsa/vector_ar/tests/test_var.py::test_irf_err_bands
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/irf.py:528: ComplexWarning: Casting complex values to real discards the imaginary part
+    W[i,j,:,:], eigva[i,j,:,0], k[i,j] = util.eigval_decomp(cov_hold[i,j,:,:])
+
+tsa/vector_ar/tests/test_var.py::test_irf_err_bands
+  /build/reproducible-path/statsmodels-0.14.5+dfsg/.pybuild/cpython3_3.13_statsmodels/build/statsmodels/tsa/vector_ar/irf.py:483: ComplexWarning: Casting complex values to real discards the imaginary part
+    W[i], eigva[i], k[i] = util.eigval_decomp(stack_cov[i])
+
+-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
+= 17541 passed, 305 skipped, 140 xfailed, 2 xpassed, 1061 warnings in 2116.44s (0:35:16) =
+make[1]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+   create-stamp debian/debhelper-build-stamp
+   dh_testroot -O--buildsystem=pybuild
+   dh_prep -O--buildsystem=pybuild
+   dh_auto_install -O--buildsystem=pybuild
+I: pybuild plugin_pyproject:178: Copying package built for python3.13 to destdir
+   debian/rules execute_after_dh_auto_install
+make[1]: Entering directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+: # Remove compiled due to testing files
+find debian -name *.pyc -delete
+rm -f debian/*/usr/lib/*/dist-packages/enet_poisson.csv debian/*/usr/lib/*/dist-packages/enet_binomial.csv
+: # strip docs/ since they aren't really a Python module, there is -doc for it
+: # TODO find debian -wholename \*scikits/statsmodels/docs | xargs rm -rf
+: # remove other unnecessary files
+rm -fv debian/*/usr/setup.cfg
+find debian -iname COPYING -o -iname LICENSE* | xargs -r rm -fv
+removed 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/LICENSE.txt'
+removed 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/stats/libqsturng/LICENSE.txt'
+removed 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels-0.14.5+dfsg.dist-info/licenses/LICENSE.txt'
+: # move binary libraries into -lib
+for PACKAGE_NAME in python3-statsmodels; do \
+  for lib in $(find debian/${PACKAGE_NAME}/usr -name '*.so'); do \
+     sdir=$(dirname $lib) ; \
+     tdir=debian/${PACKAGE_NAME}-lib/${sdir#*${PACKAGE_NAME}/} ; \
+     mkdir -p $tdir ; \
+     echo "I: Moving '$lib' into '$tdir'." ; \
+     mv $lib $tdir ; \
+  done ; \
+done
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/robust/_qn.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/robust'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/nonparametric/linbin.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/nonparametric'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/nonparametric/_smoothers_lowess.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/nonparametric'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_tools.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters/_univariate.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters/_inversions.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters/_conventional.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters/_univariate_diffuse.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_filters'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_simulation_smoother.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_kalman_filter.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_initialization.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_representation.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_cfa_simulation_smoother.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_kalman_smoother.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers/_classical.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers/_univariate.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers/_conventional.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers/_alternative.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers/_univariate_diffuse.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/statespace/_smoothers'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/innovations/_arma_innovations.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/innovations'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/_innovations.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/regime_switching/_hamilton_filter.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/regime_switching'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/regime_switching/_kim_smoother.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/regime_switching'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/exponential_smoothing/_ets_smooth.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/exponential_smoothing'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/stl/_stl.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/stl'.
+I: Moving 'debian/python3-statsmodels/usr/lib/python3.13/dist-packages/statsmodels/tsa/holtwinters/_exponential_smoothers.cpython-313-x86_64-linux-gnu.so' into 'debian/python3-statsmodels-lib/usr/lib/python3.13/dist-packages/statsmodels/tsa/holtwinters'.
+make[1]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+   dh_installdocs -O--buildsystem=pybuild
+dh_installdocs: warning: Cannot auto-detect main package for python-statsmodels-doc.  If the default is wrong, please use --doc-main-package
+   dh_sphinxdoc -O--buildsystem=pybuild
+dh_sphinxdoc: warning: ignoring unknown JavaScript code: debian/python-statsmodels-doc/usr/share/doc/python-statsmodels-doc/html/_static/mktree.js
+dh_sphinxdoc: warning: ignoring unknown JavaScript code: debian/python-statsmodels-doc/usr/share/doc/python-statsmodels-doc/html/_static/scripts.js
+dh_sphinxdoc: warning: ignoring unknown JavaScript code: debian/python-statsmodels-doc/usr/share/doc/python-statsmodels-doc/html/_static/facebox.js
+   dh_installchangelogs -O--buildsystem=pybuild
+   dh_installexamples -O--buildsystem=pybuild
+dh_installexamples: warning: Cannot auto-detect main package for python-statsmodels-doc.  If the default is wrong, please use --doc-main-package
+   dh_python3 -O--buildsystem=pybuild
+I: dh_python3 pydist:339: Ignoring complex environment marker: pywinpty; os_name == "nt" and extra == "develop"
+   dh_installsystemduser -O--buildsystem=pybuild
+   dh_lintian -O--buildsystem=pybuild
+   dh_perl -O--buildsystem=pybuild
+   debian/rules execute_before_dh_link
+make[1]: Entering directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+: # deduplicate images - the ||true is because we only build-depend on jdupes if we're building documentation
+jdupes -r -l debian/python-statsmodels-doc/usr/share/doc || true
+make[1]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+   dh_link -O--buildsystem=pybuild
+   dh_strip_nondeterminism -O--buildsystem=pybuild
+   debian/rules override_dh_compress
+make[1]: Entering directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+dh_compress -X.py -X.html -X.pdf -X.css -X.jpg -X.txt -X.js -X.json -X.rtc -X.inv -Xobjects.inv
+make[1]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+   dh_fixperms -O--buildsystem=pybuild
+   debian/rules execute_after_dh_fixperms
+make[1]: Entering directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+find debian -name "*.txt" -exec chmod -x \{\} \;
+make[1]: Leaving directory '/build/reproducible-path/statsmodels-0.14.5+dfsg'
+   dh_missing -O--buildsystem=pybuild
+   dh_dwz -a -O--buildsystem=pybuild
+   dh_strip -a -O--buildsystem=pybuild
+   dh_makeshlibs -a -O--buildsystem=pybuild
+   dh_shlibdeps -a -O--buildsystem=pybuild
+   dh_installdeb -O--buildsystem=pybuild
+   dh_numpy3 -O--buildsystem=pybuild
+Possible precedence issue with control flow operator (return) at /usr/bin/dh_numpy3 line 57.
+   dh_gencontrol -O--buildsystem=pybuild
+dpkg-gencontrol: warning: Provides field of package python3-statsmodels: substitution variable ${python3:Provides} used, but is not defined
+dpkg-gencontrol: warning: package python-statsmodels-doc: substitution variable ${sphinxdoc:Built-Using} unused, but is defined
+   dh_md5sums -O--buildsystem=pybuild
+   dh_builddeb -O--buildsystem=pybuild
+dpkg-deb: building package 'python-statsmodels-doc' in '../python-statsmodels-doc_0.14.5+dfsg-1_all.deb'.
+dpkg-deb: building package 'python3-statsmodels' in '../python3-statsmodels_0.14.5+dfsg-1_all.deb'.
+dpkg-deb: building package 'python3-statsmodels-lib' in '../python3-statsmodels-lib_0.14.5+dfsg-1_amd64.deb'.
+dpkg-deb: building package 'python3-statsmodels-lib-dbgsym' in '../python3-statsmodels-lib-dbgsym_0.14.5+dfsg-1_amd64.deb'.
+ dpkg-genbuildinfo --build=binary -O../statsmodels_0.14.5+dfsg-1_amd64.buildinfo
+ dpkg-genchanges --build=binary -O../statsmodels_0.14.5+dfsg-1_amd64.changes
+dpkg-genchanges: info: binary-only upload (no source code included)
+ dpkg-source --after-build .
+dpkg-buildpackage: info: binary-only upload (no source included)
+dpkg-genchanges: info: including full source code in upload
+I: copying local configuration
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/B01_cleanup starting
+I: user script /srv/workspace/pbuilder/468671/tmp/hooks/B01_cleanup finished
+I: unmounting dev/ptmx filesystem
+I: unmounting dev/pts filesystem
+I: unmounting dev/shm filesystem
+I: unmounting proc filesystem
+I: unmounting sys filesystem
+I: cleaning the build env 
+I: removing directory /srv/workspace/pbuilder/468671 and its subdirectories
+I: Current time: Tue Oct 27 09:08:45 +14 2026
+I: pbuilder-time-stamp: 1793041725