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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmph11yk6mx/5e66c020-4eb3-49b4-84e9-691b434b8e37 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Least squares fitting of models to data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a quick introduction to `statsmodels` for physical scientists (e.g. physicists, astronomers) or engineers.\n", "\n", "Why is this needed?\n", "\n", "Because most of `statsmodels` was written by statisticians and they use a different terminology and sometimes methods, making it hard to know which classes and functions are relevant and what their inputs and outputs mean." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assume you have data points with measurements `y` at positions `x` as well as measurement errors `y_err`.\n", "\n", "How can you use `statsmodels` to fit a straight line model to this data?\n", "\n", "For an extensive discussion see [Hogg et al. (2010), \"Data analysis recipes: Fitting a model to data\"](http://arxiv.org/abs/1008.4686) ... we'll use the example data given by them in Table 1.\n", "\n", "So the model is `f(x) = a * x + b` and on Figure 1 they print the result we want to reproduce ... the best-fit parameter and the parameter errors for a \"standard weighted least-squares fit\" for this data are:\n", "* `a = 2.24 +- 0.11`\n", "* `b = 34 +- 18`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x</th>\n", " <th>y</th>\n", " <th>y_err</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>201.0</td>\n", " <td>592.0</td>\n", " <td>61.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>244.0</td>\n", " <td>401.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>47.0</td>\n", " <td>583.0</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>287.0</td>\n", " <td>402.0</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>203.0</td>\n", " <td>495.0</td>\n", " <td>21.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x y y_err\n", "0 201.0 592.0 61.0\n", "1 244.0 401.0 25.0\n", "2 47.0 583.0 38.0\n", "3 287.0 402.0 15.0\n", "4 203.0 495.0 21.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = \"\"\"\n", " x y y_err\n", "201 592 61\n", "244 401 25\n", " 47 583 38\n", "287 402 15\n", "203 495 21\n", " 58 173 15\n", "210 479 27\n", "202 504 14\n", "198 510 30\n", "158 416 16\n", "165 393 14\n", "201 442 25\n", "157 317 52\n", "131 311 16\n", "166 400 34\n", "160 337 31\n", "186 423 42\n", "125 334 26\n", "218 533 16\n", "146 344 22\n", "\"\"\"\n", "try:\n", " from StringIO import StringIO\n", "except ImportError:\n", " from io import StringIO\n", "data = pd.read_csv(StringIO(data), delim_whitespace=True).astype(float)\n", "\n", "# Note: for the results we compare with the paper here, they drop the first four points\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To fit a straight line use the weighted least squares class [WLS](http://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.WLS.html) ... the parameters are called:\n", "* `exog` = `sm.add_constant(x)`\n", "* `endog` = `y`\n", "* `weights` = `1 / sqrt(y_err)`\n", "\n", "Note that `exog` must be a 2-dimensional array with `x` as a column and an extra column of ones. Adding this column of ones means you want to fit the model `y = a * x + b`, leaving it off means you want to fit the model `y = a * x`.\n", "\n", "And you have to use the option `cov_type='fixed scale'` to tell `statsmodels` that you really have measurement errors with an absolute scale. If you don't, `statsmodels` will treat the weights as relative weights between the data points and internally re-scale them so that the best-fit model will have `chi**2 / ndf = 1`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " WLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.400\n", "Model: WLS Adj. R-squared: 0.367\n", "Method: Least Squares F-statistic: 193.5\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 4.52e-11\n", "Time: 07:39:38 Log-Likelihood: -119.06\n", "No. Observations: 20 AIC: 242.1\n", "Df Residuals: 18 BIC: 244.1\n", "Df Model: 1 \n", "Covariance Type: fixed scale \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 213.2735 14.394 14.817 0.000 185.062 241.485\n", "x 1.0767 0.077 13.910 0.000 0.925 1.228\n", "==============================================================================\n", "Omnibus: 0.943 Durbin-Watson: 2.901\n", "Prob(Omnibus): 0.624 Jarque-Bera (JB): 0.181\n", "Skew: -0.205 Prob(JB): 0.914\n", "Kurtosis: 3.220 Cond. No. 575.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors are based on fixed scale\n" ] } ], "source": [ "exog = sm.add_constant(data['x'])\n", "endog = data['y']\n", "weights = 1. / (data['y_err'] ** 2)\n", "wls = sm.WLS(endog, exog, weights)\n", "results = wls.fit(cov_type='fixed scale')\n", "print(results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check against scipy.optimize.curve_fit" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 1.077 +- 0.077\n", "b = 213.273 +- 14.394\n" ] } ], "source": [ "# You can use `scipy.optimize.curve_fit` to get the best-fit parameters and parameter errors.\n", "from scipy.optimize import curve_fit\n", "\n", "def f(x, a, b):\n", " return a * x + b\n", "\n", "xdata = data['x']\n", "ydata = data['y']\n", "p0 = [0, 0] # initial parameter estimate\n", "sigma = data['y_err']\n", "popt, pcov = curve_fit(f, xdata, ydata, p0, sigma, absolute_sigma=True)\n", "perr = np.sqrt(np.diag(pcov))\n", "print('a = {0:10.3f} +- {1:10.3f}'.format(popt[0], perr[0]))\n", "print('b = {0:10.3f} +- {1:10.3f}'.format(popt[1], perr[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check against self-written cost function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 1.077\n", "b = 213.274\n" ] } ], "source": [ "# You can also use `scipy.optimize.minimize` and write your own cost function.\n", "# This doesn't give you the parameter errors though ... you'd have\n", "# to estimate the HESSE matrix separately ...\n", "from scipy.optimize import minimize\n", "\n", "def chi2(pars):\n", " \"\"\"Cost function.\n", " \"\"\"\n", " y_model = pars[0] * data['x'] + pars[1]\n", " chi = (data['y'] - y_model) / data['y_err']\n", " return np.sum(chi ** 2)\n", "\n", "result = minimize(fun=chi2, x0=[0, 0])\n", "popt = result.x\n", "print('a = {0:10.3f}'.format(popt[0]))\n", "print('b = {0:10.3f}'.format(popt[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Non-linear models" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TODO: we could use the examples from here:\n", "# http://probfit.readthedocs.org/en/latest/api.html#probfit.costfunc.Chi2Regression" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Discrete Choice Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fair's Affair data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A survey of women only was conducted in 1974 by *Redbook* asking about extramarital affairs." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import logit, probit, poisson, ols" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fair, Ray. 1978. \"A Theory of Extramarital Affairs,\" `Journal of Political\n", "Economy`, February, 45-61.\n", "\n", "The data is available at http://fairmodel.econ.yale.edu/rayfair/pdf/2011b.htm\n", "\n" ] } ], "source": [ "print(sm.datasets.fair.SOURCE)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of observations: 6366\n", " Number of variables: 9\n", " Variable name definitions:\n", "\n", " rate_marriage : How rate marriage, 1 = very poor, 2 = poor, 3 = fair,\n", " 4 = good, 5 = very good\n", " age : Age\n", " yrs_married : No. years married. Interval approximations. See\n", " original paper for detailed explanation.\n", " children : No. children\n", " religious : How relgious, 1 = not, 2 = mildly, 3 = fairly,\n", " 4 = strongly\n", " educ : Level of education, 9 = grade school, 12 = high\n", " school, 14 = some college, 16 = college graduate,\n", " 17 = some graduate school, 20 = advanced degree\n", " occupation : 1 = student, 2 = farming, agriculture; semi-skilled,\n", " or unskilled worker; 3 = white-colloar; 4 = teacher\n", " counselor social worker, nurse; artist, writers;\n", " technician, skilled worker, 5 = managerial,\n", " administrative, business, 6 = professional with\n", " advanced degree\n", " occupation_husb : Husband's occupation. Same as occupation.\n", " affairs : measure of time spent in extramarital affairs\n", "\n", " See the original paper for more details.\n", "\n" ] } ], "source": [ "print( sm.datasets.fair.NOTE)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta = sm.datasets.fair.load_pandas().data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " rate_marriage age yrs_married children religious educ occupation \\\n", "0 3.0 32.0 9.0 3.0 3.0 17.0 2.0 \n", "1 3.0 27.0 13.0 3.0 1.0 14.0 3.0 \n", "2 4.0 22.0 2.5 0.0 1.0 16.0 3.0 \n", "3 4.0 37.0 16.5 4.0 3.0 16.0 5.0 \n", "4 5.0 27.0 9.0 1.0 1.0 14.0 3.0 \n", "5 4.0 27.0 9.0 0.0 2.0 14.0 3.0 \n", "6 5.0 37.0 23.0 5.5 2.0 12.0 5.0 \n", "7 5.0 37.0 23.0 5.5 2.0 12.0 2.0 \n", "8 3.0 22.0 2.5 0.0 2.0 12.0 3.0 \n", "9 3.0 27.0 6.0 0.0 1.0 16.0 3.0 \n", "\n", " occupation_husb affairs affair \n", "0 5.0 0.111111 1.0 \n", "1 4.0 3.230769 1.0 \n", "2 5.0 1.400000 1.0 \n", "3 5.0 0.727273 1.0 \n", "4 4.0 4.666666 1.0 \n", "5 4.0 4.666666 1.0 \n", "6 4.0 0.852174 1.0 \n", "7 3.0 1.826086 1.0 \n", "8 3.0 4.799999 1.0 \n", "9 5.0 1.333333 1.0 \n" ] } ], "source": [ "dta['affair'] = (dta['affairs'] > 0).astype(float)\n", "print(dta.head(10))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " rate_marriage age yrs_married children religious \\\n", "count 6366.000000 6366.000000 6366.000000 6366.000000 6366.000000 \n", "mean 4.109645 29.082862 9.009425 1.396874 2.426170 \n", "std 0.961430 6.847882 7.280120 1.433471 0.878369 \n", "min 1.000000 17.500000 0.500000 0.000000 1.000000 \n", "25% 4.000000 22.000000 2.500000 0.000000 2.000000 \n", "50% 4.000000 27.000000 6.000000 1.000000 2.000000 \n", "75% 5.000000 32.000000 16.500000 2.000000 3.000000 \n", "max 5.000000 42.000000 23.000000 5.500000 4.000000 \n", "\n", " educ occupation occupation_husb affairs affair \n", "count 6366.000000 6366.000000 6366.000000 6366.000000 6366.000000 \n", "mean 14.209865 3.424128 3.850141 0.705374 0.322495 \n", "std 2.178003 0.942399 1.346435 2.203374 0.467468 \n", "min 9.000000 1.000000 1.000000 0.000000 0.000000 \n", "25% 12.000000 3.000000 3.000000 0.000000 0.000000 \n", "50% 14.000000 3.000000 4.000000 0.000000 0.000000 \n", "75% 16.000000 4.000000 5.000000 0.484848 1.000000 \n", "max 20.000000 6.000000 6.000000 57.599991 1.000000 \n" ] } ], "source": [ "print(dta.describe())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.545314\n", " Iterations 6\n" ] } ], "source": [ "affair_mod = logit(\"affair ~ occupation + educ + occupation_husb\" \n", " \"+ rate_marriage + age + yrs_married + children\"\n", " \" + religious\", dta).fit()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: affair No. Observations: 6366\n", "Model: Logit Df Residuals: 6357\n", "Method: MLE Df Model: 8\n", "Date: Fri, 12 Jun 2020 Pseudo R-squ.: 0.1327\n", "Time: 07:45:09 Log-Likelihood: -3471.5\n", "converged: True LL-Null: -4002.5\n", " LLR p-value: 5.807e-224\n", "===================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "Intercept 3.7257 0.299 12.470 0.000 3.140 4.311\n", "occupation 0.1602 0.034 4.717 0.000 0.094 0.227\n", "educ -0.0392 0.015 -2.533 0.011 -0.070 -0.009\n", "occupation_husb 0.0124 0.023 0.541 0.589 -0.033 0.057\n", "rate_marriage -0.7161 0.031 -22.784 0.000 -0.778 -0.655\n", "age -0.0605 0.010 -5.885 0.000 -0.081 -0.040\n", "yrs_married 0.1100 0.011 10.054 0.000 0.089 0.131\n", "children -0.0042 0.032 -0.134 0.893 -0.066 0.058\n", "religious -0.3752 0.035 -10.792 0.000 -0.443 -0.307\n", "===================================================================================\n" ] } ], "source": [ "print(affair_mod.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How well are we predicting?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[3882., 431.],\n", " [1326., 727.]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "affair_mod.pred_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coefficients of the discrete choice model do not tell us much. What we're after is marginal effects." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Marginal Effects \n", "=====================================\n", "Dep. Variable: affair\n", "Method: dydx\n", "At: overall\n", "===================================================================================\n", " dy/dx std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "occupation 0.0293 0.006 4.744 0.000 0.017 0.041\n", "educ -0.0072 0.003 -2.538 0.011 -0.013 -0.002\n", "occupation_husb 0.0023 0.004 0.541 0.589 -0.006 0.010\n", "rate_marriage -0.1308 0.005 -26.891 0.000 -0.140 -0.121\n", "age -0.0110 0.002 -5.937 0.000 -0.015 -0.007\n", "yrs_married 0.0201 0.002 10.327 0.000 0.016 0.024\n", "children -0.0008 0.006 -0.134 0.893 -0.012 0.011\n", "religious -0.0685 0.006 -11.119 0.000 -0.081 -0.056\n", "===================================================================================\n" ] } ], "source": [ "mfx = affair_mod.get_margeff()\n", "print(mfx.summary())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rate_marriage 4.000000\n", "age 37.000000\n", "yrs_married 23.000000\n", "children 3.000000\n", "religious 3.000000\n", "educ 12.000000\n", "occupation 3.000000\n", "occupation_husb 4.000000\n", "affairs 0.521739\n", "affair 1.000000\n", "Name: 1000, dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "respondent1000 = dta.ix[1000]\n", "print(respondent1000)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 3.0, 2: 12.0, 3: 4.0, 4: 4.0, 5: 37.0, 6: 23.0, 7: 3.0, 8: 3.0, 0: 1}\n" ] } ], "source": [ "resp = dict(zip(range(1,9), respondent1000[[\"occupation\", \"educ\", \n", " \"occupation_husb\", \"rate_marriage\", \n", " \"age\", \"yrs_married\", \"children\", \n", " \"religious\"]].tolist()))\n", "resp.update({0 : 1})\n", "print(resp)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Marginal Effects \n", "=====================================\n", "Dep. Variable: affair\n", "Method: dydx\n", "At: overall\n", "===================================================================================\n", " dy/dx std err z P>|z| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "occupation 0.0400 0.008 4.711 0.000 0.023 0.057\n", "educ -0.0098 0.004 -2.537 0.011 -0.017 -0.002\n", "occupation_husb 0.0031 0.006 0.541 0.589 -0.008 0.014\n", "rate_marriage -0.1788 0.008 -22.743 0.000 -0.194 -0.163\n", "age -0.0151 0.003 -5.928 0.000 -0.020 -0.010\n", "yrs_married 0.0275 0.003 10.256 0.000 0.022 0.033\n", "children -0.0011 0.008 -0.134 0.893 -0.017 0.014\n", "religious -0.0937 0.009 -10.722 0.000 -0.111 -0.077\n", "===================================================================================\n" ] } ], "source": [ "mfx = affair_mod.get_margeff(atexog=resp)\n", "print(mfx.summary())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-14-13d03df9f844>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maffair_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrespondent1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# patsy requires a DataFrame, not a Series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" ] } ], "source": [ "affair_mod.predict(pd.DataFrame(respondent1000).T) # patsy requires a DataFrame, not a Series" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0751615928505669" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "affair_mod.fittedvalues[1000]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.518781557212148" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "affair_mod.model.cdf(affair_mod.fittedvalues[1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"correct\" model here is likely the Tobit model. We have an work in progress branch \"tobit-model\" on github, if anyone is interested in censored regression models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Logit vs Probit" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "support = np.linspace(-6, 6, 1000)\n", "ax.plot(support, stats.logistic.cdf(support), 'r-', label='Logistic')\n", "ax.plot(support, stats.norm.cdf(support), label='Probit')\n", "ax.legend();" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "support = np.linspace(-6, 6, 1000)\n", "ax.plot(support, stats.logistic.pdf(support), 'r-', label='Logistic')\n", "ax.plot(support, stats.norm.pdf(support), label='Probit')\n", "ax.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the estimates of the Logit Fair model above to a Probit model. Does the prediction table look better? Much difference in marginal effects?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Genarlized Linear Model Example" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Jeff Gill's `Generalized Linear Models: A Unified Approach`\n", "\n", "http://jgill.wustl.edu/research/books.html\n", "\n" ] } ], "source": [ "print(sm.datasets.star98.SOURCE)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "This data is on the California education policy and outcomes (STAR program\n", "results for 1998. The data measured standardized testing by the California\n", "Department of Education that required evaluation of 2nd - 11th grade students\n", "by the the Stanford 9 test on a variety of subjects. This dataset is at\n", "the level of the unified school district and consists of 303 cases. The\n", "binary response variable represents the number of 9th graders scoring\n", "over the national median value on the mathematics exam.\n", "\n", "The data used in this example is only a subset of the original source.\n", "\n" ] } ], "source": [ "print(sm.datasets.star98.DESCRLONG)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 303 (counties in California).\n", "\n", " Number of Variables - 13 and 8 interaction terms.\n", "\n", " Definition of variables names::\n", "\n", " NABOVE - Total number of students above the national median for the\n", " math section.\n", " NBELOW - Total number of students below the national median for the\n", " math section.\n", " LOWINC - Percentage of low income students\n", " PERASIAN - Percentage of Asian student\n", " PERBLACK - Percentage of black students\n", " PERHISP - Percentage of Hispanic students\n", " PERMINTE - Percentage of minority teachers\n", " AVYRSEXP - Sum of teachers' years in educational service divided by the\n", " number of teachers.\n", " AVSALK - Total salary budget including benefits divided by the number\n", " of full-time teachers (in thousands)\n", " PERSPENK - Per-pupil spending (in thousands)\n", " PTRATIO - Pupil-teacher ratio.\n", " PCTAF - Percentage of students taking UC/CSU prep courses\n", " PCTCHRT - Percentage of charter schools\n", " PCTYRRND - Percentage of year-round schools\n", "\n", " The below variables are interaction terms of the variables defined\n", " above.\n", "\n", " PERMINTE_AVYRSEXP\n", " PEMINTE_AVSAL\n", " AVYRSEXP_AVSAL\n", " PERSPEN_PTRATIO\n", " PERSPEN_PCTAF\n", " PTRATIO_PCTAF\n", " PERMINTE_AVTRSEXP_AVSAL\n", " PERSPEN_PTRATIO_PCTAF\n", "\n" ] } ], "source": [ "print(sm.datasets.star98.NOTE)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n", " 'PERMINTE', 'AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF',\n", " 'PCTCHRT', 'PCTYRRND', 'PERMINTE_AVYRSEXP', 'PERMINTE_AVSAL',\n", " 'AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO', 'PERSPEN_PCTAF', 'PTRATIO_PCTAF',\n", " 'PERMINTE_AVYRSEXP_AVSAL', 'PERSPEN_PTRATIO_PCTAF'],\n", " dtype='object')\n" ] } ], "source": [ "dta = sm.datasets.star98.load_pandas().data\n", "print(dta.columns)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " NABOVE NBELOW LOWINC PERASIAN PERBLACK PERHISP PERMINTE\n", "0 452.0 355.0 34.39730 23.299300 14.235280 11.411120 15.918370\n", "1 144.0 40.0 17.36507 29.328380 8.234897 9.314884 13.636360\n", "2 337.0 234.0 32.64324 9.226386 42.406310 13.543720 28.834360\n", "3 395.0 178.0 11.90953 13.883090 3.796973 11.443110 11.111110\n", "4 8.0 57.0 36.88889 12.187500 76.875000 7.604167 43.589740\n", "5 1348.0 899.0 20.93149 28.023510 4.643221 13.808160 15.378490\n", "6 477.0 887.0 53.26898 8.447858 19.374830 37.905330 25.525530\n", "7 565.0 347.0 15.19009 3.665781 2.649680 13.092070 6.203008\n", "8 205.0 320.0 28.21582 10.430420 6.786374 32.334300 13.461540\n", "9 469.0 598.0 32.77897 17.178310 12.484930 28.323290 27.259890\n" ] } ], "source": [ "print(dta[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP', 'PERMINTE']].head(10))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AVYRSEXP AVSALK PERSPENK PTRATIO PCTAF PCTCHRT PCTYRRND\n", "0 14.70646 59.15732 4.445207 21.71025 57.03276 0.0 22.222220\n", "1 16.08324 59.50397 5.267598 20.44278 64.62264 0.0 0.000000\n", "2 14.59559 60.56992 5.482922 18.95419 53.94191 0.0 0.000000\n", "3 14.38939 58.33411 4.165093 21.63539 49.06103 0.0 7.142857\n", "4 13.90568 63.15364 4.324902 18.77984 52.38095 0.0 0.000000\n", "5 14.97755 66.97055 3.916104 24.51914 44.91578 0.0 2.380952\n", "6 14.67829 57.62195 4.270903 22.21278 32.28916 0.0 12.121210\n", "7 13.66197 63.44740 4.309734 24.59026 30.45267 0.0 0.000000\n", "8 16.41760 57.84564 4.527603 21.74138 22.64574 0.0 0.000000\n", "9 12.51864 57.80141 4.648917 20.26010 26.07099 0.0 0.000000\n" ] } ], "source": [ "print(dta[['AVYRSEXP', 'AVSALK', 'PERSPENK', 'PTRATIO', 'PCTAF', 'PCTCHRT', 'PCTYRRND']].head(10))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "formula = 'NABOVE + NBELOW ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT '\n", "formula += '+ PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aside: Binomial distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toss a six-sided die 5 times, what's the probability of exactly 2 fours?" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.16075102880658435" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.binom(5, 1./6).pmf(2)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: `comb` is deprecated!\n", "Importing `comb` from scipy.misc is deprecated in scipy 1.0.0. Use `scipy.special.comb` instead.\n", " \n" ] }, { "data": { "text/plain": [ "0.1607510288065844" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.misc import comb\n", "comb(5,2) * (1/6.)**2 * (5/6.)**3" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.formula.api import glm\n", "glm_mod = glm(formula, dta, family=sm.families.Binomial()).fit()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Generalized Linear Model Regression Results \n", "================================================================================\n", "Dep. Variable: ['NABOVE', 'NBELOW'] No. Observations: 303\n", "Model: GLM Df Residuals: 282\n", "Model Family: Binomial Df Model: 20\n", "Link Function: logit Scale: 1.0\n", "Method: IRLS Log-Likelihood: -2998.6\n", "Date: Fri, 12 Jun 2020 Deviance: 4078.8\n", "Time: 07:45:19 Pearson chi2: 9.60\n", "No. Iterations: 5 \n", "============================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "--------------------------------------------------------------------------------------------\n", "Intercept 2.9589 1.547 1.913 0.056 -0.073 5.990\n", "LOWINC -0.0168 0.000 -38.749 0.000 -0.018 -0.016\n", "PERASIAN 0.0099 0.001 16.505 0.000 0.009 0.011\n", "PERBLACK -0.0187 0.001 -25.182 0.000 -0.020 -0.017\n", "PERHISP -0.0142 0.000 -32.818 0.000 -0.015 -0.013\n", "PCTCHRT 0.0049 0.001 3.921 0.000 0.002 0.007\n", "PCTYRRND -0.0036 0.000 -15.878 0.000 -0.004 -0.003\n", "PERMINTE 0.2545 0.030 8.498 0.000 0.196 0.313\n", "AVYRSEXP 0.2407 0.057 4.212 0.000 0.129 0.353\n", "PERMINTE:AVYRSEXP -0.0141 0.002 -7.391 0.000 -0.018 -0.010\n", "AVSALK 0.0804 0.014 5.775 0.000 0.053 0.108\n", "PERMINTE:AVSALK -0.0040 0.000 -8.450 0.000 -0.005 -0.003\n", "AVYRSEXP:AVSALK -0.0039 0.001 -4.059 0.000 -0.006 -0.002\n", "PERMINTE:AVYRSEXP:AVSALK 0.0002 2.99e-05 7.428 0.000 0.000 0.000\n", "PERSPENK -1.9522 0.317 -6.162 0.000 -2.573 -1.331\n", "PTRATIO -0.3341 0.061 -5.453 0.000 -0.454 -0.214\n", "PERSPENK:PTRATIO 0.0917 0.015 6.321 0.000 0.063 0.120\n", "PCTAF -0.1690 0.033 -5.169 0.000 -0.233 -0.105\n", "PERSPENK:PCTAF 0.0490 0.007 6.574 0.000 0.034 0.064\n", "PTRATIO:PCTAF 0.0080 0.001 5.362 0.000 0.005 0.011\n", "PERSPENK:PTRATIO:PCTAF -0.0022 0.000 -6.445 0.000 -0.003 -0.002\n", "============================================================================================\n" ] } ], "source": [ "print(glm_mod.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of trials " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 807.0\n", "1 184.0\n", "2 571.0\n", "3 573.0\n", "4 65.0\n", "5 2247.0\n", "6 1364.0\n", "7 912.0\n", "8 525.0\n", "9 1067.0\n", "10 3016.0\n", "11 235.0\n", "12 556.0\n", "13 688.0\n", "14 252.0\n", "15 925.0\n", "16 377.0\n", "17 69.0\n", "18 1092.0\n", "19 115.0\n", "20 139.0\n", "21 449.0\n", "22 309.0\n", "23 116.0\n", "24 81.0\n", "25 66.0\n", "26 1259.0\n", "27 190.0\n", "28 322.0\n", "29 2394.0\n", " ... \n", "273 120.0\n", "274 224.0\n", "275 733.0\n", "276 120.0\n", "277 135.0\n", "278 776.0\n", "279 207.0\n", "280 41.0\n", "281 43.0\n", "282 259.0\n", "283 342.0\n", "284 250.0\n", "285 1750.0\n", "286 150.0\n", "287 134.0\n", "288 53.0\n", "289 266.0\n", "290 304.0\n", "291 1338.0\n", "292 1170.0\n", "293 1431.0\n", "294 248.0\n", "295 516.0\n", "296 591.0\n", "297 59.0\n", "298 342.0\n", "299 154.0\n", "300 595.0\n", "301 709.0\n", "302 156.0\n", "Length: 303, dtype: float64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glm_mod.model.data.orig_endog.sum(1)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 470.732584\n", "1 138.266178\n", "2 285.832629\n", "3 392.702917\n", "4 20.963146\n", "5 1543.545102\n", "6 454.209651\n", "7 598.497867\n", "8 261.720305\n", "9 540.687237\n", "10 722.479333\n", "11 203.583934\n", "12 258.167040\n", "13 303.902616\n", "14 168.330747\n", "15 684.393625\n", "16 195.911948\n", "17 29.285268\n", "18 616.911004\n", "19 68.139395\n", "20 48.369683\n", "21 253.303415\n", "22 154.420779\n", "23 41.360255\n", "24 16.809362\n", "25 12.057599\n", "26 565.702043\n", "27 91.247771\n", "28 193.088229\n", "29 1408.837645\n", " ... \n", "273 47.775769\n", "274 63.404739\n", "275 297.019427\n", "276 36.144700\n", "277 35.640558\n", "278 343.034529\n", "279 83.929791\n", "280 16.140299\n", "281 23.773918\n", "282 36.529829\n", "283 60.021489\n", "284 48.727397\n", "285 704.464980\n", "286 31.525238\n", "287 13.014093\n", "288 33.470295\n", "289 68.855461\n", "290 174.264199\n", "291 827.377548\n", "292 506.242734\n", "293 958.896993\n", "294 187.988967\n", "295 259.823500\n", "296 379.553974\n", "297 17.656181\n", "298 111.464708\n", "299 61.037884\n", "300 235.517446\n", "301 290.952508\n", "302 53.312851\n", "Length: 303, dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glm_mod.fittedvalues * glm_mod.model.data.orig_endog.sum(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact\n", "on the response variables:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "exog = glm_mod.model.data.orig_exog # get the dataframe" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 1.000000\n", "LOWINC 41.409877\n", "PERASIAN 5.896335\n", "PERBLACK 5.636808\n", "PERHISP 34.398080\n", "PCTCHRT 1.175909\n", "PCTYRRND 11.611905\n", "PERMINTE 14.694747\n", "AVYRSEXP 14.253875\n", "PERMINTE:AVYRSEXP 209.018700\n", "AVSALK 58.640258\n", "PERMINTE:AVSALK 879.979883\n", "AVYRSEXP:AVSALK 839.718173\n", "PERMINTE:AVYRSEXP:AVSALK 12585.266464\n", "PERSPENK 4.320310\n", "PTRATIO 22.464250\n", "PERSPENK:PTRATIO 96.295756\n", "PCTAF 33.630593\n", "PERSPENK:PCTAF 147.235740\n", "PTRATIO:PCTAF 747.445536\n", "PERSPENK:PTRATIO:PCTAF 3243.607568\n", "dtype: float64\n" ] } ], "source": [ "means25 = exog.mean()\n", "print(means25)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 1.000000\n", "LOWINC 26.683040\n", "PERASIAN 5.896335\n", "PERBLACK 5.636808\n", "PERHISP 34.398080\n", "PCTCHRT 1.175909\n", "PCTYRRND 11.611905\n", "PERMINTE 14.694747\n", "AVYRSEXP 14.253875\n", "PERMINTE:AVYRSEXP 209.018700\n", "AVSALK 58.640258\n", "PERMINTE:AVSALK 879.979883\n", "AVYRSEXP:AVSALK 839.718173\n", "PERMINTE:AVYRSEXP:AVSALK 12585.266464\n", "PERSPENK 4.320310\n", "PTRATIO 22.464250\n", "PERSPENK:PTRATIO 96.295756\n", "PCTAF 33.630593\n", "PERSPENK:PCTAF 147.235740\n", "PTRATIO:PCTAF 747.445536\n", "PERSPENK:PTRATIO:PCTAF 3243.607568\n", "dtype: float64\n" ] } ], "source": [ "means25['LOWINC'] = exog['LOWINC'].quantile(.25)\n", "print(means25)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 1.000000\n", "LOWINC 55.460075\n", "PERASIAN 5.896335\n", "PERBLACK 5.636808\n", "PERHISP 34.398080\n", "PCTCHRT 1.175909\n", "PCTYRRND 11.611905\n", "PERMINTE 14.694747\n", "AVYRSEXP 14.253875\n", "PERMINTE:AVYRSEXP 209.018700\n", "AVSALK 58.640258\n", "PERMINTE:AVSALK 879.979883\n", "AVYRSEXP:AVSALK 839.718173\n", "PERMINTE:AVYRSEXP:AVSALK 12585.266464\n", "PERSPENK 4.320310\n", "PTRATIO 22.464250\n", "PERSPENK:PTRATIO 96.295756\n", "PCTAF 33.630593\n", "PERSPENK:PCTAF 147.235740\n", "PTRATIO:PCTAF 747.445536\n", "PERSPENK:PTRATIO:PCTAF 3243.607568\n", "dtype: float64\n" ] } ], "source": [ "means75 = exog.mean()\n", "means75['LOWINC'] = exog['LOWINC'].quantile(.75)\n", "print(means75)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-36-8a9e3216b7f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresp25\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglm_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeans25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# patsy requires a DataFrame, not a Series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mresp75\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mglm_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeans75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp75\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mresp25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" ] } ], "source": [ "resp25 = glm_mod.predict(pd.DataFrame(means25).T) # patsy requires a DataFrame, not a Series\n", "resp75 = glm_mod.predict(pd.DataFrame(means75).T)\n", "diff = resp75 - resp25" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The interquartile first difference for the percentage of low income households in a school district is:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'diff' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-37-f46d4183d9e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%2.4f%%\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'diff' is not defined" ] } ], "source": [ "print(\"%2.4f%%\" % (diff[0]*100))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nobs = glm_mod.nobs\n", "y = glm_mod.model.endog\n", "yhat = glm_mod.mu" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics.api import abline_plot\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, ylabel='Observed Values', xlabel='Fitted Values')\n", "ax.scatter(yhat, y)\n", "y_vs_yhat = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n", "fig = abline_plot(model_results=y_vs_yhat, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot fitted values vs Pearson residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pearson residuals are defined to be \n", "\n", "$$\\frac{(y - \\mu)}{\\sqrt{(var(\\mu))}}$$\n", "\n", "where var is typically determined by the family. E.g., binomial variance is $np(1 - p)$" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, title='Residual Dependence Plot', xlabel='Fitted Values',\n", " ylabel='Pearson Residuals')\n", "ax.scatter(yhat, stats.zscore(glm_mod.resid_pearson))\n", "ax.axis('tight')\n", "ax.plot([0.0, 1.0],[0.0, 0.0], 'k-');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Histogram of standardized deviance residuals with Kernel Density Estimate overlayed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The definition of the deviance residuals depends on the family. For the Binomial distribution this is \n", "\n", "$$r_{dev} = sign\\left(Y-\\mu\\right)*\\sqrt{2n(Y\\log\\frac{Y}{\\mu}+(1-Y)\\log\\frac{(1-Y)}{(1-\\mu)}}$$\n", "\n", "They can be used to detect ill-fitting covariates" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] } ], "source": [ "resid = glm_mod.resid_deviance\n", "resid_std = stats.zscore(resid) \n", "kde_resid = sm.nonparametric.KDEUnivariate(resid_std)\n", "kde_resid.fit()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", " alternative=\"'density'\", removal=\"3.1\")\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, title=\"Standardized Deviance Residuals\")\n", "ax.hist(resid_std, bins=25, normed=True);\n", "ax.plot(kde_resid.support, kde_resid.density, 'r');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### QQ-plot of deviance residuals" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "fig = sm.graphics.qqplot(resid, line='r', ax=ax)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp5j8buwzg/7072d4d0-72de-4e81-87b7-dea0cc524dc2 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Discrete Choice Models Overview" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data\n", "\n", "Load data from Spector and Mazzeo (1980). Examples follow Greene's Econometric Analysis Ch. 21 (5th Edition)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "spector_data = sm.datasets.spector.load()\n", "spector_data.exog = sm.add_constant(spector_data.exog, prepend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2.66 20. 0. 1. ]\n", " [ 2.89 22. 0. 1. ]\n", " [ 3.28 24. 0. 1. ]\n", " [ 2.92 12. 0. 1. ]\n", " [ 4. 21. 0. 1. ]]\n", "[0. 0. 0. 0. 1.]\n" ] } ], "source": [ "print(spector_data.exog[:5,:])\n", "print(spector_data.endog[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Probability Model (OLS)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [0.46385168 0.01049512 0.37855479]\n" ] } ], "source": [ "lpm_mod = sm.OLS(spector_data.endog, spector_data.exog)\n", "lpm_res = lpm_mod.fit()\n", "print('Parameters: ', lpm_res.params[:-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logit Model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [ 2.82611259 0.09515766 2.37868766 -13.02134686]\n" ] } ], "source": [ "logit_mod = sm.Logit(spector_data.endog, spector_data.exog)\n", "logit_res = logit_mod.fit(disp=0)\n", "print('Parameters: ', logit_res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Marginal Effects" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Marginal Effects \n", "=====================================\n", "Dep. Variable: y\n", "Method: dydx\n", "At: overall\n", "==============================================================================\n", " dy/dx std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 0.3626 0.109 3.313 0.001 0.148 0.577\n", "x2 0.0122 0.018 0.686 0.493 -0.023 0.047\n", "x3 0.3052 0.092 3.304 0.001 0.124 0.486\n", "==============================================================================\n" ] } ], "source": [ "margeff = logit_res.get_margeff()\n", "print(margeff.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in all the discrete data models presented below, we can print a nice summary of results:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 32\n", "Model: Logit Df Residuals: 28\n", "Method: MLE Df Model: 3\n", "Date: Fri, 12 Jun 2020 Pseudo R-squ.: 0.3740\n", "Time: 07:44:25 Log-Likelihood: -12.890\n", "converged: True LL-Null: -20.592\n", " LLR p-value: 0.001502\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 2.8261 1.263 2.238 0.025 0.351 5.301\n", "x2 0.0952 0.142 0.672 0.501 -0.182 0.373\n", "x3 2.3787 1.065 2.234 0.025 0.292 4.465\n", "const -13.0213 4.931 -2.641 0.008 -22.687 -3.356\n", "==============================================================================\n" ] } ], "source": [ "print(logit_res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probit Model " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.400588\n", " Iterations 6\n", "Parameters: [ 1.62581004 0.05172895 1.42633234 -7.45231965]\n", "Marginal effects: \n", " Probit Marginal Effects \n", "=====================================\n", "Dep. Variable: y\n", "Method: dydx\n", "At: overall\n", "==============================================================================\n", " dy/dx std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 0.3608 0.113 3.182 0.001 0.139 0.583\n", "x2 0.0115 0.018 0.624 0.533 -0.025 0.048\n", "x3 0.3165 0.090 3.508 0.000 0.140 0.493\n", "==============================================================================\n" ] } ], "source": [ "probit_mod = sm.Probit(spector_data.endog, spector_data.exog)\n", "probit_res = probit_mod.fit()\n", "probit_margeff = probit_res.get_margeff()\n", "print('Parameters: ', probit_res.params)\n", "print('Marginal effects: ')\n", "print(probit_margeff.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multinomial Logit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load data from the American National Election Studies:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "anes_data = sm.datasets.anes96.load()\n", "anes_exog = anes_data.exog\n", "anes_exog = sm.add_constant(anes_exog, prepend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-2.30258509 7. 36. 3. 1. ]\n", " [ 5.24755025 3. 20. 4. 1. ]\n", " [ 3.43720782 2. 24. 6. 1. ]\n", " [ 4.4200447 3. 28. 6. 1. ]\n", " [ 6.46162441 5. 68. 6. 1. ]]\n", "[6. 1. 1. 1. 0.]\n" ] } ], "source": [ "print(anes_data.exog[:5,:])\n", "print(anes_data.endog[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit MNL model:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 1.548647\n", " Iterations 7\n", "[[-1.15359746e-02 -8.87506530e-02 -1.05966699e-01 -9.15567017e-02\n", " -9.32846040e-02 -1.40880692e-01]\n", " [ 2.97714352e-01 3.91668642e-01 5.73450508e-01 1.27877179e+00\n", " 1.34696165e+00 2.07008014e+00]\n", " [-2.49449954e-02 -2.28978371e-02 -1.48512069e-02 -8.68134503e-03\n", " -1.79040689e-02 -9.43264870e-03]\n", " [ 8.24914421e-02 1.81042758e-01 -7.15241904e-03 1.99827955e-01\n", " 2.16938850e-01 3.21925702e-01]\n", " [ 5.19655317e-03 4.78739761e-02 5.75751595e-02 8.44983753e-02\n", " 8.09584122e-02 1.08894083e-01]\n", " [-3.73401677e-01 -2.25091318e+00 -3.66558353e+00 -7.61384309e+00\n", " -7.06047825e+00 -1.21057509e+01]]\n" ] } ], "source": [ "mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)\n", "mlogit_res = mlogit_mod.fit()\n", "print(mlogit_res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Poisson\n", "\n", "Load the Rand data. Note that this example is similar to Cameron and Trivedi's `Microeconometrics` Table 20.5, but it is slightly different because of minor changes in the data. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "rand_data = sm.datasets.randhie.load()\n", "rand_exog = rand_data.exog.view(float).reshape(len(rand_data.exog), -1)\n", "rand_exog = sm.add_constant(rand_exog, prepend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit Poisson model: " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 3.091609\n", " Iterations 12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Poisson Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 20190\n", "Model: Poisson Df Residuals: 20180\n", "Method: MLE Df Model: 9\n", "Date: Fri, 12 Jun 2020 Pseudo R-squ.: 0.06343\n", "Time: 07:44:33 Log-Likelihood: -62420.\n", "converged: True LL-Null: -66647.\n", " LLR p-value: 0.000\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0525 0.003 -18.216 0.000 -0.058 -0.047\n", "x2 -0.2471 0.011 -23.272 0.000 -0.268 -0.226\n", "x3 0.0353 0.002 19.302 0.000 0.032 0.039\n", "x4 -0.0346 0.002 -21.439 0.000 -0.038 -0.031\n", "x5 0.2717 0.012 22.200 0.000 0.248 0.296\n", "x6 0.0339 0.001 60.098 0.000 0.033 0.035\n", "x7 -0.0126 0.009 -1.366 0.172 -0.031 0.005\n", "x8 0.0541 0.015 3.531 0.000 0.024 0.084\n", "x9 0.2061 0.026 7.843 0.000 0.155 0.258\n", "const 0.7004 0.011 62.741 0.000 0.678 0.722\n", "==============================================================================\n" ] } ], "source": [ "poisson_mod = sm.Poisson(rand_data.endog, rand_exog)\n", "poisson_res = poisson_mod.fit(method=\"newton\")\n", "print(poisson_res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Negative Binomial\n", "\n", "The negative binomial model gives slightly different results. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", " \"Check mle_retvals\", ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " NegativeBinomial Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 20190\n", "Model: NegativeBinomial Df Residuals: 20180\n", "Method: MLE Df Model: 9\n", "Date: Fri, 12 Jun 2020 Pseudo R-squ.: 0.01845\n", "Time: 07:44:47 Log-Likelihood: -43384.\n", "converged: False LL-Null: -44199.\n", " LLR p-value: 0.000\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0580 0.006 -9.517 0.000 -0.070 -0.046\n", "x2 -0.2678 0.023 -11.802 0.000 -0.312 -0.223\n", "x3 0.0412 0.004 9.937 0.000 0.033 0.049\n", "x4 -0.0381 0.003 -11.219 0.000 -0.045 -0.031\n", "x5 0.2690 0.030 8.981 0.000 0.210 0.328\n", "x6 0.0382 0.001 26.081 0.000 0.035 0.041\n", "x7 -0.0441 0.020 -2.200 0.028 -0.083 -0.005\n", "x8 0.0172 0.036 0.477 0.633 -0.054 0.088\n", "x9 0.1780 0.074 2.397 0.017 0.032 0.324\n", "const 0.6636 0.025 26.787 0.000 0.615 0.712\n", "alpha 1.2930 0.019 69.477 0.000 1.256 1.329\n", "==============================================================================\n" ] } ], "source": [ "mod_nbin = sm.NegativeBinomial(rand_data.endog, rand_exog)\n", "res_nbin = mod_nbin.fit(disp=False)\n", "print(res_nbin.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Alternative solvers\n", "\n", "The default method for fitting discrete data MLE models is Newton-Raphson. You can use other solvers by using the ``method`` argument: " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of iterations has been exceeded.\n", " Current function value: 1.548650\n", " Iterations: 100\n", " Function evaluations: 106\n", " Gradient evaluations: 106\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n", " \"Check mle_retvals\", ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " MNLogit Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 944\n", "Model: MNLogit Df Residuals: 908\n", "Method: MLE Df Model: 30\n", "Date: Fri, 12 Jun 2020 Pseudo R-squ.: 0.1648\n", "Time: 07:44:49 Log-Likelihood: -1461.9\n", "converged: False LL-Null: -1750.3\n", " LLR p-value: 1.827e-102\n", "==============================================================================\n", " y=1 coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0116 0.034 -0.338 0.735 -0.079 0.056\n", "x2 0.2973 0.094 3.175 0.001 0.114 0.481\n", "x3 -0.0250 0.007 -3.825 0.000 -0.038 -0.012\n", "x4 0.0821 0.074 1.116 0.264 -0.062 0.226\n", "x5 0.0052 0.018 0.294 0.769 -0.029 0.040\n", "const -0.3689 0.630 -0.586 0.558 -1.603 0.866\n", "------------------------------------------------------------------------------\n", " y=2 coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0888 0.039 -2.269 0.023 -0.166 -0.012\n", "x2 0.3913 0.108 3.615 0.000 0.179 0.603\n", "x3 -0.0229 0.008 -2.897 0.004 -0.038 -0.007\n", "x4 0.1808 0.085 2.120 0.034 0.014 0.348\n", "x5 0.0478 0.022 2.145 0.032 0.004 0.091\n", "const -2.2451 0.763 -2.942 0.003 -3.741 -0.749\n", "------------------------------------------------------------------------------\n", " y=3 coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.1062 0.057 -1.861 0.063 -0.218 0.006\n", "x2 0.5730 0.159 3.614 0.000 0.262 0.884\n", "x3 -0.0149 0.011 -1.313 0.189 -0.037 0.007\n", "x4 -0.0075 0.126 -0.060 0.952 -0.255 0.240\n", "x5 0.0575 0.034 1.711 0.087 -0.008 0.123\n", "const -3.6592 1.156 -3.164 0.002 -5.926 -1.393\n", "------------------------------------------------------------------------------\n", " y=4 coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0914 0.044 -2.085 0.037 -0.177 -0.005\n", "x2 1.2826 0.129 9.937 0.000 1.030 1.536\n", "x3 -0.0085 0.008 -1.008 0.314 -0.025 0.008\n", "x4 0.2012 0.094 2.136 0.033 0.017 0.386\n", "x5 0.0850 0.026 3.240 0.001 0.034 0.136\n", "const -7.6589 0.960 -7.982 0.000 -9.540 -5.778\n", "------------------------------------------------------------------------------\n", " y=5 coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0934 0.039 -2.374 0.018 -0.170 -0.016\n", "x2 1.3451 0.117 11.485 0.000 1.116 1.575\n", "x3 -0.0180 0.008 -2.362 0.018 -0.033 -0.003\n", "x4 0.2161 0.085 2.542 0.011 0.049 0.383\n", "x5 0.0808 0.023 3.517 0.000 0.036 0.126\n", "const -7.0401 0.844 -8.344 0.000 -8.694 -5.387\n", "------------------------------------------------------------------------------\n", " y=6 coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.1409 0.042 -3.345 0.001 -0.224 -0.058\n", "x2 2.0686 0.143 14.433 0.000 1.788 2.349\n", "x3 -0.0095 0.008 -1.164 0.244 -0.025 0.006\n", "x4 0.3216 0.091 3.532 0.000 0.143 0.500\n", "x5 0.1087 0.025 4.299 0.000 0.059 0.158\n", "const -12.0913 1.059 -11.415 0.000 -14.167 -10.015\n", "==============================================================================\n" ] } ], "source": [ "mlogit_res = mlogit_mod.fit(method='bfgs', maxiter=100)\n", "print(mlogit_res.summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formulas: Fitting models using R-style formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the [patsy](http://patsy.readthedocs.org/) package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the ``patsy`` docs: \n", "\n", "* [Patsy formula language description](http://patsy.readthedocs.org/)\n", "\n", "## Loading modules and functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import convention" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can import explicitly from statsmodels.formula.api" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.formula.api import ols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you can just use the `formula` namespace of the main `statsmodels.api`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bound method Model.from_formula of <class 'statsmodels.regression.linear_model.OLS'>>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.formula.ols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or you can use the following conventioin" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import statsmodels.formula.api as smf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These names are just a convenient way to get access to each model's `from_formula` classmethod. See, for instance" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<bound method Model.from_formula of <class 'statsmodels.regression.linear_model.OLS'>>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.OLS.from_formula" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the lower case models accept ``formula`` and ``data`` arguments, whereas upper case ones take ``endog`` and ``exog`` design matrices. ``formula`` accepts a string which describes the model in terms of a ``patsy`` formula. ``data`` takes a [pandas](http://pandas.pydata.org/) data frame or any other data structure that defines a ``__getitem__`` for variable names like a structured array or a dictionary of variables. \n", "\n", "``dir(sm.formula)`` will print a list of available models. \n", "\n", "Formula-compatible models have the following generic call signature: ``(formula, data, subset=None, *args, **kwargs)``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## OLS regression using formulas\n", "\n", "To begin, we fit the linear model described on the [Getting Started](gettingstarted.html) page. Download the data, subset columns, and list-wise delete to remove missing observations:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta = sm.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Lottery</th>\n", " <th>Literacy</th>\n", " <th>Wealth</th>\n", " <th>Region</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>41</td>\n", " <td>37</td>\n", " <td>73</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>38</td>\n", " <td>51</td>\n", " <td>22</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>66</td>\n", " <td>13</td>\n", " <td>61</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>80</td>\n", " <td>46</td>\n", " <td>76</td>\n", " <td>E</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>79</td>\n", " <td>69</td>\n", " <td>83</td>\n", " <td>E</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Lottery Literacy Wealth Region\n", "0 41 37 73 E\n", "1 38 51 22 N\n", "2 66 13 61 C\n", "3 80 46 76 E\n", "4 79 69 83 E" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = dta.data[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit the model:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Lottery R-squared: 0.338\n", "Model: OLS Adj. R-squared: 0.287\n", "Method: Least Squares F-statistic: 6.636\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 1.07e-05\n", "Time: 07:44:05 Log-Likelihood: -375.30\n", "No. Observations: 85 AIC: 764.6\n", "Df Residuals: 78 BIC: 781.7\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 38.6517 9.456 4.087 0.000 19.826 57.478\n", "Region[T.E] -15.4278 9.727 -1.586 0.117 -34.793 3.938\n", "Region[T.N] -10.0170 9.260 -1.082 0.283 -28.453 8.419\n", "Region[T.S] -4.5483 7.279 -0.625 0.534 -19.039 9.943\n", "Region[T.W] -10.0913 7.196 -1.402 0.165 -24.418 4.235\n", "Literacy -0.1858 0.210 -0.886 0.378 -0.603 0.232\n", "Wealth 0.4515 0.103 4.390 0.000 0.247 0.656\n", "==============================================================================\n", "Omnibus: 3.049 Durbin-Watson: 1.785\n", "Prob(Omnibus): 0.218 Jarque-Bera (JB): 2.694\n", "Skew: -0.340 Prob(JB): 0.260\n", "Kurtosis: 2.454 Cond. No. 371.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "mod = ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Categorical variables\n", "\n", "Looking at the summary printed above, notice that ``patsy`` determined that elements of *Region* were text strings, so it treated *Region* as a categorical variable. `patsy`'s default is also to include an intercept, so we automatically dropped one of the *Region* categories.\n", "\n", "If *Region* had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the ``C()`` operator: " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 38.651655\n", "C(Region)[T.E] -15.427785\n", "C(Region)[T.N] -10.016961\n", "C(Region)[T.S] -4.548257\n", "C(Region)[T.W] -10.091276\n", "Literacy -0.185819\n", "Wealth 0.451475\n", "dtype: float64\n" ] } ], "source": [ "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Patsy's mode advanced features for categorical variables are discussed in: [Patsy: Contrast Coding Systems for categorical variables](contrasts.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Operators\n", "\n", "We have already seen that \"~\" separates the left-hand side of the model from the right-hand side, and that \"+\" adds new columns to the design matrix. \n", "\n", "### Removing variables\n", "\n", "The \"-\" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by: " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C(Region)[C] 38.651655\n", "C(Region)[E] 23.223870\n", "C(Region)[N] 28.634694\n", "C(Region)[S] 34.103399\n", "C(Region)[W] 28.560379\n", "Literacy -0.185819\n", "Wealth 0.451475\n", "dtype: float64\n" ] } ], "source": [ "res = ols(formula='Lottery ~ Literacy + Wealth + C(Region) -1 ', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiplicative interactions\n", "\n", "\":\" adds a new column to the design matrix with the interaction of the other two columns. \"*\" will also include the individual columns that were multiplied together:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Literacy:Wealth 0.018176\n", "dtype: float64 \n", "\n", "Literacy 0.427386\n", "Wealth 1.080987\n", "Literacy:Wealth -0.013609\n", "dtype: float64\n" ] } ], "source": [ "res1 = ols(formula='Lottery ~ Literacy : Wealth - 1', data=df).fit()\n", "res2 = ols(formula='Lottery ~ Literacy * Wealth - 1', data=df).fit()\n", "print(res1.params, '\\n')\n", "print(res2.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many other things are possible with operators. Please consult the [patsy docs](https://patsy.readthedocs.org/en/latest/formulas.html) to learn more." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions\n", "\n", "You can apply vectorized functions to the variables in your model: " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 115.609119\n", "np.log(Literacy) -20.393959\n", "dtype: float64\n" ] } ], "source": [ "res = smf.ols(formula='Lottery ~ np.log(Literacy)', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a custom function:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept 136.003079\n", "log_plus_1(Literacy) -20.393959\n", "dtype: float64\n" ] } ], "source": [ "def log_plus_1(x):\n", " return np.log(x) + 1.\n", "res = smf.ols(formula='Lottery ~ log_plus_1(Literacy)', data=df).fit()\n", "print(res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any function that is in the calling namespace is available to the formula." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using formulas with models that do not (yet) support them\n", "\n", "Even if a given `statsmodels` function does not support formulas, you can still use `patsy`'s formula language to produce design matrices. Those matrices \n", "can then be fed to the fitting function as `endog` and `exog` arguments. \n", "\n", "To generate ``numpy`` arrays: " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Lottery\n", "0 41.0\n", "1 38.0\n", "2 66.0\n", "3 80.0\n", "4 79.0\n", " Intercept Literacy Wealth Literacy:Wealth\n", "0 1.0 37.0 73.0 2701.0\n", "1 1.0 51.0 22.0 1122.0\n", "2 1.0 13.0 61.0 793.0\n", "3 1.0 46.0 76.0 3496.0\n", "4 1.0 69.0 83.0 5727.0\n" ] } ], "source": [ "import patsy\n", "f = 'Lottery ~ Literacy * Wealth'\n", "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n", "print(y[:5])\n", "print(X[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate pandas data frames: " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Lottery\n", "0 41.0\n", "1 38.0\n", "2 66.0\n", "3 80.0\n", "4 79.0\n", " Intercept Literacy Wealth Literacy:Wealth\n", "0 1.0 37.0 73.0 2701.0\n", "1 1.0 51.0 22.0 1122.0\n", "2 1.0 13.0 61.0 793.0\n", "3 1.0 46.0 76.0 3496.0\n", "4 1.0 69.0 83.0 5727.0\n" ] } ], "source": [ "f = 'Lottery ~ Literacy * Wealth'\n", "y,X = patsy.dmatrices(f, df, return_type='dataframe')\n", "print(y[:5])\n", "print(X[:5])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Lottery R-squared: 0.309\n", "Model: OLS Adj. R-squared: 0.283\n", "Method: Least Squares F-statistic: 12.06\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 1.32e-06\n", "Time: 07:44:06 Log-Likelihood: -377.13\n", "No. Observations: 85 AIC: 762.3\n", "Df Residuals: 81 BIC: 772.0\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "===================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "Intercept 38.6348 15.825 2.441 0.017 7.149 70.121\n", "Literacy -0.3522 0.334 -1.056 0.294 -1.016 0.312\n", "Wealth 0.4364 0.283 1.544 0.126 -0.126 0.999\n", "Literacy:Wealth -0.0005 0.006 -0.085 0.933 -0.013 0.012\n", "==============================================================================\n", "Omnibus: 4.447 Durbin-Watson: 1.953\n", "Prob(Omnibus): 0.108 Jarque-Bera (JB): 3.228\n", "Skew: -0.332 Prob(JB): 0.199\n", "Kurtosis: 2.314 Cond. No. 1.40e+04\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 1.4e+04. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "print(sm.OLS(y, X).fit().summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Likelihood Estimation (Generic models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give two examples: \n", "\n", "1. Probit model for binary dependent variables\n", "2. Negative binomial model for count data\n", "\n", "The `GenericLikelihoodModel` class eases the process by providing tools such as automatic numeric differentiation and a unified interface to ``scipy`` optimization functions. Using ``statsmodels``, users can fit new MLE models simply by \"plugging-in\" a log-likelihood function. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Probit model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``Spector`` dataset is distributed with ``statsmodels``. You can access a vector of values for the dependent variable (``endog``) and a matrix of regressors (``exog``) like this:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 32\n", "\n", " Number of Variables - 4\n", "\n", " Variable name definitions::\n", "\n", " Grade - binary variable indicating whether or not a student's grade\n", " improved. 1 indicates an improvement.\n", " TUCE - Test score on economics test\n", " PSI - participation in program\n", " GPA - Student's grade point average\n", "\n", " GPA TUCE PSI\n", "0 2.66 20.0 0.0\n", "1 2.89 22.0 0.0\n", "2 3.28 24.0 0.0\n", "3 2.92 12.0 0.0\n", "4 4.00 21.0 0.0\n" ] } ], "source": [ "data = sm.datasets.spector.load_pandas()\n", "exog = data.exog\n", "endog = data.endog\n", "print(sm.datasets.spector.NOTE)\n", "print(data.exog.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Them, we add a constant to the matrix of regressors:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "exog = sm.add_constant(exog, prepend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create your own Likelihood Model, you simply need to overwrite the loglike method." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class MyProbit(GenericLikelihoodModel):\n", " def loglike(self, params):\n", " exog = self.exog\n", " endog = self.endog\n", " q = 2 * endog - 1\n", " return stats.norm.logcdf(q*np.dot(exog, params)).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate the model and print a summary:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.400588\n", " Iterations: 292\n", " Function evaluations: 494\n", " MyProbit Results \n", "==============================================================================\n", "Dep. Variable: GRADE Log-Likelihood: -12.819\n", "Model: MyProbit AIC: 33.64\n", "Method: Maximum Likelihood BIC: 39.50\n", "Date: Fri, 12 Jun 2020 \n", "Time: 07:43:13 \n", "No. Observations: 32 \n", "Df Residuals: 28 \n", "Df Model: 3 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -7.4523 2.542 -2.931 0.003 -12.435 -2.469\n", "GPA 1.6258 0.694 2.343 0.019 0.266 2.986\n", "TUCE 0.0517 0.084 0.617 0.537 -0.113 0.216\n", "PSI 1.4263 0.595 2.397 0.017 0.260 2.593\n", "==============================================================================\n" ] } ], "source": [ "sm_probit_manual = MyProbit(endog, exog).fit()\n", "print(sm_probit_manual.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare your Probit implementation to ``statsmodels``' \"canned\" implementation:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.400588\n", " Iterations 6\n" ] } ], "source": [ "sm_probit_canned = sm.Probit(endog, exog).fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "const -7.452320\n", "GPA 1.625810\n", "TUCE 0.051729\n", "PSI 1.426332\n", "dtype: float64\n", "[-7.45233176 1.62580888 0.05172971 1.42631954]\n" ] } ], "source": [ "print(sm_probit_canned.params)\n", "print(sm_probit_manual.params)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " const GPA TUCE PSI\n", "const 6.464166 -1.169668 -0.101173 -0.594792\n", "GPA -1.169668 0.481473 -0.018914 0.105439\n", "TUCE -0.101173 -0.018914 0.007038 0.002472\n", "PSI -0.594792 0.105439 0.002472 0.354070\n", "[[ 6.46416770e+00 -1.16966617e+00 -1.01173180e-01 -5.94788993e-01]\n", " [-1.16966617e+00 4.81472116e-01 -1.89134586e-02 1.05438227e-01]\n", " [-1.01173180e-01 -1.89134586e-02 7.03758392e-03 2.47189191e-03]\n", " [-5.94788993e-01 1.05438227e-01 2.47189191e-03 3.54069512e-01]]\n" ] } ], "source": [ "print(sm_probit_canned.cov_params())\n", "print(sm_probit_manual.cov_params())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the ``GenericMaximumLikelihood`` class provides automatic differentiation, so we didn't have to provide Hessian or Score functions in order to calculate the covariance estimates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Example 2: Negative Binomial Regression for Count Data\n", "\n", "Consider a negative binomial regression model for count data with\n", "log-likelihood (type NB-2) function expressed as:\n", "\n", "$$\n", " \\mathcal{L}(\\beta_j; y, \\alpha) = \\sum_{i=1}^n y_i ln \n", " \\left ( \\frac{\\alpha exp(X_i'\\beta)}{1+\\alpha exp(X_i'\\beta)} \\right ) -\n", " \\frac{1}{\\alpha} ln(1+\\alpha exp(X_i'\\beta)) + ln \\Gamma (y_i + 1/\\alpha) - ln \\Gamma (y_i+1) - ln \\Gamma (1/\\alpha)\n", "$$\n", "\n", "with a matrix of regressors $X$, a vector of coefficients $\\beta$,\n", "and the negative binomial heterogeneity parameter $\\alpha$. \n", "\n", "Using the ``nbinom`` distribution from ``scipy``, we can write this likelihood\n", "simply as:\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from scipy.stats import nbinom" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def _ll_nb2(y, X, beta, alph):\n", " mu = np.exp(np.dot(X, beta))\n", " size = 1/alph\n", " prob = size/(size+mu)\n", " ll = nbinom.logpmf(y, size, prob)\n", " return ll" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### New Model Class\n", "\n", "We create a new model class which inherits from ``GenericLikelihoodModel``:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NBin(GenericLikelihoodModel):\n", " def __init__(self, endog, exog, **kwds):\n", " super(NBin, self).__init__(endog, exog, **kwds)\n", " \n", " def nloglikeobs(self, params):\n", " alph = params[-1]\n", " beta = params[:-1]\n", " ll = _ll_nb2(self.endog, self.exog, beta, alph)\n", " return -ll \n", " \n", " def fit(self, start_params=None, maxiter=10000, maxfun=5000, **kwds):\n", " # we have one additional parameter and we need to add it for summary\n", " self.exog_names.append('alpha')\n", " if start_params == None:\n", " # Reasonable starting values\n", " start_params = np.append(np.zeros(self.exog.shape[1]), .5)\n", " # intercept\n", " start_params[-2] = np.log(self.endog.mean())\n", " return super(NBin, self).fit(start_params=start_params, \n", " maxiter=maxiter, maxfun=maxfun, \n", " **kwds) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Two important things to notice: \n", "\n", "+ ``nloglikeobs``: This function should return one evaluation of the negative log-likelihood function per observation in your dataset (i.e. rows of the endog/X matrix). \n", "+ ``start_params``: A one-dimensional array of starting values needs to be provided. The size of this array determines the number of parameters that will be used in optimization.\n", " \n", "That's it! You're done!\n", "\n", "### Usage Example\n", "\n", "The [Medpar](http://vincentarelbundock.github.com/Rdatasets/doc/COUNT/medpar.html)\n", "dataset is hosted in CSV format at the [Rdatasets repository](http://vincentarelbundock.github.com/Rdatasets). We use the ``read_csv``\n", "function from the [Pandas library](http://pandas.pydata.org) to load the data\n", "in memory. We then print the first few columns: \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>los</th>\n", " <th>hmo</th>\n", " <th>white</th>\n", " <th>died</th>\n", " <th>age80</th>\n", " <th>type</th>\n", " <th>type1</th>\n", " <th>type2</th>\n", " <th>type3</th>\n", " <th>provnum</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30001</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " los hmo white died age80 type type1 type2 type3 provnum\n", "0 4 0 1 0 0 1 1 0 0 30001\n", "1 9 1 1 0 0 1 1 0 0 30001\n", "2 3 1 1 1 1 1 1 0 0 30001\n", "3 9 0 1 0 0 1 1 0 0 30001\n", "4 1 0 1 1 1 1 1 0 0 30001" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "medpar = sm.datasets.get_rdataset(\"medpar\", \"COUNT\", cache=True).data\n", "\n", "medpar.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model we are interested in has a vector of non-negative integers as\n", "dependent variable (``los``), and 5 regressors: ``Intercept``, ``type2``,\n", "``type3``, ``hmo``, ``white``.\n", "\n", "For estimation, we need to create two variables to hold our regressors and the outcome variable. These can be ndarrays or pandas objects." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y = medpar.los\n", "X = medpar[[\"type2\", \"type3\", \"hmo\", \"white\"]].copy()\n", "X[\"constant\"] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we fit the model and extract some information: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 3.209014\n", " Iterations: 805\n", " Function evaluations: 1238\n" ] } ], "source": [ "mod = NBin(y, X)\n", "res = mod.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Extract parameter estimates, standard errors, p-values, AIC, etc.:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [ 0.2212642 0.70613942 -0.06798155 -0.12903932 2.31026565 0.44575147]\n", "Standard errors: [0.05059259 0.07613047 0.05326096 0.0685414 0.06794696 0.01981542]\n", "P-values: [1.22298084e-005 1.76979047e-020 2.01819053e-001 5.97481232e-002\n", " 2.15207253e-253 4.62688811e-112]\n", "AIC: 9604.95320583016\n" ] } ], "source": [ "print('Parameters: ', res.params)\n", "print('Standard errors: ', res.bse)\n", "print('P-values: ', res.pvalues)\n", "print('AIC: ', res.aic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual, you can obtain a full list of available information by typing\n", "``dir(res)``.\n", "We can also look at the summary of the estimation results." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " NBin Results \n", "==============================================================================\n", "Dep. Variable: los Log-Likelihood: -4797.5\n", "Model: NBin AIC: 9605.\n", "Method: Maximum Likelihood BIC: 9632.\n", "Date: Fri, 12 Jun 2020 \n", "Time: 07:43:26 \n", "No. Observations: 1495 \n", "Df Residuals: 1490 \n", "Df Model: 4 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "type2 0.2213 0.051 4.373 0.000 0.122 0.320\n", "type3 0.7061 0.076 9.275 0.000 0.557 0.855\n", "hmo -0.0680 0.053 -1.276 0.202 -0.172 0.036\n", "white -0.1290 0.069 -1.883 0.060 -0.263 0.005\n", "constant 2.3103 0.068 34.001 0.000 2.177 2.443\n", "alpha 0.4458 0.020 22.495 0.000 0.407 0.485\n", "==============================================================================\n" ] } ], "source": [ "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the results by using the statsmodels implementation of the Negative Binomial model, which uses the analytic score function and Hessian." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " NegativeBinomial Regression Results \n", "==============================================================================\n", "Dep. Variable: los No. Observations: 1495\n", "Model: NegativeBinomial Df Residuals: 1490\n", "Method: MLE Df Model: 4\n", "Date: Fri, 12 Jun 2020 Pseudo R-squ.: 0.01215\n", "Time: 07:43:27 Log-Likelihood: -4797.5\n", "converged: True LL-Null: -4856.5\n", " LLR p-value: 1.404e-24\n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "type2 0.2212 0.051 4.373 0.000 0.122 0.320\n", "type3 0.7062 0.076 9.276 0.000 0.557 0.855\n", "hmo -0.0680 0.053 -1.277 0.202 -0.172 0.036\n", "white -0.1291 0.069 -1.883 0.060 -0.263 0.005\n", "constant 2.3103 0.068 34.001 0.000 2.177 2.443\n", "alpha 0.4458 0.020 22.495 0.000 0.407 0.485\n", "==============================================================================\n" ] } ], "source": [ "res_nbin = sm.NegativeBinomial(y, X).fit(disp=0)\n", "print(res_nbin.summary())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type2 0.221231\n", "type3 0.706175\n", "hmo -0.067990\n", "white -0.129065\n", "constant 2.310288\n", "alpha 0.445758\n", "dtype: float64\n" ] } ], "source": [ "print(res_nbin.params)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "type2 0.050592\n", "type3 0.076132\n", "hmo 0.053261\n", "white 0.068542\n", "constant 0.067947\n", "alpha 0.019816\n", "dtype: float64\n" ] } ], "source": [ "print(res_nbin.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we could compare them to results obtained using the MASS implementation for R:\n", "\n", " url = 'http://vincentarelbundock.github.com/Rdatasets/csv/COUNT/medpar.csv'\n", " medpar = read.csv(url)\n", " f = los~factor(type)+hmo+white\n", " \n", " library(MASS)\n", " mod = glm.nb(f, medpar)\n", " coef(summary(mod))\n", " Estimate Std. Error z value Pr(>|z|)\n", " (Intercept) 2.31027893 0.06744676 34.253370 3.885556e-257\n", " factor(type)2 0.22124898 0.05045746 4.384861 1.160597e-05\n", " factor(type)3 0.70615882 0.07599849 9.291748 1.517751e-20\n", " hmo -0.06795522 0.05321375 -1.277024 2.015939e-01\n", " white -0.12906544 0.06836272 -1.887951 5.903257e-02\n", "\n", "### Numerical precision \n", "\n", "The ``statsmodels`` generic MLE and ``R`` parameter estimates agree up to the fourth decimal. The standard errors, however, agree only up to the second decimal. This discrepancy is the result of imprecision in our Hessian numerical estimates. In the current context, the difference between ``MASS`` and ``statsmodels`` standard error estimates is substantively irrelevant, but it highlights the fact that users who need very precise estimates may not always want to rely on default settings when using numerical derivatives. In such cases, it is better to use analytical derivatives with the ``LikelihoodModel`` class." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generalized Linear Models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm\n", "from scipy import stats\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GLM: Binomial response data\n", "\n", "### Load data\n", "\n", " In this example, we use the Star98 dataset which was taken with permission\n", " from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook\n", " information can be obtained by typing: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 303 (counties in California).\n", "\n", " Number of Variables - 13 and 8 interaction terms.\n", "\n", " Definition of variables names::\n", "\n", " NABOVE - Total number of students above the national median for the\n", " math section.\n", " NBELOW - Total number of students below the national median for the\n", " math section.\n", " LOWINC - Percentage of low income students\n", " PERASIAN - Percentage of Asian student\n", " PERBLACK - Percentage of black students\n", " PERHISP - Percentage of Hispanic students\n", " PERMINTE - Percentage of minority teachers\n", " AVYRSEXP - Sum of teachers' years in educational service divided by the\n", " number of teachers.\n", " AVSALK - Total salary budget including benefits divided by the number\n", " of full-time teachers (in thousands)\n", " PERSPENK - Per-pupil spending (in thousands)\n", " PTRATIO - Pupil-teacher ratio.\n", " PCTAF - Percentage of students taking UC/CSU prep courses\n", " PCTCHRT - Percentage of charter schools\n", " PCTYRRND - Percentage of year-round schools\n", "\n", " The below variables are interaction terms of the variables defined\n", " above.\n", "\n", " PERMINTE_AVYRSEXP\n", " PEMINTE_AVSAL\n", " AVYRSEXP_AVSAL\n", " PERSPEN_PTRATIO\n", " PERSPEN_PCTAF\n", " PTRATIO_PCTAF\n", " PERMINTE_AVTRSEXP_AVSAL\n", " PERSPEN_PTRATIO_PCTAF\n", "\n" ] } ], "source": [ "print(sm.datasets.star98.NOTE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the data and add a constant to the exogenous (independent) variables:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:89: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " endog = np.column_stack(data[field] for field in endog_name)\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "data = sm.datasets.star98.load()\n", "data.exog = sm.add_constant(data.exog, prepend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[452. 355.]\n", " [144. 40.]\n", " [337. 234.]\n", " [395. 178.]\n", " [ 8. 57.]]\n" ] } ], "source": [ "print(data.endog[:5,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The independent variables include all the other variables described above, as\n", " well as the interaction terms:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[3.43973000e+01 2.32993000e+01 1.42352800e+01 1.14111200e+01\n", " 1.59183700e+01 1.47064600e+01 5.91573200e+01 4.44520700e+00\n", " 2.17102500e+01 5.70327600e+01 0.00000000e+00 2.22222200e+01\n", " 2.34102872e+02 9.41688110e+02 8.69994800e+02 9.65065600e+01\n", " 2.53522420e+02 1.23819550e+03 1.38488985e+04 5.50403520e+03\n", " 1.00000000e+00]\n", " [1.73650700e+01 2.93283800e+01 8.23489700e+00 9.31488400e+00\n", " 1.36363600e+01 1.60832400e+01 5.95039700e+01 5.26759800e+00\n", " 2.04427800e+01 6.46226400e+01 0.00000000e+00 0.00000000e+00\n", " 2.19316851e+02 8.11417560e+02 9.57016600e+02 1.07684350e+02\n", " 3.40406090e+02 1.32106640e+03 1.30502233e+04 6.95884680e+03\n", " 1.00000000e+00]]\n" ] } ], "source": [ "print(data.exog[:2,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit and summary" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Generalized Linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: ['y1', 'y2'] No. Observations: 303\n", "Model: GLM Df Residuals: 282\n", "Model Family: Binomial Df Model: 20\n", "Link Function: logit Scale: 1.0\n", "Method: IRLS Log-Likelihood: -2998.6\n", "Date: Fri, 12 Jun 2020 Deviance: 4078.8\n", "Time: 07:38:59 Pearson chi2: 9.60\n", "No. Iterations: 5 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0168 0.000 -38.749 0.000 -0.018 -0.016\n", "x2 0.0099 0.001 16.505 0.000 0.009 0.011\n", "x3 -0.0187 0.001 -25.182 0.000 -0.020 -0.017\n", "x4 -0.0142 0.000 -32.818 0.000 -0.015 -0.013\n", "x5 0.2545 0.030 8.498 0.000 0.196 0.313\n", "x6 0.2407 0.057 4.212 0.000 0.129 0.353\n", "x7 0.0804 0.014 5.775 0.000 0.053 0.108\n", "x8 -1.9522 0.317 -6.162 0.000 -2.573 -1.331\n", "x9 -0.3341 0.061 -5.453 0.000 -0.454 -0.214\n", "x10 -0.1690 0.033 -5.169 0.000 -0.233 -0.105\n", "x11 0.0049 0.001 3.921 0.000 0.002 0.007\n", "x12 -0.0036 0.000 -15.878 0.000 -0.004 -0.003\n", "x13 -0.0141 0.002 -7.391 0.000 -0.018 -0.010\n", "x14 -0.0040 0.000 -8.450 0.000 -0.005 -0.003\n", "x15 -0.0039 0.001 -4.059 0.000 -0.006 -0.002\n", "x16 0.0917 0.015 6.321 0.000 0.063 0.120\n", "x17 0.0490 0.007 6.574 0.000 0.034 0.064\n", "x18 0.0080 0.001 5.362 0.000 0.005 0.011\n", "x19 0.0002 2.99e-05 7.428 0.000 0.000 0.000\n", "x20 -0.0022 0.000 -6.445 0.000 -0.003 -0.002\n", "const 2.9589 1.547 1.913 0.056 -0.073 5.990\n", "==============================================================================\n" ] } ], "source": [ "glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial())\n", "res = glm_binom.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quantities of interest" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of trials: 807.0\n", "Parameters: [-1.68150366e-02 9.92547661e-03 -1.87242148e-02 -1.42385609e-02\n", " 2.54487173e-01 2.40693664e-01 8.04086739e-02 -1.95216050e+00\n", " -3.34086475e-01 -1.69022168e-01 4.91670212e-03 -3.57996435e-03\n", " -1.40765648e-02 -4.00499176e-03 -3.90639579e-03 9.17143006e-02\n", " 4.89898381e-02 8.04073890e-03 2.22009503e-04 -2.24924861e-03\n", " 2.95887793e+00]\n", "T-values: [-38.74908321 16.50473627 -25.1821894 -32.81791308 8.49827113\n", " 4.21247925 5.7749976 -6.16191078 -5.45321673 -5.16865445\n", " 3.92119964 -15.87825999 -7.39093058 -8.44963886 -4.05916246\n", " 6.3210987 6.57434662 5.36229044 7.42806363 -6.44513698\n", " 1.91301155]\n" ] } ], "source": [ "print('Total number of trials:', data.endog[0].sum())\n", "print('Parameters: ', res.params)\n", "print('T-values: ', res.tvalues)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] } ], "source": [ "means = data.exog.mean(axis=0)\n", "means25 = means.copy()\n", "means25[0] = stats.scoreatpercentile(data.exog[:,0], 25)\n", "means75 = means.copy()\n", "means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:,0], 75)\n", "resp_25 = res.predict(means25)\n", "resp_75 = res.predict(means75)\n", "diff = resp_75 - resp_25" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The interquartile first difference for the percentage of low income households in a school district is:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-11.8753%\n" ] } ], "source": [ "print(\"%2.4f%%\" % (diff*100))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plots\n", "\n", " We extract information that will be used to draw some interesting plots: " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nobs = res.nobs\n", "y = data.endog[:,0]/data.endog.sum(1)\n", "yhat = res.mu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot yhat vs y:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.graphics.api import abline_plot" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.scatter(yhat, y)\n", "line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit()\n", "abline_plot(model_results=line_fit, ax=ax)\n", "\n", "\n", "ax.set_title('Model Fit Plot')\n", "ax.set_ylabel('Observed values')\n", "ax.set_xlabel('Fitted values');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot yhat vs. Pearson residuals:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Fitted values')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.scatter(yhat, res.resid_pearson)\n", "ax.hlines(0, 0, 1)\n", "ax.set_xlim(0, 1)\n", "ax.set_title('Residual Dependence Plot')\n", "ax.set_ylabel('Pearson Residuals')\n", "ax.set_xlabel('Fitted values')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Histogram of standardized deviance residuals:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAGUhJREFUeJzt3X+0XGV97/H3hxBIyg8Bc8RAEo4CYgP3GtpjhAu1NEINPxRo1Yo/ChUbW0sLra2A9rZhXdsbVxX0Lrt0xYIJgiCXH4WCFlPAm0tF6IkGTAgWhGASAzn8iEkA6Q187x/Pc+pmnDkzc87MmXOefF5rnXVm72fP3t+9Z+Yzez97z4wiAjMzm/x263UBZmbWGQ50M7NCONDNzArhQDczK4QD3cysEA50M7NCONAzSWslndDrOnpJ0pmSNkjaIenoXtczTNJiSVd1cH7nSLq7MrxD0us7Nf88z29L+nCL054gaWMHlvl+Sd8a63wmombr1s72brKcjjwWvbJLBLqk9ZJOrBn3ihd1RBwZEd9uMp9+SSFp9y6V2mufAc6LiL0j4vut3knSMkmf6mJdXZXX99Fe1zFWEXF1RPxmr+vohpLXrZN2iUCfLCbAG8UhwNoe19BRkqb0ugZLJsDzu3gO9Ky6Fy9pvqRBSdskPSnp0jzZyvx/az5MP1bSbpL+UtLjkrZIulLSqyrz/d3c9rSk/16znMWSrpd0laRtwDl52fdI2ipps6QvSNqjMr+Q9FFJD0vaLul/SDpU0ndyvddVp69Zx7q1StpT0g5gCnC/pB/Vua8kXZbvt03SDyQdJWkR8H7g43mb/FOe/iJJP8o1PijpzMq8zpF0t6TPSHpW0mOSTq60v07S/8n3XQHMqKnlf0t6QtJPJa2UdGSlbZmkL0r6hqTngN+Q9GpJt+S67wMOrZlfSDpM0kF5HYb/npcUlek+JGldrvl2SYdU2k6S9FCu6QuA6j0Gedrpuc5nJT0IvLmm/SBJN0gaytvmTyrjX5B0QGXaoyU9JWmqfrEr6fNKXWjbJK2S9GuVtsX5uXJl3s5rJQ1U2mdLujHX8HRep6bboWY9ho9oz5X0Y+DOPP6Y/HzdKul+Vbo68zo8mmt6TNL7K+Or69Zwe6umi041R9aSfi/Xvz0v6yMjPFYXStqUp/2hpLc1mnZCiIji/4D1wIk1484B7q43DXAP8MF8e2/gmHy7Hwhg98r9PgQ8Arw+T3sj8NXcNhfYARwP7EHq0vh/leUszsNnkN5cpwO/ChwD7J6Xtw64oLK8AG4G9gWOBF4E7sjLfxXwIHB2g+3QsNbKvA9rcN+3A6uA/Ugvnl8GZua2ZcCnaqZ/N3BQXq/fAZ6rTH9OXu/fJ72J/CHwE0CV7X8psCfwVmA7cFXNeuyT2z8HrK60LQN+ChyXlz0NuBa4DtgLOArYVPPY111v4Grgmnz79Lztfjk/Nn8JfCe3zcg1vguYCvwpsBP4cINtuQT4v8ABwGxgDbAxt+2Wt/Nf5efM64FHgbfn9juB36/M6++ALzV4Tn8AeHWu92PAE8C0ynPvZ8Ap+TH4n8B3c9sU4H7gsrzNpgHHN9sOddazP2/bK/N8pgMHA0/n5e4GnJSH+/I024Aj8v1nAkfWrluz7Z3X7ao6deyeh08lvakL+HXgeeBXctsJlcfiCGADcFBlPof2Os9GzLpeFzAuK5nCegewtfL3PI0DfSVwCTCjwRO0Guh3AB+tDB9BCqvdSS/KayptvwT8B68M9JVNar8AuKkyHMBxleFVwIWV4c8Cn2swr4a1VubdKNAXAP9OerPZraZtGTWBXuf+q4HT8+1zgEdqtksArwXm5BfnXpX2r1VfoDXz3S/f91WVWq6stE/J6/jGyri/pUmgAxfmbTs9D38TOLfSvlt+Dh0C/C45DHObgI00DvRHgYWV4UX8PETeAvy4ZvqLga/k2x8G7qwsZwPw1sp2vbveMnP7s8CbKs+9f6m0zQVeyLePBYaoPM8r0zXcDnWm7c/b9vU12/WrNdPdDpxNCvStwG8Pb/fKNP+5bs22N00CvU6d/wicn2+fUHksDgO2ACcCU0d6fk+Uv12py+WMiNhv+A/46AjTngu8AXhI0r9JOm2EaQ8CHq8MP04K8wNz24bhhoh4nrQ3UrWhOiDpDZJuzV0K20jhM6PmPk9Wbr9QZ3jvUdQ6ooi4E/gC8PfAFklLJe3baHqlrqbV+bB6K2nPuLoeT1Tm/Xy+uXeu8dmIeK6mzuH5TpG0RKk7ZxvpjZiaeVe3aV9ex+q46jaoV/vJwPmk58wLefQhwOcr6/MMKUgO5hcf56hZXq2DaFzPIcBBw8vJy/oEP3+MbgCOlTSTdPTyMmlvv956/HnuWvhpns+raPAYkEJ5Wu6WmA08HhE768x2pO3QSHVdDwHeXbN+x5OO3p4jHc39AbBZ0m2S3lhnfu1u71eQdLKk70p6Ji//FH7xNUZEPELaoVpMes5fK+mgVpfTC7tSoLcsIh6OiLOA1wCfBq6XtBfpXb7WT0hP0mHDe5hPApuBWcMNkqaTDoFfsbia4S8CDwGHR8S+pBdzw/7YNo1Ua1MR8b8i4ldJe3NvAP5iuKk6Xe5T/TJwHvDq/Aa6htbWYzOwf97e1TqHvY902H8iKaD6hxdbLbVye4i0jrMbzO8VJB0BLAfeExHVkNgAfKS6UxAR0yPiO7nm2ZV5qGZ59daxUT0bgMdqlrNPRJwCEBHPAt8iBd/7gGtzoNWux68BHwfeA+yfH4Of0tpjsAGYo/onMUfaDo1U69tA2kOv3n+viFiS1+/2iDiJ1N3yEOl5VKvZ9n6OdNQ37LWVafckvSl+Bjgwb5dv0GC7RMTXIuJ40usmSHkwYTnQ65D0AUl9EfEy6RAQ0p7QUP5fvWb5GuBPlU7k7U3ao/563ru5HniHpP+mdKJyMc1fUPuQ+hF35L2TP+zUejWpdUSS3izpLZKmkl4wPyNtC0hvCNVtMvzmN5Tv+3ukPfSmIuJxYBC4RNIeko4H3lGZZB/SeYOnSS/av20yv5dI5woWS/olSXNJh/f11nFf0vmJT0bE3TXNXwIuVj4Bq3Qy+d257TbgSEm/lUPwT6iESB3X5XntL2kW8MeVtvuA7flk3PR8RHKUpOqJ06+Ruh3elW/Xsw/pjWwI2F3SX5HOu7TiPlJoLpG0l6Rpko7LbSNth1ZcRXpNvD2v2zSla79nSTpQ0un5zfxFUjfpy3Xm0Wx7rwbeKmmO0gUKF1fa9iCdexkCduajsbqXQ0o6QtKC/CbwM9LRb716JgwHen0LgbVKV358HnhvRLyQuwb+BvjXfLh4DHAF8FVSv/tjpAf+jwEiYm2+fS3pBbKD1Cf34gjL/nPSntd20t7J1zu4Xg1rbcG+uZ5nSV0ET5NOyAFcDszN2+QfI+JBUl/+PaSw/y/Av7ZR5/tIfcnPAH9NOqk27Mq8/E2kE8DfbWF+55G6c54g9bF/pcF0v0I6r3CZKle7AETETaS9s2tzV88a4OTc9hTpJPAS0nY5nJHX95K8Do+R9ra/OtyQ34BOA+bl9qeAfyAdjQy7JS/jiYi4v8Eybgf+mXTe43HSY91St0Su4R2kPuQfk/qnfye3NdwOLc57A+kI6xOkUN1AOtLbLf/9GelI8hnSCctf2KFptr0jYgXpdfMA6TzIrZW27aQ3gOtIz+X3kbZnPXvmZTxFeu68hle+OUw4w1cV2DjIe8VbSd0pj/W6HjMri/fQu0zSO/Kh/l6kfrsf8PMTeWZmHeNA777TSYeQPyEdGr633kksM7OxcpeLmVkhvIduZlaIcf2ynBkzZkR/f/94LtLMbNJbtWrVUxHR12y6cQ30/v5+BgcHx3ORZmaTnqQRP908zF0uZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIRzoZmaFGNdPitqup/+i29qafv2SU7tUiVn5vIduZlaIlgM9//7f9yXdmodfJ+leSY9I+nr+zUwzM+uRdvbQzwfWVYY/DVwWEYeRfpvv3E4WZmZm7Wkp0PMvk59K+rFaJAlYQPpVe4DlwBndKNDMzFrT6h7654CPAy/n4VcDWyNiZx7eCBxc746SFkkalDQ4NDQ0pmLNzKyxpoEu6TRgS0SsGs0CImJpRAxExEBfX9PvZzczs1Fq5bLF44B3SjoFmAbsC3we2E/S7nkvfRawqXtlmplZM0330CPi4oiYFRH9wHuBOyPi/cBdwLvyZGcDN3etSjMza2os16FfCPyZpEdIfeqXd6YkMzMbjbY+KRoR3wa+nW8/CszvfElmZjYa/qSomVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWiFZ+JHqapPsk3S9praRL8vhlkh6TtDr/zet+uWZm1kgrv1j0IrAgInZImgrcLembue0vIuL67pVnZmatahroERHAjjw4Nf9FN4syM7P2tdSHLmmKpNXAFmBFRNybm/5G0gOSLpO0Z4P7LpI0KGlwaGioQ2WbmVmtlgI9Il6KiHnALGC+pKOAi4E3Am8GDgAubHDfpRExEBEDfX19HSrbzMxqtXWVS0RsBe4CFkbE5kheBL4CzO9GgWZm1ppWrnLpk7Rfvj0dOAl4SNLMPE7AGcCabhZqZmYja+Uql5nAcklTSG8A10XErZLulNQHCFgN/EEX6zQzsyZaucrlAeDoOuMXdKUiMzMbFX9S1MysEA50M7NCONDNzArhQDczK0QrV7mY/af+i27rdQlm1oD30M3MCuFANzMrhAPdzKwQDnQzs0I40M3MCuFANzMrhAPdzKwQDnQzs0I40M3MCuFPiu7C/KlPs7J4D93MrBCt/ATdNEn3Sbpf0lpJl+Txr5N0r6RHJH1d0h7dL9fMzBppZQ/9RWBBRLwJmAcslHQM8Gngsog4DHgWOLd7ZZqZWTNNAz2SHXlwav4LYAFwfR6/nPRD0WZm1iMt9aFLmiJpNbAFWAH8CNgaETvzJBuBgxvcd5GkQUmDQ0NDnajZzMzqaCnQI+KliJgHzALmA29sdQERsTQiBiJioK+vb5RlmplZM21d5RIRW4G7gGOB/SQNX/Y4C9jU4drMzKwNrVzl0idpv3x7OnASsI4U7O/Kk50N3NytIs3MrLlWPlg0E1guaQrpDeC6iLhV0oPAtZI+BXwfuLyLdZqZWRNNAz0iHgCOrjP+UVJ/upmZTQD+pKiZWSEc6GZmhXCgm5kVwoFuZlYIf32uTSjtfqXv+iWndqkSs8nHe+hmZoVwoJuZFcKBbmZWCAe6mVkhfFK0IP6NULNdm/fQzcwK4UA3MyuEA93MrBAOdDOzQvikqE1q/mSp2c95D93MrBCt/ATdbEl3SXpQ0lpJ5+fxiyVtkrQ6/53S/XLNzKyRVrpcdgIfi4jvSdoHWCVpRW67LCI+073yzMysVa38BN1mYHO+vV3SOuDgbhdmZmbtaasPXVI/6fdF782jzpP0gKQrJO3f4drMzKwNLQe6pL2BG4ALImIb8EXgUGAeaQ/+sw3ut0jSoKTBoaGhDpRsZmb1tBTokqaSwvzqiLgRICKejIiXIuJl4MvA/Hr3jYilETEQEQN9fX2dqtvMzGq0cpWLgMuBdRFxaWX8zMpkZwJrOl+emZm1qpWrXI4DPgj8QNLqPO4TwFmS5gEBrAc+0pUKzcysJa1c5XI3oDpN3+h8OWZmNlr+pKiZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSH8fei2S/H3p1vJvIduZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSEc6GZmhWjlN0VnS7pL0oOS1ko6P48/QNIKSQ/n//t3v1wzM2uklT30ncDHImIucAzwR5LmAhcBd0TE4cAdedjMzHqkaaBHxOaI+F6+vR1YBxwMnA4sz5MtB87oVpFmZtZcW1+fK6kfOBq4FzgwIjbnpieAAxvcZxGwCGDOnDmjrXOX1O5XvZrZrq3lk6KS9gZuAC6IiG3VtogIIOrdLyKWRsRARAz09fWNqVgzM2uspUCXNJUU5ldHxI159JOSZub2mcCW7pRoZmataOUqFwGXA+si4tJK0y3A2fn22cDNnS/PzMxa1Uof+nHAB4EfSFqdx30CWAJcJ+lc4HHgPd0p0czMWtE00CPibkANmt/W2XLMzGy0/ElRM7NCONDNzArhQDczK4QD3cysEA50M7NCONDNzArhQDczK4QD3cysEA50M7NCtPX1uWa7mtF8hfH6Jad2oRKz5ryHbmZWCAe6mVkhHOhmZoVwoJuZFcKBbmZWCAe6mVkhWvkJuiskbZG0pjJusaRNklbnv1O6W6aZmTXTyh76MmBhnfGXRcS8/PeNzpZlZmbtahroEbESeGYcajEzszEYSx/6eZIeyF0y+zeaSNIiSYOSBoeGhsawODMzG8loA/2LwKHAPGAz8NlGE0bE0ogYiIiBvr6+US7OzMyaGVWgR8STEfFSRLwMfBmY39myzMysXaMKdEkzK4NnAmsaTWtmZuOj6bctSroGOAGYIWkj8NfACZLmAQGsBz7SxRrNzKwFTQM9Is6qM/ryLtRiZmZj4E+KmpkVwoFuZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlYIB7qZWSEc6GZmhXCgm5kVwoFuZlaIpl+fa431X3Rbr0uwCajd58X6Jad2qRLb1XgP3cysEE0DXdIVkrZIWlMZd4CkFZIezv/3726ZZmbWTCt76MuAhTXjLgLuiIjDgTvysJmZ9VDTQI+IlcAzNaNPB5bn28uBMzpcl5mZtWm0fegHRsTmfPsJ4MBGE0paJGlQ0uDQ0NAoF2dmZs2M+aRoRAQQI7QvjYiBiBjo6+sb6+LMzKyB0Qb6k5JmAuT/WzpXkpmZjcZoA/0W4Ox8+2zg5s6UY2Zmo9XKZYvXAPcAR0jaKOlcYAlwkqSHgRPzsJmZ9VDTT4pGxFkNmt7W4VrMzGwM/ElRM7NCONDNzArhQDczK4QD3cysEA50M7NCONDNzArhQDczK4QD3cysEA50M7NCONDNzArhQDczK4QD3cysEA50M7NCONDNzArhQDczK4QD3cysEE1/4GIkktYD24GXgJ0RMdCJoszMrH1jCvTsNyLiqQ7Mx8zMxsBdLmZmhRhroAfwLUmrJC3qREFmZjY6Y+1yOT4iNkl6DbBC0kMRsbI6QQ76RQBz5swZ4+LMzKyRMe2hR8Sm/H8LcBMwv840SyNiICIG+vr6xrI4MzMbwagDXdJekvYZvg38JrCmU4WZmVl7xtLlciBwk6Th+XwtIv65I1WZmVnbRh3oEfEo8KYO1mJmZmPQievQi9F/0W29LsF2Qe0+79YvObVLldhk5+vQzcwK4UA3MyuEA93MrBAOdDOzQkyak6I+cWQ2On7t7Dq8h25mVggHuplZIRzoZmaFcKCbmRVi0pwUbZc/9WmlmmjP7dHU4xOv3eE9dDOzQjjQzcwK4UA3MyuEA93MrBDFnhQ1s9EZj5Ou3f706kSb/2iWMRreQzczK8SYAl3SQkk/lPSIpIs6VZSZmbVvLD8SPQX4e+BkYC5wlqS5nSrMzMzaM5Y99PnAIxHxaET8B3AtcHpnyjIzs3aN5aTowcCGyvBG4C21E0laBCzKgzsk/XAMy+ymGcBTvS6iTa55fEy2midbvdCkZn26uwsf5fzb2s5jXIdDWpmo61e5RMRSYGm3lzNWkgYjYqDXdbTDNY+PyVbzZKsXXHOnjKXLZRMwuzI8K48zM7MeGEug/xtwuKTXSdoDeC9wS2fKMjOzdo26yyUidko6D7gdmAJcERFrO1bZ+Jvw3UJ1uObxMdlqnmz1gmvuCEVEr2swM7MO8CdFzcwK4UA3MyuEA71C0t9JekjSA5JukrRfr2tqRtK7Ja2V9LKkCXUJVdVk+5oISVdI2iJpTa9raZWk2ZLukvRgfk6c3+uampE0TdJ9ku7PNV/S65paIWmKpO9LurXXtVQ50F9pBXBURPxX4N+Bi3tcTyvWAL8FrOx1IY1M0q+JWAYs7HURbdoJfCwi5gLHAH80Cbbzi8CCiHgTMA9YKOmYHtfUivOBdb0uopYDvSIivhURO/Pgd0nX1k9oEbEuIibqp2+HTbqviYiIlcAzva6jHRGxOSK+l29vJwXOwb2tamSR7MiDU/PfhL5SQ9Is4FTgH3pdSy0HemMfAr7Z6yIKUe9rIiZ00Ex2kvqBo4F7e1tJc7n7YjWwBVgRERO95s8BHwde7nUhtXa5H7iQ9C/Aa+s0fTIibs7TfJJ0+Hr1eNbWSCs1mw2TtDdwA3BBRGzrdT3NRMRLwLx8zuomSUdFxIQ8dyHpNGBLRKySdEKv66m1ywV6RJw4Urukc4DTgLfFBLlIv1nNk4C/JmKcSJpKCvOrI+LGXtfTjojYKuku0rmLCRnowHHAOyWdAkwD9pV0VUR8oMd1Ae5yeQVJC0mHUu+MiOd7XU9B/DUR40CSgMuBdRFxaa/raYWkvuGrySRNB04CHuptVY1FxMURMSsi+knP4zsnSpiDA73WF4B9gBWSVkv6Uq8LakbSmZI2AscCt0m6vdc11conmoe/JmIdcN1E/5oISdcA9wBHSNoo6dxe19SC44APAgvy83d13pOcyGYCd0l6gPTGvyIiJtSlgJOJP/pvZlYI76GbmRXCgW5mVggHuplZIRzoZmaFcKCbmRXCgW5mVggHuplZIf4/PYbBAXLJQvMAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy import stats\n", "\n", "fig, ax = plt.subplots()\n", "\n", "resid = res.resid_deviance.copy()\n", "resid_std = stats.zscore(resid)\n", "ax.hist(resid_std, bins=25)\n", "ax.set_title('Histogram of standardized deviance residuals');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "QQ Plot of Deviance Residuals:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels import graphics\n", "graphics.gofplots.qqplot(resid, line='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GLM: Gamma for proportional count response\n", "\n", "### Load data\n", "\n", " In the example above, we printed the ``NOTE`` attribute to learn about the\n", " Star98 dataset. Statsmodels datasets ships with other useful information. For\n", " example: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "This data is based on the example in Gill and describes the proportion of\n", "voters who voted Yes to grant the Scottish Parliament taxation powers.\n", "The data are divided into 32 council districts. This example's explanatory\n", "variables include the amount of council tax collected in pounds sterling as\n", "of April 1997 per two adults before adjustments, the female percentage of\n", "total claims for unemployment benefits as of January, 1998, the standardized\n", "mortality rate (UK is 100), the percentage of labor force participation,\n", "regional GDP, the percentage of children aged 5 to 15, and an interaction term\n", "between female unemployment and the council tax.\n", "\n", "The original source files and variable information are included in\n", "/scotland/src/\n", "\n" ] } ], "source": [ "print(sm.datasets.scotland.DESCRLONG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Load the data and add a constant to the exogenous variables:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[7.12000e+02 2.10000e+01 1.05000e+02 8.24000e+01 1.35660e+04 1.23000e+01\n", " 1.49520e+04 1.00000e+00]\n", " [6.43000e+02 2.65000e+01 9.70000e+01 8.02000e+01 1.35660e+04 1.53000e+01\n", " 1.70395e+04 1.00000e+00]\n", " [6.79000e+02 2.83000e+01 1.13000e+02 8.63000e+01 9.61100e+03 1.39000e+01\n", " 1.92157e+04 1.00000e+00]\n", " [8.01000e+02 2.71000e+01 1.09000e+02 8.04000e+01 9.48300e+03 1.36000e+01\n", " 2.17071e+04 1.00000e+00]\n", " [7.53000e+02 2.20000e+01 1.15000e+02 6.47000e+01 9.26500e+03 1.46000e+01\n", " 1.65660e+04 1.00000e+00]]\n", "[60.3 52.3 53.4 57. 68.7]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "data2 = sm.datasets.scotland.load()\n", "data2.exog = sm.add_constant(data2.exog, prepend=False)\n", "print(data2.exog[:5,:])\n", "print(data2.endog[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit and summary" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/genmod/generalized_linear_model.py:244: DomainWarning: The inverse_power link function does not respect the domain of the Gamma family.\n", " DomainWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Generalized Linear Model Regression Results \n", "=================================================================================\n", "Dep. Variable: y No. Observations: 32\n", "Model: GLM Df Residuals: 24\n", "Model Family: Gamma Df Model: 7\n", "Link Function: inverse_power Scale: 0.0035842831734956906\n", "Method: IRLS Log-Likelihood: -83.017\n", "Date: Fri, 12 Jun 2020 Deviance: 0.087389\n", "Time: 07:39:08 Pearson chi2: 0.0860\n", "No. Iterations: 4 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 4.962e-05 1.62e-05 3.060 0.002 1.78e-05 8.14e-05\n", "x2 0.0020 0.001 3.824 0.000 0.001 0.003\n", "x3 -7.181e-05 2.71e-05 -2.648 0.008 -0.000 -1.87e-05\n", "x4 0.0001 4.06e-05 2.757 0.006 3.23e-05 0.000\n", "x5 -1.468e-07 1.24e-07 -1.187 0.235 -3.89e-07 9.56e-08\n", "x6 -0.0005 0.000 -2.159 0.031 -0.001 -4.78e-05\n", "x7 -2.427e-06 7.46e-07 -3.253 0.001 -3.89e-06 -9.65e-07\n", "const -0.0178 0.011 -1.548 0.122 -0.040 0.005\n", "==============================================================================\n" ] } ], "source": [ "glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma())\n", "glm_results = glm_gamma.fit()\n", "print(glm_results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GLM: Gaussian distribution with a noncanonical link\n", "\n", "### Artificial data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nobs2 = 100\n", "x = np.arange(nobs2)\n", "np.random.seed(54321)\n", "X = np.column_stack((x,x**2))\n", "X = sm.add_constant(X, prepend=False)\n", "lny = np.exp(-(.03*x + .0001*x**2 - 1.0)) + .001 * np.random.rand(nobs2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit and summary" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Generalized Linear Model Regression Results \n", "=================================================================================\n", "Dep. Variable: y No. Observations: 100\n", "Model: GLM Df Residuals: 97\n", "Model Family: Gaussian Df Model: 2\n", "Link Function: log Scale: 1.053114255880704e-07\n", "Method: IRLS Log-Likelihood: 662.92\n", "Date: Fri, 12 Jun 2020 Deviance: 1.0215e-05\n", "Time: 07:39:09 Pearson chi2: 1.02e-05\n", "No. Iterations: 5 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 -0.0300 5.6e-06 -5361.333 0.000 -0.030 -0.030\n", "x2 -9.939e-05 1.05e-07 -951.097 0.000 -9.96e-05 -9.92e-05\n", "const 1.0003 5.39e-05 1.86e+04 0.000 1.000 1.000\n", "==============================================================================\n" ] } ], "source": [ "gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log))\n", "gauss_log_results = gauss_log.fit()\n", "print(gauss_log_results.summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generalized Linear Models (Formula)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.\n", "\n", "To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from __future__ import print_function\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "star98 = sm.datasets.star98.load_pandas().data\n", "formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n", " PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n", "dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n", " 'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',\n", " 'PERSPENK', 'PTRATIO', 'PCTAF']].copy()\n", "endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))\n", "del dta['NABOVE']\n", "dta['SUCCESS'] = endog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we fit the GLM model:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Generalized Linear Model Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>SUCCESS</td> <th> No. Observations: </th> <td> 303</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 282</td> \n", "</tr>\n", "<tr>\n", " <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 20</td> \n", "</tr>\n", "<tr>\n", " <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td>1.0</td> \n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -189.70</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> Deviance: </th> <td> 380.66</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:45:35</td> <th> Pearson chi2: </th> <td> 8.48</td> \n", "</tr>\n", "<tr>\n", " <th>No. Iterations:</th> <td>5</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 0.4037</td> <td> 25.036</td> <td> 0.016</td> <td> 0.987</td> <td> -48.665</td> <td> 49.472</td>\n", "</tr>\n", "<tr>\n", " <th>LOWINC</th> <td> -0.0204</td> <td> 0.010</td> <td> -1.982</td> <td> 0.048</td> <td> -0.041</td> <td> -0.000</td>\n", "</tr>\n", "<tr>\n", " <th>PERASIAN</th> <td> 0.0159</td> <td> 0.017</td> <td> 0.910</td> <td> 0.363</td> <td> -0.018</td> <td> 0.050</td>\n", "</tr>\n", "<tr>\n", " <th>PERBLACK</th> <td> -0.0198</td> <td> 0.020</td> <td> -1.004</td> <td> 0.316</td> <td> -0.058</td> <td> 0.019</td>\n", "</tr>\n", "<tr>\n", " <th>PERHISP</th> <td> -0.0096</td> <td> 0.010</td> <td> -0.951</td> <td> 0.341</td> <td> -0.029</td> <td> 0.010</td>\n", "</tr>\n", "<tr>\n", " <th>PCTCHRT</th> <td> -0.0022</td> <td> 0.022</td> <td> -0.103</td> <td> 0.918</td> <td> -0.045</td> <td> 0.040</td>\n", "</tr>\n", "<tr>\n", " <th>PCTYRRND</th> <td> -0.0022</td> <td> 0.006</td> <td> -0.348</td> <td> 0.728</td> <td> -0.014</td> <td> 0.010</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE</th> <td> 0.1068</td> <td> 0.787</td> <td> 0.136</td> <td> 0.892</td> <td> -1.436</td> <td> 1.650</td>\n", "</tr>\n", "<tr>\n", " <th>AVYRSEXP</th> <td> -0.0411</td> <td> 1.176</td> <td> -0.035</td> <td> 0.972</td> <td> -2.346</td> <td> 2.264</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE:AVYRSEXP</th> <td> -0.0031</td> <td> 0.054</td> <td> -0.057</td> <td> 0.954</td> <td> -0.108</td> <td> 0.102</td>\n", "</tr>\n", "<tr>\n", " <th>AVSALK</th> <td> 0.0131</td> <td> 0.295</td> <td> 0.044</td> <td> 0.965</td> <td> -0.566</td> <td> 0.592</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE:AVSALK</th> <td> -0.0019</td> <td> 0.013</td> <td> -0.145</td> <td> 0.885</td> <td> -0.028</td> <td> 0.024</td>\n", "</tr>\n", "<tr>\n", " <th>AVYRSEXP:AVSALK</th> <td> 0.0008</td> <td> 0.020</td> <td> 0.038</td> <td> 0.970</td> <td> -0.039</td> <td> 0.041</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE:AVYRSEXP:AVSALK</th> <td> 5.978e-05</td> <td> 0.001</td> <td> 0.068</td> <td> 0.946</td> <td> -0.002</td> <td> 0.002</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK</th> <td> -0.3097</td> <td> 4.233</td> <td> -0.073</td> <td> 0.942</td> <td> -8.606</td> <td> 7.987</td>\n", "</tr>\n", "<tr>\n", " <th>PTRATIO</th> <td> 0.0096</td> <td> 0.919</td> <td> 0.010</td> <td> 0.992</td> <td> -1.792</td> <td> 1.811</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK:PTRATIO</th> <td> 0.0066</td> <td> 0.206</td> <td> 0.032</td> <td> 0.974</td> <td> -0.397</td> <td> 0.410</td>\n", "</tr>\n", "<tr>\n", " <th>PCTAF</th> <td> -0.0143</td> <td> 0.474</td> <td> -0.030</td> <td> 0.976</td> <td> -0.944</td> <td> 0.916</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK:PCTAF</th> <td> 0.0105</td> <td> 0.098</td> <td> 0.107</td> <td> 0.915</td> <td> -0.182</td> <td> 0.203</td>\n", "</tr>\n", "<tr>\n", " <th>PTRATIO:PCTAF</th> <td> -0.0001</td> <td> 0.022</td> <td> -0.005</td> <td> 0.996</td> <td> -0.044</td> <td> 0.044</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK:PTRATIO:PCTAF</th> <td> -0.0002</td> <td> 0.005</td> <td> -0.051</td> <td> 0.959</td> <td> -0.010</td> <td> 0.009</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: SUCCESS No. Observations: 303\n", "Model: GLM Df Residuals: 282\n", "Model Family: Binomial Df Model: 20\n", "Link Function: logit Scale: 1.0\n", "Method: IRLS Log-Likelihood: -189.70\n", "Date: Fri, 12 Jun 2020 Deviance: 380.66\n", "Time: 07:45:35 Pearson chi2: 8.48\n", "No. Iterations: 5 \n", "============================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "--------------------------------------------------------------------------------------------\n", "Intercept 0.4037 25.036 0.016 0.987 -48.665 49.472\n", "LOWINC -0.0204 0.010 -1.982 0.048 -0.041 -0.000\n", "PERASIAN 0.0159 0.017 0.910 0.363 -0.018 0.050\n", "PERBLACK -0.0198 0.020 -1.004 0.316 -0.058 0.019\n", "PERHISP -0.0096 0.010 -0.951 0.341 -0.029 0.010\n", "PCTCHRT -0.0022 0.022 -0.103 0.918 -0.045 0.040\n", "PCTYRRND -0.0022 0.006 -0.348 0.728 -0.014 0.010\n", "PERMINTE 0.1068 0.787 0.136 0.892 -1.436 1.650\n", "AVYRSEXP -0.0411 1.176 -0.035 0.972 -2.346 2.264\n", "PERMINTE:AVYRSEXP -0.0031 0.054 -0.057 0.954 -0.108 0.102\n", "AVSALK 0.0131 0.295 0.044 0.965 -0.566 0.592\n", "PERMINTE:AVSALK -0.0019 0.013 -0.145 0.885 -0.028 0.024\n", "AVYRSEXP:AVSALK 0.0008 0.020 0.038 0.970 -0.039 0.041\n", "PERMINTE:AVYRSEXP:AVSALK 5.978e-05 0.001 0.068 0.946 -0.002 0.002\n", "PERSPENK -0.3097 4.233 -0.073 0.942 -8.606 7.987\n", "PTRATIO 0.0096 0.919 0.010 0.992 -1.792 1.811\n", "PERSPENK:PTRATIO 0.0066 0.206 0.032 0.974 -0.397 0.410\n", "PCTAF -0.0143 0.474 -0.030 0.976 -0.944 0.916\n", "PERSPENK:PCTAF 0.0105 0.098 0.107 0.915 -0.182 0.203\n", "PTRATIO:PCTAF -0.0001 0.022 -0.005 0.996 -0.044 0.044\n", "PERSPENK:PTRATIO:PCTAF -0.0002 0.005 -0.051 0.959 -0.010 0.009\n", "============================================================================================\n", "\"\"\"" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n", "mod1.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we define a function to operate customized data transformation using the formula framework:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Generalized Linear Model Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>SUCCESS</td> <th> No. Observations: </th> <td> 303</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 282</td> \n", "</tr>\n", "<tr>\n", " <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 20</td> \n", "</tr>\n", "<tr>\n", " <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td>1.0</td> \n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -189.70</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> Deviance: </th> <td> 380.66</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:45:36</td> <th> Pearson chi2: </th> <td> 8.48</td> \n", "</tr>\n", "<tr>\n", " <th>No. Iterations:</th> <td>5</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 0.4037</td> <td> 25.036</td> <td> 0.016</td> <td> 0.987</td> <td> -48.665</td> <td> 49.472</td>\n", "</tr>\n", "<tr>\n", " <th>double_it(LOWINC)</th> <td> -0.0102</td> <td> 0.005</td> <td> -1.982</td> <td> 0.048</td> <td> -0.020</td> <td> -0.000</td>\n", "</tr>\n", "<tr>\n", " <th>PERASIAN</th> <td> 0.0159</td> <td> 0.017</td> <td> 0.910</td> <td> 0.363</td> <td> -0.018</td> <td> 0.050</td>\n", "</tr>\n", "<tr>\n", " <th>PERBLACK</th> <td> -0.0198</td> <td> 0.020</td> <td> -1.004</td> <td> 0.316</td> <td> -0.058</td> <td> 0.019</td>\n", "</tr>\n", "<tr>\n", " <th>PERHISP</th> <td> -0.0096</td> <td> 0.010</td> <td> -0.951</td> <td> 0.341</td> <td> -0.029</td> <td> 0.010</td>\n", "</tr>\n", "<tr>\n", " <th>PCTCHRT</th> <td> -0.0022</td> <td> 0.022</td> <td> -0.103</td> <td> 0.918</td> <td> -0.045</td> <td> 0.040</td>\n", "</tr>\n", "<tr>\n", " <th>PCTYRRND</th> <td> -0.0022</td> <td> 0.006</td> <td> -0.348</td> <td> 0.728</td> <td> -0.014</td> <td> 0.010</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE</th> <td> 0.1068</td> <td> 0.787</td> <td> 0.136</td> <td> 0.892</td> <td> -1.436</td> <td> 1.650</td>\n", "</tr>\n", "<tr>\n", " <th>AVYRSEXP</th> <td> -0.0411</td> <td> 1.176</td> <td> -0.035</td> <td> 0.972</td> <td> -2.346</td> <td> 2.264</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE:AVYRSEXP</th> <td> -0.0031</td> <td> 0.054</td> <td> -0.057</td> <td> 0.954</td> <td> -0.108</td> <td> 0.102</td>\n", "</tr>\n", "<tr>\n", " <th>AVSALK</th> <td> 0.0131</td> <td> 0.295</td> <td> 0.044</td> <td> 0.965</td> <td> -0.566</td> <td> 0.592</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE:AVSALK</th> <td> -0.0019</td> <td> 0.013</td> <td> -0.145</td> <td> 0.885</td> <td> -0.028</td> <td> 0.024</td>\n", "</tr>\n", "<tr>\n", " <th>AVYRSEXP:AVSALK</th> <td> 0.0008</td> <td> 0.020</td> <td> 0.038</td> <td> 0.970</td> <td> -0.039</td> <td> 0.041</td>\n", "</tr>\n", "<tr>\n", " <th>PERMINTE:AVYRSEXP:AVSALK</th> <td> 5.978e-05</td> <td> 0.001</td> <td> 0.068</td> <td> 0.946</td> <td> -0.002</td> <td> 0.002</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK</th> <td> -0.3097</td> <td> 4.233</td> <td> -0.073</td> <td> 0.942</td> <td> -8.606</td> <td> 7.987</td>\n", "</tr>\n", "<tr>\n", " <th>PTRATIO</th> <td> 0.0096</td> <td> 0.919</td> <td> 0.010</td> <td> 0.992</td> <td> -1.792</td> <td> 1.811</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK:PTRATIO</th> <td> 0.0066</td> <td> 0.206</td> <td> 0.032</td> <td> 0.974</td> <td> -0.397</td> <td> 0.410</td>\n", "</tr>\n", "<tr>\n", " <th>PCTAF</th> <td> -0.0143</td> <td> 0.474</td> <td> -0.030</td> <td> 0.976</td> <td> -0.944</td> <td> 0.916</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK:PCTAF</th> <td> 0.0105</td> <td> 0.098</td> <td> 0.107</td> <td> 0.915</td> <td> -0.182</td> <td> 0.203</td>\n", "</tr>\n", "<tr>\n", " <th>PTRATIO:PCTAF</th> <td> -0.0001</td> <td> 0.022</td> <td> -0.005</td> <td> 0.996</td> <td> -0.044</td> <td> 0.044</td>\n", "</tr>\n", "<tr>\n", " <th>PERSPENK:PTRATIO:PCTAF</th> <td> -0.0002</td> <td> 0.005</td> <td> -0.051</td> <td> 0.959</td> <td> -0.010</td> <td> 0.009</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: SUCCESS No. Observations: 303\n", "Model: GLM Df Residuals: 282\n", "Model Family: Binomial Df Model: 20\n", "Link Function: logit Scale: 1.0\n", "Method: IRLS Log-Likelihood: -189.70\n", "Date: Fri, 12 Jun 2020 Deviance: 380.66\n", "Time: 07:45:36 Pearson chi2: 8.48\n", "No. Iterations: 5 \n", "============================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "--------------------------------------------------------------------------------------------\n", "Intercept 0.4037 25.036 0.016 0.987 -48.665 49.472\n", "double_it(LOWINC) -0.0102 0.005 -1.982 0.048 -0.020 -0.000\n", "PERASIAN 0.0159 0.017 0.910 0.363 -0.018 0.050\n", "PERBLACK -0.0198 0.020 -1.004 0.316 -0.058 0.019\n", "PERHISP -0.0096 0.010 -0.951 0.341 -0.029 0.010\n", "PCTCHRT -0.0022 0.022 -0.103 0.918 -0.045 0.040\n", "PCTYRRND -0.0022 0.006 -0.348 0.728 -0.014 0.010\n", "PERMINTE 0.1068 0.787 0.136 0.892 -1.436 1.650\n", "AVYRSEXP -0.0411 1.176 -0.035 0.972 -2.346 2.264\n", "PERMINTE:AVYRSEXP -0.0031 0.054 -0.057 0.954 -0.108 0.102\n", "AVSALK 0.0131 0.295 0.044 0.965 -0.566 0.592\n", "PERMINTE:AVSALK -0.0019 0.013 -0.145 0.885 -0.028 0.024\n", "AVYRSEXP:AVSALK 0.0008 0.020 0.038 0.970 -0.039 0.041\n", "PERMINTE:AVYRSEXP:AVSALK 5.978e-05 0.001 0.068 0.946 -0.002 0.002\n", "PERSPENK -0.3097 4.233 -0.073 0.942 -8.606 7.987\n", "PTRATIO 0.0096 0.919 0.010 0.992 -1.792 1.811\n", "PERSPENK:PTRATIO 0.0066 0.206 0.032 0.974 -0.397 0.410\n", "PCTAF -0.0143 0.474 -0.030 0.976 -0.944 0.916\n", "PERSPENK:PCTAF 0.0105 0.098 0.107 0.915 -0.182 0.203\n", "PTRATIO:PCTAF -0.0001 0.022 -0.005 0.996 -0.044 0.044\n", "PERSPENK:PTRATIO:PCTAF -0.0002 0.005 -0.051 0.959 -0.010 0.009\n", "============================================================================================\n", "\"\"\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def double_it(x):\n", " return 2 * x\n", "formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n", " PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n", "mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n", "mod2.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.020395987154800062\n", "-0.020395987154800385\n" ] } ], "source": [ "print(mod1.params[1])\n", "print(mod2.params[1] * 2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp0x7gqf48/97d9c99c-c69f-4d99-99f3-ec1498cc257b vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generalized Least Squares" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from __future__ import print_function\n", "import statsmodels.api as sm\n", "import numpy as np\n", "from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Longley dataset is a time series dataset: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1.00000e+00 8.30000e+01 2.34289e+05 2.35600e+03 1.59000e+03 1.07608e+05\n", " 1.94700e+03]\n", " [1.00000e+00 8.85000e+01 2.59426e+05 2.32500e+03 1.45600e+03 1.08632e+05\n", " 1.94800e+03]\n", " [1.00000e+00 8.82000e+01 2.58054e+05 3.68200e+03 1.61600e+03 1.09773e+05\n", " 1.94900e+03]\n", " [1.00000e+00 8.95000e+01 2.84599e+05 3.35100e+03 1.65000e+03 1.10929e+05\n", " 1.95000e+03]\n", " [1.00000e+00 9.62000e+01 3.28975e+05 2.09900e+03 3.09900e+03 1.12075e+05\n", " 1.95100e+03]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "data = sm.datasets.longley.load()\n", "data.exog = sm.add_constant(data.exog)\n", "print(data.exog[:5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " Let's assume that the data is heteroskedastic and that we know\n", " the nature of the heteroskedasticity. We can then define\n", " `sigma` and use it to give us a GLS model\n", "\n", " First we will obtain the residuals from an OLS fit" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ols_resid = sm.OLS(data.endog, data.exog).fit().resid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assume that the error terms follow an AR(1) process with a trend:\n", "\n", "$\\epsilon_i = \\beta_0 + \\rho\\epsilon_{i-1} + \\eta_i$\n", "\n", "where $\\eta \\sim N(0,\\Sigma^2)$\n", " \n", "and that $\\rho$ is simply the correlation of the residual a consistent estimator for rho is to regress the residuals on the lagged residuals" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.4390229839838662\n", "0.17378444788528696\n" ] } ], "source": [ "resid_fit = sm.OLS(ols_resid[1:], sm.add_constant(ols_resid[:-1])).fit()\n", "print(resid_fit.tvalues[1])\n", "print(resid_fit.pvalues[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " While we don't have strong evidence that the errors follow an AR(1)\n", " process we continue" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rho = resid_fit.params[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we know, an AR(1) process means that near-neighbors have a stronger\n", " relation so we can give this structure by using a toeplitz matrix" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 2, 3, 4],\n", " [1, 0, 1, 2, 3],\n", " [2, 1, 0, 1, 2],\n", " [3, 2, 1, 0, 1],\n", " [4, 3, 2, 1, 0]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.linalg import toeplitz\n", "\n", "toeplitz(range(5))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "order = toeplitz(range(len(ols_resid)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "so that our error covariance structure is actually rho**order\n", " which defines an autocorrelation structure" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sigma = rho**order\n", "gls_model = sm.GLS(data.endog, data.exog, sigma=sigma)\n", "gls_results = gls_model.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, the exact rho in this instance is not known so it it might make more sense to use feasible gls, which currently only has experimental support. \n", "\n", "We can use the GLSAR model with one lag, to get to a similar result:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " GLSAR Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.996\n", "Model: GLSAR Adj. R-squared: 0.992\n", "Method: Least Squares F-statistic: 295.2\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 6.09e-09\n", "Time: 07:43:32 Log-Likelihood: -102.04\n", "No. Observations: 15 AIC: 218.1\n", "Df Residuals: 8 BIC: 223.0\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -3.468e+06 8.72e+05 -3.979 0.004 -5.48e+06 -1.46e+06\n", "x1 34.5568 84.734 0.408 0.694 -160.840 229.953\n", "x2 -0.0343 0.033 -1.047 0.326 -0.110 0.041\n", "x3 -1.9621 0.481 -4.083 0.004 -3.070 -0.854\n", "x4 -1.0020 0.211 -4.740 0.001 -1.489 -0.515\n", "x5 -0.0978 0.225 -0.435 0.675 -0.616 0.421\n", "x6 1823.1829 445.829 4.089 0.003 795.100 2851.266\n", "==============================================================================\n", "Omnibus: 1.960 Durbin-Watson: 2.554\n", "Prob(Omnibus): 0.375 Jarque-Bera (JB): 1.423\n", "Skew: 0.713 Prob(JB): 0.491\n", "Kurtosis: 2.508 Cond. No. 4.80e+09\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 4.8e+09. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n", " \"anyway, n=%i\" % int(n))\n" ] } ], "source": [ "glsar_model = sm.GLSAR(data.endog, data.exog, 1)\n", "glsar_results = glsar_model.iterative_fit(1)\n", "print(glsar_results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing gls and glsar results, we see that there are some small\n", " differences in the parameter estimates and the resulting standard\n", " errors of the parameter estimate. This might be do to the numerical\n", " differences in the algorithm, e.g. the treatment of initial conditions,\n", " because of the small number of observations in the longley dataset." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-3.79785490e+06 -1.27656454e+01 -3.80013250e-02 -2.18694871e+00\n", " -1.15177649e+00 -6.80535580e-02 1.99395293e+03]\n", "[-3.46796063e+06 3.45567846e+01 -3.43410090e-02 -1.96214395e+00\n", " -1.00197296e+00 -9.78045986e-02 1.82318289e+03]\n", "[6.70688699e+05 6.94308073e+01 2.62476822e-02 3.82393151e-01\n", " 1.65252692e-01 1.76428334e-01 3.42634628e+02]\n", "[8.71584052e+05 8.47337145e+01 3.28032450e-02 4.80544865e-01\n", " 2.11383871e-01 2.24774369e-01 4.45828748e+02]\n" ] } ], "source": [ "print(gls_results.params)\n", "print(glsar_results.params)\n", "print(gls_results.bse)\n", "print(glsar_results.bse)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Markov switching autoregression models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook provides an example of the use of Markov switching models in Statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.\n", "\n", "This is tested against the Markov-switching models from E-views 8, which can be found at http://www.eviews.com/EViews8/ev8ecswitch_n.html#MarkovAR or the Markov-switching models of Stata 14 which can be found at http://www.stata.com/manuals14/tsmswitch.pdf." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] }, { "ename": "ModuleNotFoundError", "evalue": "No module named 'pandas_datareader'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-67b3d8188f6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# NBER recessions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_datareader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0musrec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'USREC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fred'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1947\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2013\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas_datareader'" ] } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import requests\n", "from io import BytesIO\n", "\n", "# NBER recessions\n", "from pandas_datareader.data import DataReader\n", "from datetime import datetime\n", "usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hamilton (1989) switching model of GNP\n", "\n", "This replicates Hamilton's (1989) seminal paper introducing Markov-switching models. The model is an autoregressive model of order 4 in which the mean of the process switches between two regimes. It can be written:\n", "\n", "$$\n", "y_t = \\mu_{S_t} + \\phi_1 (y_{t-1} - \\mu_{S_{t-1}}) + \\phi_2 (y_{t-2} - \\mu_{S_{t-2}}) + \\phi_3 (y_{t-3} - \\mu_{S_{t-3}}) + \\phi_4 (y_{t-4} - \\mu_{S_{t-4}}) + \\varepsilon_t\n", "$$\n", "\n", "Each period, the regime transitions according to the following matrix of transition probabilities:\n", "\n", "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00} & p_{10} \\\\\n", "p_{01} & p_{11}\n", "\\end{bmatrix}\n", "$$\n", "\n", "where $p_{ij}$ is the probability of transitioning *from* regime $i$, *to* regime $j$.\n", "\n", "The model class is `MarkovAutoregression` in the time-series part of `Statsmodels`. In order to create the model, we must specify the number of regimes with `k_regimes=2`, and the order of the autoregression with `order=4`. The default model also includes switching autoregressive coefficients, so here we also need to specify `switching_ar=False` to avoid that.\n", "\n", "After creation, the model is `fit` via maximum likelihood estimation. Under the hood, good starting parameters are found using a number of steps of the expectation maximization (EM) algorithm, and a quasi-Newton (BFGS) algorithm is applied to quickly find the maximum." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the RGNP data to replicate Hamilton\n", "from statsmodels.tsa.regime_switching.tests.test_markov_autoregression import rgnp\n", "dta_hamilton = pd.Series(rgnp, index=pd.date_range('1951-04-01', '1984-10-01', freq='QS'))\n", "\n", "# Plot the data\n", "dta_hamilton.plot(title='Growth rate of Real GNP', figsize=(12,3))\n", "\n", "# Fit the model\n", "mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)\n", "res_hamilton = mod_hamilton.fit()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>131</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovAutoregression</td> <th> Log Likelihood </th> <td>-181.263</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> AIC </th> <td>380.527</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:40:29</td> <th> BIC </th> <td>406.404</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>04-01-1952</td> <th> HQIC </th> <td>391.042</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-1984</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.3588</td> <td> 0.265</td> <td> -1.356</td> <td> 0.175</td> <td> -0.877</td> <td> 0.160</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 1.1635</td> <td> 0.075</td> <td> 15.614</td> <td> 0.000</td> <td> 1.017</td> <td> 1.310</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.5914</td> <td> 0.103</td> <td> 5.761</td> <td> 0.000</td> <td> 0.390</td> <td> 0.793</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L1</th> <td> 0.0135</td> <td> 0.120</td> <td> 0.112</td> <td> 0.911</td> <td> -0.222</td> <td> 0.249</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L2</th> <td> -0.0575</td> <td> 0.138</td> <td> -0.418</td> <td> 0.676</td> <td> -0.327</td> <td> 0.212</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L3</th> <td> -0.2470</td> <td> 0.107</td> <td> -2.310</td> <td> 0.021</td> <td> -0.457</td> <td> -0.037</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L4</th> <td> -0.2129</td> <td> 0.111</td> <td> -1.926</td> <td> 0.054</td> <td> -0.430</td> <td> 0.004</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.7547</td> <td> 0.097</td> <td> 7.819</td> <td> 0.000</td> <td> 0.565</td> <td> 0.944</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.0959</td> <td> 0.038</td> <td> 2.542</td> <td> 0.011</td> <td> 0.022</td> <td> 0.170</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "================================================================================\n", "Dep. Variable: y No. Observations: 131\n", "Model: MarkovAutoregression Log Likelihood -181.263\n", "Date: Fri, 12 Jun 2020 AIC 380.527\n", "Time: 07:40:29 BIC 406.404\n", "Sample: 04-01-1952 HQIC 391.042\n", " - 10-01-1984 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.3588 0.265 -1.356 0.175 -0.877 0.160\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 1.1635 0.075 15.614 0.000 1.017 1.310\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.5914 0.103 5.761 0.000 0.390 0.793\n", "ar.L1 0.0135 0.120 0.112 0.911 -0.222 0.249\n", "ar.L2 -0.0575 0.138 -0.418 0.676 -0.327 0.212\n", "ar.L3 -0.2470 0.107 -2.310 0.021 -0.457 -0.037\n", "ar.L4 -0.2129 0.111 -1.926 0.054 -0.430 0.004\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.7547 0.097 7.819 0.000 0.565 0.944\n", "p[1->0] 0.0959 0.038 2.542 0.011 0.022 0.170\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical differentiation.\n", "\"\"\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_hamilton.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the filtered and smoothed probabilities of a recession. Filtered refers to an estimate of the probability at time $t$ based on data up to and including time $t$ (but excluding time $t+1, ..., T$). Smoothed refers to an estimate of the probability at time $t$ using all the data in the sample.\n", "\n", "For reference, the shaded periods represent the NBER recessions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'usrec' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-9b61339d54f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m 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"text/plain": [ "<Figure size 504x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, figsize=(7,7))\n", "ax = axes[0]\n", "ax.plot(res_hamilton.filtered_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)\n", "ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n", "ax.set(title='Filtered probability of recession')\n", "\n", "ax = axes[1]\n", "ax.plot(res_hamilton.smoothed_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)\n", "ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n", "ax.set(title='Smoothed probability of recession')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the estimated transition matrix we can calculate the expected duration of a recession versus an expansion." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4.07604792 10.42589264]\n" ] } ], "source": [ "print(res_hamilton.expected_durations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, it is expected that a recession will last about one year (4 quarters) and an expansion about two and a half years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kim, Nelson, and Startz (1998) Three-state Variance Switching\n", "\n", "This model demonstrates estimation with regime heteroskedasticity (switching of variances) and no mean effect. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn.\n", "\n", "The model in question is:\n", "\n", "$$\n", "\\begin{align}\n", "y_t & = \\varepsilon_t \\\\\n", "\\varepsilon_t & \\sim N(0, \\sigma_{S_t}^2)\n", "\\end{align}\n", "$$\n", "\n", "Since there is no autoregressive component, this model can be fit using the `MarkovRegression` class. Since there is no mean effect, we specify `trend='nc'`. There are hypotheized to be three regimes for the switching variances, so we specify `k_regimes=3` and `switching_variance=True` (by default, the variance is assumed to be the same across regimes)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac5a7690>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m 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Plot the dataset\n", "dta_kns[0].plot(title='Excess returns', figsize=(12, 3))\n", "\n", "# Fit the model\n", "mod_kns = sm.tsa.MarkovRegression(dta_kns, k_regimes=3, trend='nc', switching_variance=True)\n", "res_kns = mod_kns.fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res_kns' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-e40786696a94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres_kns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'res_kns' is not defined" ] } ], "source": [ "res_kns.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the probabilities of being in each of the regimes; only in a few periods is a high-variance regime probable." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res_kns' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-0cf0c0fb70ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_kns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothed_marginal_probabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Smoothed probability of a low-variance regime for stock returns'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'res_kns' is not defined" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 720x504 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, figsize=(10,7))\n", "\n", "ax = axes[0]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[0])\n", "ax.set(title='Smoothed probability of a low-variance regime for stock returns')\n", "\n", "ax = axes[1]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[1])\n", "ax.set(title='Smoothed probability of a medium-variance regime for stock returns')\n", "\n", "ax = axes[2]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[2])\n", "ax.set(title='Smoothed probability of a high-variance regime for stock returns')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filardo (1994) Time-Varying Transition Probabilities\n", "\n", "This model demonstrates estimation with time-varying transition probabilities. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn.\n", "\n", "In the above models we have assumed that the transition probabilities are constant across time. Here we allow the probabilities to change with the state of the economy. Otherwise, the model is the same Markov autoregression of Hamilton (1989).\n", "\n", "Each period, the regime now transitions according to the following matrix of time-varying transition probabilities:\n", "\n", "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00,t} & p_{10,t} \\\\\n", "p_{01,t} & p_{11,t}\n", "\\end{bmatrix}\n", "$$\n", "\n", "where $p_{ij,t}$ is the probability of transitioning *from* regime $i$, *to* regime $j$ in period $t$, and is defined to be:\n", "\n", "$$\n", "p_{ij,t} = \\frac{\\exp\\{ x_{t-1}' \\beta_{ij} \\}}{1 + \\exp\\{ x_{t-1}' \\beta_{ij} \\}}\n", "$$\n", "\n", "Instead of estimating the transition probabilities as part of maximum likelihood, the regression coefficients $\\beta_{ij}$ are estimated. These coefficients relate the transition probabilities to a vector of pre-determined or exogenous regressors $x_{t-1}$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac5a7db0>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xac5a7db0>: Failed to establish a new connection: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n", 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establish a new connection: [Errno 111] Connection refused')))", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-9-e3772af85a7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfilardo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdta_filardo\u001b[0m \u001b[0;34m=\u001b[0m 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timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, 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"# See hmt_tvp.opt or Filardo (1994) p. 302\n", "std_ratio = dta_filardo['dlip']['1960-01-01':].std() / dta_filardo['dlip'][:'1959-12-01'].std()\n", "dta_filardo['dlip'][:'1959-12-01'] = dta_filardo['dlip'][:'1959-12-01'] * std_ratio\n", "\n", "dta_filardo['dlleading'] = np.log(dta_filardo['leading']).diff()*100\n", "dta_filardo['dmdlleading'] = dta_filardo['dlleading'] - dta_filardo['dlleading'].mean()\n", "\n", "# Plot the data\n", "dta_filardo['dlip'].plot(title='Standardized growth rate of industrial production', figsize=(13,3))\n", "plt.figure()\n", "dta_filardo['dmdlleading'].plot(title='Leading indicator', figsize=(13,3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The time-varying transition probabilities are specified by the `exog_tvtp` parameter.\n", "\n", "Here we demonstrate another feature of model fitting - the use of a random search for MLE starting parameters. Because Markov switching models are often characterized by many local maxima of the likelihood function, performing an initial optimization step can be helpful to find the best parameters.\n", "\n", "Below, we specify that 20 random perturbations from the starting parameter vector are examined and the best one used as the actual starting parameters. Because of the random nature of the search, we seed the random number generator beforehand to allow replication of the result." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'dta_filardo' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-10-7d8be23b1f6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m mod_filardo = sm.tsa.MarkovAutoregression(\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdta_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dlip'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk_regimes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswitching_ar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m exog_tvtp=sm.add_constant(dta_filardo.ix[1:-1, 'dmdlleading']))\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12345\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'dta_filardo' is not defined" ] } ], "source": [ "mod_filardo = sm.tsa.MarkovAutoregression(\n", " dta_filardo.ix[2:, 'dlip'], k_regimes=2, order=4, switching_ar=False,\n", " exog_tvtp=sm.add_constant(dta_filardo.ix[1:-1, 'dmdlleading']))\n", "\n", "np.random.seed(12345)\n", "res_filardo = mod_filardo.fit(search_reps=20)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res_filardo' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-254b3810b2f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'res_filardo' is not defined" ] } ], "source": [ "res_filardo.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the smoothed probability of the economy operating in a low-production state, and again include the NBER recessions for comparison." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res_filardo' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-12-1a1095e831fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_filardo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmoothed_marginal_probabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musrec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m 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"text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12,3))\n", "\n", "ax.plot(res_filardo.smoothed_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='gray', alpha=0.2)\n", "ax.set_xlim(dta_filardo.index[6], dta_filardo.index[-1])\n", "ax.set(title='Smoothed probability of a low-production state');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the time-varying transition probabilities, we can see how the expected duration of a low-production state changes over time:\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res_filardo' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-13-a30b5c1ed40c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m res_filardo.expected_durations[0].plot(\n\u001b[0m\u001b[1;32m 2\u001b[0m title='Expected duration of a low-production state', figsize=(12,3));\n", "\u001b[0;31mNameError\u001b[0m: name 'res_filardo' is not defined" ] } ], "source": [ "res_filardo.expected_durations[0].plot(\n", " title='Expected duration of a low-production state', figsize=(12,3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "During recessions, the expected duration of a low-production state is much higher than in an expansion." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp4pybx947/bea34191-ee90-4f2d-a8d8-61eddf7f11f3 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Markov switching dynamic regression models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook provides an example of the use of Markov switching models in Statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Federal funds rate with switching intercept\n", "\n", "The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:\n", "\n", "$$r_t = \\mu_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n", "\n", "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n", "\n", "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00} & p_{10} \\\\\n", "1 - p_{00} & 1 - p_{10}\n", "\\end{bmatrix}\n", "$$\n", "\n", "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\sigma^2$.\n", "\n", "The data used in this example can be found at http://www.stata-press.com/data/r14/usmacro." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the federal funds rate data\n", "from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds\n", "dta_fedfunds = pd.Series(fedfunds, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n", "\n", "# Plot the data\n", "dta_fedfunds.plot(title='Federal funds rate', figsize=(12,3))\n", "\n", "# Fit the model\n", "# (a switching mean is the default of the MarkovRegession model)\n", "mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)\n", "res_fedfunds = mod_fedfunds.fit()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>226</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-508.636</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> AIC </th> <td>1027.272</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:41:23</td> <th> BIC </th> <td>1044.375</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>07-01-1954</td> <th> HQIC </th> <td>1034.174</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 3.7088</td> <td> 0.177</td> <td> 20.988</td> <td> 0.000</td> <td> 3.362</td> <td> 4.055</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 9.5568</td> <td> 0.300</td> <td> 31.857</td> <td> 0.000</td> <td> 8.969</td> <td> 10.145</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 4.4418</td> <td> 0.425</td> <td> 10.447</td> <td> 0.000</td> <td> 3.608</td> <td> 5.275</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.9821</td> <td> 0.010</td> <td> 94.443</td> <td> 0.000</td> <td> 0.962</td> <td> 1.002</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.0504</td> <td> 0.027</td> <td> 1.876</td> <td> 0.061</td> <td> -0.002</td> <td> 0.103</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 226\n", "Model: MarkovRegression Log Likelihood -508.636\n", "Date: Fri, 12 Jun 2020 AIC 1027.272\n", "Time: 07:41:23 BIC 1044.375\n", "Sample: 07-01-1954 HQIC 1034.174\n", " - 10-01-2010 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 3.7088 0.177 20.988 0.000 3.362 4.055\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 9.5568 0.300 31.857 0.000 8.969 10.145\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 4.4418 0.425 10.447 0.000 3.608 5.275\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.9821 0.010 94.443 0.000 0.962 1.002\n", "p[1->0] 0.0504 0.027 1.876 0.061 -0.002 0.103\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical differentiation.\n", "\"\"\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_fedfunds.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the summary output, the mean federal funds rate in the first regime (the \"low regime\") is estimated to be $3.7$ whereas in the \"high regime\" it is $9.6$. Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980's was a time-period in which a high federal funds rate existed." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "res_fedfunds.smoothed_marginal_probabilities[1].plot(\n", " title='Probability of being in the high regime', figsize=(12,3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[55.85400626 19.85506546]\n" ] } ], "source": [ "print(res_fedfunds.expected_durations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Federal funds rate with switching intercept and lagged dependent variable\n", "\n", "The second example augments the previous model to include the lagged value of the federal funds rate.\n", "\n", "$$r_t = \\mu_{S_t} + r_{t-1} \\beta_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n", "\n", "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n", "\n", "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00} & p_{10} \\\\\n", "1 - p_{00} & 1 - p_{10}\n", "\\end{bmatrix}\n", "$$\n", "\n", "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma^2$." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Fit the model\n", "mod_fedfunds2 = sm.tsa.MarkovRegression(\n", " dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1])\n", "res_fedfunds2 = mod_fedfunds2.fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>225</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-264.711</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> AIC </th> <td>543.421</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:41:27</td> <th> BIC </th> <td>567.334</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>10-01-1954</td> <th> HQIC </th> <td>553.073</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 0.7245</td> <td> 0.289</td> <td> 2.510</td> <td> 0.012</td> <td> 0.159</td> <td> 1.290</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.7631</td> <td> 0.034</td> <td> 22.629</td> <td> 0.000</td> <td> 0.697</td> <td> 0.829</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.0989</td> <td> 0.118</td> <td> -0.835</td> <td> 0.404</td> <td> -0.331</td> <td> 0.133</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 1.0612</td> <td> 0.019</td> <td> 57.351</td> <td> 0.000</td> <td> 1.025</td> <td> 1.097</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.4783</td> <td> 0.050</td> <td> 9.642</td> <td> 0.000</td> <td> 0.381</td> <td> 0.576</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.6378</td> <td> 0.120</td> <td> 5.304</td> <td> 0.000</td> <td> 0.402</td> <td> 0.874</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.1306</td> <td> 0.050</td> <td> 2.634</td> <td> 0.008</td> <td> 0.033</td> <td> 0.228</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 225\n", "Model: MarkovRegression Log Likelihood -264.711\n", "Date: Fri, 12 Jun 2020 AIC 543.421\n", "Time: 07:41:27 BIC 567.334\n", "Sample: 10-01-1954 HQIC 553.073\n", " - 10-01-2010 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.7245 0.289 2.510 0.012 0.159 1.290\n", "x1 0.7631 0.034 22.629 0.000 0.697 0.829\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0989 0.118 -0.835 0.404 -0.331 0.133\n", "x1 1.0612 0.019 57.351 0.000 1.025 1.097\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.4783 0.050 9.642 0.000 0.381 0.576\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.6378 0.120 5.304 0.000 0.402 0.874\n", "p[1->0] 0.1306 0.050 2.634 0.008 0.033 0.228\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical differentiation.\n", "\"\"\"" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_fedfunds2.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several things to notice from the summary output:\n", "\n", "1. The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.\n", "2. The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.\n", "\n", "Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "res_fedfunds2.smoothed_marginal_probabilities[0].plot(\n", " title='Probability of being in the high regime', figsize=(12,3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the expected durations of each regime have decreased quite a bit." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.76105188 7.65529154]\n" ] } ], "source": [ "print(res_fedfunds2.expected_durations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Taylor rule with 2 or 3 regimes\n", "\n", "We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.\n", "\n", "Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the additional data\n", "from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf\n", "dta_ogap = pd.Series(ogap, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n", "dta_inf = pd.Series(inf, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n", "\n", "exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]\n", "\n", "# Fit the 2-regime model\n", "mod_fedfunds3 = sm.tsa.MarkovRegression(\n", " dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)\n", "res_fedfunds3 = mod_fedfunds3.fit()\n", "\n", "# Fit the 3-regime model\n", "np.random.seed(12345)\n", "mod_fedfunds4 = sm.tsa.MarkovRegression(\n", " dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)\n", "res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>222</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-229.256</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> AIC </th> <td>480.512</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:42:05</td> <th> BIC </th> <td>517.942</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>07-01-1955</td> <th> HQIC </th> <td>495.624</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 0.6555</td> <td> 0.137</td> <td> 4.771</td> <td> 0.000</td> <td> 0.386</td> <td> 0.925</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.8314</td> <td> 0.033</td> <td> 24.951</td> <td> 0.000</td> <td> 0.766</td> <td> 0.897</td>\n", "</tr>\n", "<tr>\n", " <th>x2</th> <td> 0.1355</td> <td> 0.029</td> <td> 4.609</td> <td> 0.000</td> <td> 0.078</td> <td> 0.193</td>\n", "</tr>\n", "<tr>\n", " <th>x3</th> <td> -0.0274</td> <td> 0.041</td> <td> -0.671</td> <td> 0.502</td> <td> -0.107</td> <td> 0.053</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.0945</td> <td> 0.128</td> <td> -0.739</td> <td> 0.460</td> <td> -0.345</td> <td> 0.156</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.9293</td> <td> 0.027</td> <td> 34.309</td> <td> 0.000</td> <td> 0.876</td> <td> 0.982</td>\n", "</tr>\n", "<tr>\n", " <th>x2</th> <td> 0.0343</td> <td> 0.024</td> <td> 1.429</td> <td> 0.153</td> <td> -0.013</td> <td> 0.081</td>\n", "</tr>\n", "<tr>\n", " <th>x3</th> <td> 0.2125</td> <td> 0.030</td> <td> 7.147</td> <td> 0.000</td> <td> 0.154</td> <td> 0.271</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.3323</td> <td> 0.035</td> <td> 9.526</td> <td> 0.000</td> <td> 0.264</td> <td> 0.401</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.7279</td> <td> 0.093</td> <td> 7.828</td> <td> 0.000</td> <td> 0.546</td> <td> 0.910</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.2115</td> <td> 0.064</td> <td> 3.298</td> <td> 0.001</td> <td> 0.086</td> <td> 0.337</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 222\n", "Model: MarkovRegression Log Likelihood -229.256\n", "Date: Fri, 12 Jun 2020 AIC 480.512\n", "Time: 07:42:05 BIC 517.942\n", "Sample: 07-01-1955 HQIC 495.624\n", " - 10-01-2010 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.6555 0.137 4.771 0.000 0.386 0.925\n", "x1 0.8314 0.033 24.951 0.000 0.766 0.897\n", "x2 0.1355 0.029 4.609 0.000 0.078 0.193\n", "x3 -0.0274 0.041 -0.671 0.502 -0.107 0.053\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0945 0.128 -0.739 0.460 -0.345 0.156\n", "x1 0.9293 0.027 34.309 0.000 0.876 0.982\n", "x2 0.0343 0.024 1.429 0.153 -0.013 0.081\n", "x3 0.2125 0.030 7.147 0.000 0.154 0.271\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.3323 0.035 9.526 0.000 0.264 0.401\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.7279 0.093 7.828 0.000 0.546 0.910\n", "p[1->0] 0.2115 0.064 3.298 0.001 0.086 0.337\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical differentiation.\n", "\"\"\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_fedfunds3.summary()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>222</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-180.806</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> AIC </th> <td>399.611</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:42:06</td> <th> BIC </th> <td>464.262</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>07-01-1955</td> <th> HQIC </th> <td>425.713</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-2010</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -1.0250</td> <td> 0.292</td> <td> -3.514</td> <td> 0.000</td> <td> -1.597</td> <td> -0.453</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.3277</td> <td> 0.086</td> <td> 3.809</td> <td> 0.000</td> <td> 0.159</td> <td> 0.496</td>\n", "</tr>\n", "<tr>\n", " <th>x2</th> <td> 0.2036</td> <td> 0.050</td> <td> 4.086</td> <td> 0.000</td> <td> 0.106</td> <td> 0.301</td>\n", "</tr>\n", "<tr>\n", " <th>x3</th> <td> 1.1381</td> <td> 0.081</td> <td> 13.972</td> <td> 0.000</td> <td> 0.978</td> <td> 1.298</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.0259</td> <td> 0.087</td> <td> -0.298</td> <td> 0.766</td> <td> -0.196</td> <td> 0.145</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.9737</td> <td> 0.019</td> <td> 50.206</td> <td> 0.000</td> <td> 0.936</td> <td> 1.012</td>\n", "</tr>\n", "<tr>\n", " <th>x2</th> <td> 0.0341</td> <td> 0.017</td> <td> 1.973</td> <td> 0.049</td> <td> 0.000</td> <td> 0.068</td>\n", "</tr>\n", "<tr>\n", " <th>x3</th> <td> 0.1215</td> <td> 0.022</td> <td> 5.605</td> <td> 0.000</td> <td> 0.079</td> <td> 0.164</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 2 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 0.7346</td> <td> 0.136</td> <td> 5.419</td> <td> 0.000</td> <td> 0.469</td> <td> 1.000</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.8436</td> <td> 0.024</td> <td> 34.798</td> <td> 0.000</td> <td> 0.796</td> <td> 0.891</td>\n", "</tr>\n", "<tr>\n", " <th>x2</th> <td> 0.1633</td> <td> 0.032</td> <td> 5.067</td> <td> 0.000</td> <td> 0.100</td> <td> 0.226</td>\n", "</tr>\n", "<tr>\n", " <th>x3</th> <td> -0.0499</td> <td> 0.027</td> <td> -1.829</td> <td> 0.067</td> <td> -0.103</td> <td> 0.004</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.1660</td> <td> 0.018</td> <td> 9.138</td> <td> 0.000</td> <td> 0.130</td> <td> 0.202</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.7214</td> <td> 0.117</td> <td> 6.147</td> <td> 0.000</td> <td> 0.491</td> <td> 0.951</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 4.001e-08</td> <td> 0.035</td> <td> 1.13e-06</td> <td> 1.000</td> <td> -0.069</td> <td> 0.069</td>\n", "</tr>\n", "<tr>\n", " <th>p[2->0]</th> <td> 0.0783</td> <td> 0.057</td> <td> 1.372</td> <td> 0.170</td> <td> -0.034</td> <td> 0.190</td>\n", "</tr>\n", "<tr>\n", " <th>p[0->1]</th> <td> 0.1044</td> <td> 0.095</td> <td> 1.103</td> <td> 0.270</td> <td> -0.081</td> <td> 0.290</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->1]</th> <td> 0.8259</td> <td> 0.054</td> <td> 15.201</td> <td> 0.000</td> <td> 0.719</td> <td> 0.932</td>\n", "</tr>\n", "<tr>\n", " <th>p[2->1]</th> <td> 0.2288</td> <td> 0.073</td> <td> 3.126</td> <td> 0.002</td> <td> 0.085</td> <td> 0.372</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 222\n", "Model: MarkovRegression Log Likelihood -180.806\n", "Date: Fri, 12 Jun 2020 AIC 399.611\n", "Time: 07:42:06 BIC 464.262\n", "Sample: 07-01-1955 HQIC 425.713\n", " - 10-01-2010 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -1.0250 0.292 -3.514 0.000 -1.597 -0.453\n", "x1 0.3277 0.086 3.809 0.000 0.159 0.496\n", "x2 0.2036 0.050 4.086 0.000 0.106 0.301\n", "x3 1.1381 0.081 13.972 0.000 0.978 1.298\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0259 0.087 -0.298 0.766 -0.196 0.145\n", "x1 0.9737 0.019 50.206 0.000 0.936 1.012\n", "x2 0.0341 0.017 1.973 0.049 0.000 0.068\n", "x3 0.1215 0.022 5.605 0.000 0.079 0.164\n", " Regime 2 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.7346 0.136 5.419 0.000 0.469 1.000\n", "x1 0.8436 0.024 34.798 0.000 0.796 0.891\n", "x2 0.1633 0.032 5.067 0.000 0.100 0.226\n", "x3 -0.0499 0.027 -1.829 0.067 -0.103 0.004\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.1660 0.018 9.138 0.000 0.130 0.202\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.7214 0.117 6.147 0.000 0.491 0.951\n", "p[1->0] 4.001e-08 0.035 1.13e-06 1.000 -0.069 0.069\n", "p[2->0] 0.0783 0.057 1.372 0.170 -0.034 0.190\n", "p[0->1] 0.1044 0.095 1.103 0.270 -0.081 0.290\n", "p[1->1] 0.8259 0.054 15.201 0.000 0.719 0.932\n", "p[2->1] 0.2288 0.073 3.126 0.002 0.085 0.372\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical differentiation.\n", "\"\"\"" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_fedfunds4.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x504 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, figsize=(10,7))\n", "\n", "ax = axes[0]\n", "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])\n", "ax.set(title='Smoothed probability of a low-interest rate regime')\n", "\n", "ax = axes[1]\n", "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])\n", "ax.set(title='Smoothed probability of a medium-interest rate regime')\n", "\n", "ax = axes[2]\n", "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])\n", "ax.set(title='Smoothed probability of a high-interest rate regime')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Switching variances\n", "\n", "We can also accomodate switching variances. In particular, we consider the model\n", "\n", "$$\n", "y_t = \\mu_{S_t} + y_{t-1} \\beta_{S_t} + \\varepsilon_t \\quad \\varepsilon_t \\sim N(0, \\sigma_{S_t}^2)\n", "$$\n", "\n", "We use maximum likelihood to estimate the parameters of this model: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma_0^2, \\sigma_1^2$.\n", "\n", "The application is to absolute returns on stocks, where the data can be found at http://www.stata-press.com/data/r14/snp500." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fvOgG0zpcr5phWoPFMMMwjIa3HDPVGfYey6W9S+Wx6VJ79+WXbuOYBNMKDk2g4/OIYRYEi2GGYRgNV0ok1HLMnrjYNNAFABieaK8YVhPo2NFjWsDiOsMM0xIshhmGYTQc1xPDQgRO28aBHABgeLLY9n0B7AwzrcGZYYZpDRbDDMMwGq5fZ1iPSXRnUgCAw5NtjklwaTWmDXA1CYZpDRbDDMMwGqrOcCIQq6bjZTI75wx3eN1nZkXj+OcnxyQYZmGwGGYYhtFwXSDhT6BTYtj2qki02xkOFkto62aZVQY7wwzTGiyGGYZhNGg5Zr3OsGl7avXYdEn93Q5IbLMzzLSC6lSxFmaYBcFimGEYRoMm0CUE1HLMphqGBsYL7VtbiCc+Me2AnWGGaQ0WwwzDMBo0gS4Z4wwDgNHGhTdo+7xYAtMK3KlimNZgMcwwDKMRXo6ZMsMuMinvclmx2hdpUBPoeAU6pgVsFZPg84hhFgKLYYZhGA1XAkLQcszeY6btoj/nlVcz7PY5ww6LGKYN2A6vQMcwrcBimGEYRsN19eWYg8xwfy4NADA6MoGORQyzcLhTxTCtkVrqBjAMwywnqJoEEIgLw3bR5zvDlTZmhoPSaiximIXDE+gYpjVYDDMMw2i4fjUJiXBMoo+dYWaZwhPoGKY1WAwzDMNokDPsSk+sSim9mEQXZYbbP4GORQzTClSnmk8jhlkYLIYZhmE0qJoEIOG6ErYrISXQl/Wc4U7EJFgMM63A5xHDtAaLYYZhGA3XlUgkBKTrOW1UY7gTzrCqM8wihmkBKs3H9aoZZmFwNQmGYRgNR1KdYS8mQWJYZYY74QyziGFagCdiMkxrsBhmGIbRcFwgkRB+abVgKeagznAbM8O+CHZ40Q2mBWzuVDFMS7QkhoUQg0KInwgh9gghnhJCvLhdDWMYhlkKpJRICPiLbgTOcE82BSHavByzy9UkmNYhZ1hKqNrYDMM0T6uZ4f8CcKuU8s1CiAyA7ja0iWEYZsmgahKO6y3HTE5wJpVANpVApQ3O8NNjeUyXrMAZZgHDtIDlBOek40qkkmIJW8Mwpx4LFsNCiAEAVwF4JwBIKU0AZnuaxTAMszQ4fp3hhF9ejZzhbCqBbCrZFmf4lZ+7t2qfDLNQ9PPHdiVSySVsDMOcgrQSkzgPwDiA64UQjwkhvi6E6Im+SAjxHiHENiHEtvHx8RZ2xzAM03m85ZgFEgKqxjDgOcO5dKKtmWEAEMITMzy8zSwUPWbDSzIzzPxpRQynADwfwDVSyucBKAL4YPRFUsprpZRXSimvXL9+fQu7YxiG6TwUk0iIsDOcSSaRTSVbrjMcFb2ZpHcZZnOYWSi6M8yjDAwzf1oRw8cAHJNSPuz/+yfwxDHDMMwpi+vCi0kIEZpA1y5neKZkhf5NYphFDLNQaAU6wDt/GYaZHwsWw1LKEQBHhRAX+g+9AsCTbWkVwzDMEuE5w158wXUlTMdzgjN+ZrhVZ/jYdDn070yKxTDTGiFnmGMSDDNvWq0m8TcAvutXkjgI4F2tN4lhGGbpoOWYVZ1hFZPwqkm06gwfmy6F/k1i2HP3eOYTM39sjkkwTEu0JIallDsAXNmmtjAMwywplOdN+BPooqXVcukkyi06w8dnws5wmjLDPLzNLBCHJ9AxTEvwCnQMwzA+JCooM+xomeEs1RnuUEzCZjXMLBDbYWeYYVqBxTDDMIwP5S2TCQFB1SScsDPc9pgETaBjR49ZIHpHisUww8wfFsMMwzA+pCk8Z9ivM1yVGeYJdMzygusMM0xrsBhmGIbxCZxhVNcZTiWQTSdQsVpzhkfnKsilg0svOcP6UHcrzFUsvP+HOzBbthq/mFkRcJ1hhmkNFsMMwzA+4cwwquoMN7scc9GwYx1kw3YwXbJw1ppu9Rg5w+1y9B4/NoubHjuOXcdm2rI9ZvljO7Lt5xHDrCZYDDMMw/i4bjgzLP3MsBBAKiE8Z7iJzPAff+0h/MvPq8uuj80ZAICzh6rFsN0mR8/yM85ls7U4x3LBdaX6Xph4HFciq+I2S9wYhjkFYTHMMEzHeeTQFL5w5/6lbkZD9Al0yYQAAFQsB5lkAkII5FJJmLZbtaSyjuW42H1iDg8enKx6bixfAQCcpYnhdNLbT7sEH8UtWi0Bt1z4u5/sxPt/tGOpm7GssUNimDsODDNfWAwzDNNxfrHrBL5yz4GlbkZD3EhMAgAqlqvc26yf9a1XUeLoVAmOK3FoooiCYYeeG/Wd4TPXdKnHMilvoY12OcNUWcBoMdu8XDg6VcKRqVLjF65iHNdV2XOOSTDM/GExzDBMx7FcqYbvlzOkRykmAXjOMLluWV+41hOaw5NFAICUwO7js6HnRudqO8PtcvSsFeYMW45s2+TClYrtSq5KwjAtwGKYYZiOYzsuLGf5Zz8pJpEQnjsMABU7cN1yyhmuLTQPTQQu5uNVYthAOimwsT+nHmv38DY5wytHDLunREdqKXF0MczOMMPMm5aWY2YYhmkGcvYs10U2kVzi1tQmPibhBDEJ3xmuV17t0EQB/bkUerKpKjE8NlfBaX05FbcAtNJq7XKGbd8ZXiET6GxHwuLV+eqiO8PLvcPJMMsRFsMMw3QcWsXNciSyy/iq42jVJBJaTIKERjPO8PBECeet68Gangz2jxZCz43mK9jQn1UCGADSbc56knBsddno5YLlunVjEhXLwWzZwgbNbV9t2E4wesExCYaZPxyTYBim45CYMVtcyrjThJdj9h6Lc4brTaA7NFHEeet60JNJVYnm0TkDG/pzanuAVlqtTbnYlVZNwnbq582vvfcg3nD1lkVs0fLCdSVcCY5JMEwLsBhmGKbjUI51uWc/wzEJcoarM8O1XFfLcXFitoyz1/YglRRV0YfRuQo29OeUqAY64Aw7K8sZprx5LcbyFUwWzEVs0fKCxC+dU5woYZj5w2KYYZiOY52CzjBlhk3bRSrZXGbYsF1ICfRmk0gnE7C0z+u4EvmKjTXdmXhnuG0T6MgZXt7HulkaVSKpWO6qzhRTLKJZZ/iWXSfwgo/fUfeYGraDN1y9BduGp9rXUIZZxrAYZhim49CN11zmznBoOWZfDZuOq8qf9fqB54Jhxb6fxG86mUA6KWBpApciE7l0QlWQAIIJdO1bdGNlrUBnN6gmQR2Q1ZqVtSNiuNF5NDxRxFjeQKFi13zNdNHCjqMzVRNAGWalwmKYYZiOo6pJLHMxTAajXmfYtF2kEt6lcqgnAwCYKtYQw64uhhOhz0u1iTOpRGgCXbudYdM/1isnJlG/zjB9zuV+bnUKxz822SarktDoTFym/PhMGV+550DQeV3mIzkM0y5YDDMM03FIJFLZr+WKq2ISCGISjouU/481PWkAwFTRiH0/xUHSSYFUIhESceSKZ1NJJBJCbTNYLKE9wkM5wytEDFuuC9OpvQQ2ieF2dSZONSiPry+6ce29B3DuB38R6xIbdcTwrU+M4BO/2oPxgnd+sxhmVgvLuMgRwzArhSAmsbwFGuUthTaBzssMe39nU0n0ZlO1nWE9JpESoVgIOcPBanYJ2KajJtC1y9hUmeEVE5PwPo/tShVXAYAP//cTuPysQSXu7NXqDEdjElLi33+5B4DXActF6norMRxzfqjJl/5zyz3WxDDtomVnWAiRFEI8JoS4pR0NYhims9zx5Ci2H17ciTFBabXl7d6Rk5YUAsmYmATgucPTpfjqBbYek0gkQgKNOgIkWqL/t9vkDCtBU6cW8qmClFKJ+2hU4tbdI7hv/zgMFZNY3udWp1CZ4Zg6w3Fi1qxTbcSKuMbsDDOrhXbEJN4H4Kk2bIdhmEXgk7fuwbX3HlzUfZJAW+65Tn3RDaHHJDRHcqg7g8livBgmsZ9OCqSTCbjaxK5KxBlWC3n4/68l5sbylZoRgThINFZWgDNs1xF2luOiZDrquLarM3GqQd+37gwTVoyYrZcZtlQlEu+5evW0GWYl0ZIYFkKcCeD3AXy9Pc1hGKbTGHb9uq2dgETNcneaKCah1xl2XBlyhod6MpiuIYZJ7KeTCSWg6TESFlFHuCvjDWPHHZupoomXfmIz7nxqrOnPQKJwJWSGdTc42pGybBdly1EOeLsWLTnViMsME/rv/MkTczg4Xqgbk4hWImExzKwWWs0M/yeAfwDQ14a2MAyzCJh2/VJVnYAcquXuDOvVJDT9qya7AcCangz2RZZZJnQxnInM7iexS7WK6f9daRLD1eJkqmjCdFyM5itNfwZyp1eCGNbd4KjYtRyJsumoLPZyP7c6BZ1fdD7pnSr9mHzop7twWn9OTQyNdYYjEYrl3nllmHaxYGdYCPFaAGNSyu0NXvceIcQ2IcS28fHxhe6OYZg2YTruortoNPy63CfkOKFqEoEAjsYkamWGg2oSmjNskzPsCYysv4pdsKqdL2Jijs1CSlyRU1ix3LbVLl4q9My1LuyklLBcPyZhr+7MMJ0bXf55NTIXdJz0c2q2bCFfsdTrYzPDTjjSs9x/rwzTLlqJSbwEwB8IIYYB/ADAbwshvhN9kZTyWinllVLKK9evX9/C7hiGaQem7S56vtJegKhbCvTlmIUuhjVneKg342dVaztrlBkGgrJyKiaRbD4mYdrh9wLAeN7AjduP1fwMekdnKYa5v/3gMD7008fbsi3bjY9JOK6ElJ67udrrDNN33J3xBnqPT5fVc/oxKZoODNvVJtBVH69oxCZutIJhViILFsNSyg9JKc+UUp4L4K0A7pJSvq1tLWMYpiOYjqsWZlgsgkU3lrd7p0+g0/SvWo4Z8JxhwIswRNFjEmmVGQ7HJHLpsBimCXWxYjimE3HzjuP4wI93YrZco7ybJoCWYuGNhw5N4d597RkFtELOcHUWtmTaQWm1U9wFXyjqvPI7VcdnNDGsVW8pGTYqllt/Ap1Nx5VjEszqghfdYJhVhJTSc4YX2UUjUbfc3Ts3ZgIdEI5JrOmpJ4aDmAQ5w3SsA2eYMsPe88mEQCaVgBFXBisSsdC3Y9QQurooXIrcsO24bXOka02go/NprmyDiies1jrDdG50p6vFMB0nKSVKlgPDcurXGVYRG64zzKwu2iKGpZR3Sylf245tMQzTOUisLXZm+FSpJhEWw8HjoZhEXTEcxCTITQ6qSYQzw0oMC4FsMtG0MxwXnYhrA7BUYli2bXhdj/NYMflh/fM1GnU4OF6YV4m65cpHf7Yb335wWP1bZYZ9Z/jETHVMomK5kNITufUyw3RdKLMzzKwy2BlmmFWEcmgXKTP83u8/hu89fETFD5a700TN8+oM65nhcGk1ALGT6MLVJOJjEtE6wwnfGa6XGQ6J4Yi4JvaN5nHrEyNhMbwEtYZNp32l++KiEUB8Z65eDv7IZAm//dl78MCByba0aym5a89Y6HMYKn7jieG441Q0bfXaujGJSCeDxTCzWuDlmBlmFUGVDRbLGb53fzg7utxjErWqSejLAK/1xfDwRKnq/UoMpxJKQNesM5wMnOFGYlh3gS07cPt0rt9yCLfvHsV563rUY61mhveN5vHA0xN450vOa/o9tiNb7vSYtou3ff1hvOY5G9Vjcc5wdL+1mPI7LqNzzZeoW65ESyPSOdKdSVa9ll5XMrzzoGI5SjTHL8cs1esArjPMrB7YGWaYVQSJlMXKVxqWi4JhB/tfhJvr9sNTuHnH8QW9V68mocckkpozPNidwUufuQ7fuP8gJgtG6P1BZlggHVlZzlSZYYpJeKJEOcN1MsNxtWOjQqVgOCiZDixXoscXRq3GJH6y/Rg++vMn51WizXJcOK4MLf4wXyaLBh4ZnsK2w9Oh7RL1ytDFQcevuAJW5TMjmWw6FlSvGgDWdKdDz5EzXLGD98adG1XVJJZ555Vh2gWLYYZZRShxtQjOsJQSFdtBoRKI4cVwhq/bMoxP/mpP06//5K17cPdeb4W3cDWJeGcYAD7yuktQMh1cc/eB0OPKGU4kkE5EV6BzkEwEWeKMPoGuRmY4TvjSdxeNSZQMGxXbge246Mt5YqjVmES+QiKq+e1YbciHU7v1jpRVYzIdUa+ahIoGmHbN15wqRJ1hmkipO8MU5VHOsP+5HVeqY1CvNCAvusGsNlgMM8wqIihD1fmbnOl4k3byNQRNpyhUbJTm4YjNkYAoAAAgAElEQVR++8HD+OXjJwFElmPWrOFkIiyGn7WhDy88fwgPHQpnUPWYBDnDNHxvWK7KCwOaGK4Xk4gTwzUm0BVNr7JCyXTQl/MScJV5ipmHD06GXGASo6V5iOp21JSmCEjRiO9IxUUi6jrDjuNvb2U4w/rviM6RnCaG1/ZmAQTHRP/c5I7HZ4bDqxeyGGZWCyyGGWYVQTe3xcgMk1grGFbVY/WwHbelWf/5ijUv8WbaLqZLXhvdJuoME5edOYg9J/Mhh02PSaQizrDpxIvhRAKNYxIxeVkjkhkmNzVfsQIxPI/jsGdkDn907UPYcmBCPVaoeMelNA8RqdrnLFx4khOdrzGqEHes6p3TdBxLp7gzTKURdZFK50EupYlhcoYjdYN14kYNqCOjqklwTIJZJbAYZphVBAmKxYgrkEjUXalG+3VciRd/4i789NGFZX4Bz800bbepzKqU3mSvWV8MBxPoIjGJiDMMAJedNQjbldh9Yk49FopJREurWa4SwADQm035ojmBTDIR21GIqykcLdVGFM1AQFJMolhH/B2fKYcW7pguen/rJeOUM2w1LyJtp/WYRKVRTCJm2/VGOwwlhk9tZ5iOQbRjIEQ4ykMxCdOp3Qkox6xAV7UcMzvDzCqBxTDDrCLUBLpFWK2LHKv5ZIYrloPxvIEjU9WVGpqF9teMC0jHg8qk0WERwvuPiMYkAODyswYBADuOzqjHLMf1hHRCW45Zy/hmNffuT15wNr717heqRTfqllaLqR4QFc/k5hm2q8SQfuyjvOO6R/D5X+9T/6bOi+4Y5ivzj0nE1UaeL+QMF2rEJOLiNvUiOOYKEcOmE38+ZFOJUCnAtZHMcNzEwXqZ4TJXk2BWGSyGGWYVQaLAcWXHFyAg5zJOyNUiTvzNFxJwzVRSoJs9xSRszdkNT6CrvlRu6M9hY38OOzUxbDtSOXT0f3IsTSfsDK/pyeDFz1gLwKs9HCc84iIRcTliIOwC59JJdKWTmKvEL9kMeA7wWD4oNUZCsRQjhuczEU85wy18h3GZYdtxMTJbwas+fw8OTRZj9lsvM1y9vVMRcsRDMQnbVRVKiKoJdDGfOzYm4YZLq7VaFWS5c/2WQ/jqPQcav5BZ8bAYXgFIKfHlu5+OXRGLObUYnavg6s1Pd0yohkt0dfYmF62D6+2zgRhu0VV0XYmC2byAo/3Mlk1IKZXAzKbDYjjOGQaACzf24eBEIdR+Es5VznBkAp2O5wxXt7d+ZjhaTSL4dyYp0N+Vwly5tvgzLCcUYaHOg+6okzM7HxGpxH8bqkmEJ4pJPHF8FvtGC3jyxGzMfpuoJrEEK/K1k7hlzQ3bRSYVrjE8pCbQ0aIb4c+dTorYCiFxK/ut5KjErU+M4JdPjCx1M5hlAIvhFcDhyRI+dete3PHk6FI3hWmRW3adxKdv24vROaPxixdAnKjqFNFMq7f/+gI8rq7ufChZDqgf0cyQOO3HciSKpqMc2EwyEZpAFy2tRnRnkiHX1ooVw/ET6HQyyRoT6Jpcjtly3ND7U8kE+nPpus6wYbsh4Uslt+i4SSmVGJ6PiAyOaesxCR3bcTHh13Wei4l/1OvcWSvEGdbPV8KL34TPK4pJqHhI5HMPdKVrTKDztqv3xVeyGDZst6pTudQ8PZbHxR++FUcmFx4VY+YPi+EOsvPozKLMXqYbx6nuejBQE5rihGQ70G9sna4oEa124O0//nNtHZ7CCz5+hxI7C/38eU38zUcMA8B00YTpOEj5tYBrLcesE403WHYQk0hRTKLGBDqdxivQVVes0Pcb/ayppEB/V20xbDsubFfWcIZptbJgEuK8Squ51e2bL7VGFej8iMtCR2MS9+0fxy27TgBoPTN8++4RHG0hx94uanWOomK4P5dGKiFqZob7u9IwbLdqMZW4DkwrVUGWO4btLruKGcMTJZQtJzTixHQeFsMdYrJg4E3XPIAfbzvW8X2R6Gh16VVm6ZlTYrgzF+hQTKLDtYaj7l4yIWq6dzuOzGAsb+DYdBnAwt0oXSQ1FZPQboQzJSskWEMr0NVwhrOpZFioutXOsFljAp1OozrD8SvQaUI2OgyeSKA/VzsmEVdlgISiKtGmlcSbj6NqtRh1AeKvZaYjMVHwomD5GJFvuTI04e66+w/hs7fvC7VloWL4vT94DN9+6PCC3tsKT52cw/VbDql/15pQGe1k9eVSSCcTQezBrHaGgerfaJwYNm0XhyaKuPOplTfyaNrOsnO+6dqfrzP5dbGZLBjL7ji1GxbDHWLPSB6OK5WT0UnqLa/JnFooZzjGGWsH+k20nc7wTMnETCmcWY9+hu5MsubQ+eicN5GLxIzeztmyhas3P93UksD6Ah/NjMrobZwpm6EoQ7i0Wg1nOJ0IuZiWIzUxHHGGY0QLkUkmYdoufvX4Sdz6xEn1eFxsRLnF2n6jJdTinOHZsoUbtx8LbUN3DEkE07by8+xYAF60Irr89EKIE8N6TEIXvdlUAumkwKGJIi772O3Ydcyb0GjYLsb888qoU2Isyk2PHcO///Ip9W/HlahY7pKIk5seO45/ueVJNYdAj6DQY0aMM+yJYRHKDOsr1PXXWKEw7ppg2i6uu/8QPvDjnW36VMsHI1KzeTlA535hGUV6Xv2f94Y6ZSsRFsN1cFyJn+080dRNOMrekTyAxendkUMUN7TYSUZmK6E6pUzrzC1iTKKVTOeRyRJe9fl7cHLWc3L/7se78IEfhW+WUdepJ5OqeeMZzXsihxw//XW/fnIUn75tL/aN5Ru2KyTg/JvK2Fyl5m/Y1IaAp6uc4cYT6HLpiDNsu1o1iUhmOEa0ELToxvt+sAN/9Z1HVcmzuHxwXDWJ6KIYacoMa7/PX+w6iQ/8eCdOzpaDurtG9fFSSyFrx7LZFf30ygOtVZMI70+IcEyCvudUQiCXTiKVSODIVAmOK3F0yjsnDdtF0XRQ9OtOA82tQPf+H+7EtfceVOdM8N6FX8sN21nQtdLwM/DkaNMxlTI41vHOcBqZVOAMl0wba7oz6nlyhqMGStxokem4KJr2KZ+3jsO03WVXPi5whpfHvdX73Zk4vAxiQp2ExXAdHjwwifd+/zE8ppVOapZFFcNLFJN41w1b8enb9izqPufLbNk6peIjsx2OSYSWtG2hZNIjw1P+rH5vwYnxfKWqNnC0c9adSdYUSOQM0+9F//zTfpWUZoRMNCYxnjfw0k9uxh01hnj1/cyUzFCUQTQxgY4yw+TS2VpMIliBTnfwasckLEei3xcpPyEHV6sLTeInLiYRdTxTCb+aRMVWbSMxU6jY6ppRshwl+qKl1XRnqllnOFT9oZVqEpHfbFc6CSsUk/DaNtCVRi6dQCoplHggZ5uOz1g+GOIta5+3EUenS6HttDL/48ubD+CNV2+Z9/voN0TfhRXbKaqO32RSiVBMomg4WNsbiOHBbj8mERXDNZzhiuXAcuSiLNazmCxnZ3i5xCToGkkLE61UWAzXgYTJQoYr9o6SGO78CUQn62KLvvG8gcnC8i7n9kdffRCfv2Nf4xcuEzo9gS5agWChkPCd9IVqxXIxVTThulLVro3O0u7O1o5JjEXEsH6DmvLjF80IMn3p55Lp4MhUCabj4uRsJfb1ZkgMW6FawLobHLccM+CJYSkDEWFqMQkhhD9U3TgmQY5xNDMeWmksUkmg3gQ6coYdVwZZYG2CHJ1fUlZPwC3F3IybdQV1Z7G1mET4vZ4Yro5J9HelkUsnkU4mqmoi0/7H5iqhtjSKk1GcYI9vaEQF6UI4MVOueQ7Wg74n1UnUq8HYQT3nuPPKE8PU0bFV7WEgcIb180bK+JrCpu2qY7pUi5bc8eQojs+U275dw3aW3QS65ZYZJl0xU17e9/pWYTFcB7oBzFdkuq7EPl8ML0buJ4hJLO6FyrCcZeW6ThYMPH4sXH90ZK6CEzPzvwktFR3PDLctJuEtekC1rQ3bwXTJxM07j+Nln9yM2ZKFSkQMddeISUgpVSk5JYa1tilnuAlnLhqTIJFd672hahIlM1QLOBGqJlF7Ah0Q/Ab1mIT3voRWXaG6BBaRUZPtwq5vXFY4LjNcLYaFcpkpN6w7v7qQJse9rCbQ+Q6yf+3KJBNNxyTinMuFoAtWWqGvbDqYibhT/V1p5FJJpBJCW3kwvHraWN4ItaXReXTWmm4AwD5fDAfO8MKvdWXLQcV25l0/nD6DytLHOcM16lenk0JbbMQJxSQ2DXYBQKiEY63JrabtVkVoFhPHlfir72zHd9o8gVFKqZZtr7dgS8VysHnPWFv3XY9oB6gWMyVzUe6/tA9arn2lwmK4DnTRnO8Jd2y6rC6ci5MZXpoJdBU7fFNdaj5z+z687kv343sPH1GPVSynaib1cqbTMYl2TaCj/Nik79RVLBeuBLYOT8OwXZyYLdeYQFe9z7xhq3OXnF39pk/uczPD1PR7Swjv9RS/qHUTj1aTCE+gC16XqhGTyKW915J7qNcZpvfpud9sunZmmEgnhfr+QyuNOeHcKN0033zNA/jy3U+Htkd1hgGoihIV5QzbsRELNYHOoMyw912s78s2LYL06E0rzrA+quCVuhMYmQt3aoXwHM6ujOcMB4LNd1EtTQzrznCDz0J9IBrda4czXPGzv/Nd6Ia+s2IdMVzXGdbuDb3ZlHrdBRt6AQCHtZX8anWODcdFmWI1bb6Wuq5UkcKR2UrsiNhk0fDLALZ337Yr1fLr9Tput+0ewbtu2LpopfXofGs0qvyHX3kQX7xrf8PtjcxW8Mlb9yx4JUFqz0qfH8RiuA4LdYZpUtHanszixCSsxZ9A57jerPHl5AyTg/hP//04poveimIVy1Udk4cOTuI3Pn7HspmYEKViBZ2LRakzXKO02tWbn8a7b9hadztUEF7FJPz2PnWSMsRG1QS67kwy5BwSY5rIiYtJTCsx3PiY5Cs2ejJJdGdSKJuumphXK29MgmmgK62cYRIMYiHOsCtDYjiTTMB2XeVCZWvELXQxs643C9PPIeudIsNy/WoNgbi2HRfbj0xjt5/d1tvb35UCEDjD+lB3qBKFEY5HlCMxidP6s83HJDRRsX+0gDdcvSUkIm7cfgwf/u8nGm5Hv5alk17+NRozSCcT+PtXXYgPv/biUGclcIYpMxyOSTTKntMxp9E92k4rYkzVbp7n7zo6ZG6FYhL1J2bqE+gKho3ubBI5/3Wn9eXQn0vhsLawQ7RzrEYrbBeVNsUkvnTXfuwfDSbC3rZ7BK/+z3tx2+4RvOg/7sSnbt1b9Z6JfPO///kQN+oSBy3wslhisFln+HiT0Zu/+NY2XHP3AXU+L7Q90yWOScQihDhLCLFZCPGkEGK3EOJ97WzYcoDKDs1XZNLw8Pq+7CLFJBY/M1xZAgHeCLrwu9LLmaohQv973D+ax3je6Njqbq2il8FajAl0tVyq3Sdm8dDByZpDugXDViJ4SmWGvWO856R3wZ0oGDHOcCqUeST07yMuJkGZ4WjFhPi2WejLeW5h2bIx5m+7bNWvt7u2N+NNLHOCSW4hZ7hOaTVAy/hGYxJJAcuWaj/ZdI0JdMmwGKZthjLD/kIZ9LUYtovpkhVaLawv5wngdMgZ9mMS2lB3OG/sd/qV4AliEtlUwlutrNmYhHZO3blnDDuOzuCfbw7E7+a9Y/jx9qMN4wL6/lJJgUwyoUwGIpNM4DlnDuCKc4ZCnRX6nPQZx+fCMYlG7iadywfHi7CdoNpAM+dfo89jWC6GJ4oNXh1QLyYRl0V/+YXr8fYXnQMgyAxXLK+Wbn/Oy1cDXkb93HU9GNad4UjnuDubVNuvtCGKVzJtfOb2fbhlV1A68Al/WW2qRBMVbMemSyon3uw5KKXE02ONF6zQfwMPH5rC6754f+yoAf0uFqvUmXKGjdri2/XnAjTzfTx+fNbf7sK+u4oaFXA6ZtIsB1pxhm0AH5BSXgLgRQD+lxDikvY0a3mwUGeYXr++Lxu7bGi7WUoxvJx+HHpPulCx1Y+Yhk3LVvhmv9zQy2AtRmZ4dK6Cu/ZUV1koGg5KphOq2QsAT48VMDxRVK6wEJ4Y1h1MOsZxznCPX2c4KoRGNWc47qY/H2e4YNjozaXQnUmiZDpqMl8tN5D2M9iV9h1TJ7a0Wq2YBDlyRo2YRDqZgOUGM9YzTTnDXraTVseiCV2GFRbHhuVgshh0JBIimBiViskM63WE9d8tdRZLFl3vvBxl3rDRpx3LZtCzl67/PW/eO66upUXDVpMt66Ffy1IJr1pERXPxgXCFD/2YlyOZ4fGCAUPrpDT6LHQO2670sr5WcNzmm/mNtmnn0Rm8/DN3Y/vhafVcxQrO0yhRV1oX9XqUJpP0zpEb3vUC/Osbng0gyAyToznYnVadt2wqiXPW9oSc4WhMoieTUttvxwS6slktqPeNeqKVfvenD+QwNlfBgfECbts9gpd+cjMeOTQVen8j7ts/gVd+7h78aNvRuq/TfwPbhqfw+PHZ0LWIiEZVOk3UGb599wju2Tceek25STNKN1gWqkX0yNKJmcqirJ2wFCxYDEspT0opH/X/zgN4CsAZ7WrYckBNLFmoGPaHOzstGE0lhsM/jPG8gb/41raOlESp1Nhn0++3nLb3tOcqFvp9Z6xoBDf8YGWtoIf70MHJmkvVLhX6MNxixCS+/eBh/Pk3t1XtizoLY5Ebwz/+9HF8+OYncGTKc5OedVovJgueAx/VCOP5ame4K5MK1Uclws5wODPsuBIz5GxGOjE3PXYML//05lCprHzFE3BdaU/A0c2t1k1cieHuDMq+gxY/ga5WFYhwTMKOxCTInSPhUiszrA9zr1XOsNee3qwvShxXVRCgtuvVXHoyKeX8pRIJJRqrM8MRZ9igzHDw2GzZwvHpMnqzKXSlU00LkdDER21Y9fYnRwAE19RGlQF0wZROitAxPWetN8EtFclmq89j2rCdYCnpsTkvMzzQlVHPN9o3dUDKVhApceXCr3d0D6EqLHp05Kv3HMTrvnh/jbY04ww7secVlVaj68qAP9kQ8Dpf567txvGZstpONCbR5R8DfQJdK2I4WroP8EbrLtrYpzqD+YqNt177EF7x2Xvws53eUtpbDkwAaF4M0+f92r0H675OP5b0nrjJlcF8hkUSw/SdV2yM5St4z7e34x3XPRJ6TTGS86/Fwwen1N9zTcQ8frT1KDbvDU8W1K8Vf/ujHXjjl7csuFO4nGlLZlgIcS6A5wF4uB3bWy4EznD9C+Cbr3kAX7nngPo3nczr+7ybWqHD7rCqJhERNTuOzuDXT45i98nZuLe1RKvO8Cd+tQdv/0Z7T5d8xVazpPNGUEtVzZL32zxZMPGnX38Y333oSPyGlghdDHcqfmJE4geurB7+JcESjZNMlUyMzlXUksmXnTmIyaIR29aJgucM68PXPXRzdao7bTQRjdwLw3ZxfKaM7YenldCO3qh2HZvF8GQp1FnNV2xPwGWSKJuO+gy1BBC1ZbA77U8sc2PrDNd0hiMT6Ew76gyL0FB7vUU3CBWTsDxHuTcXOHSGtkiIYbshl6Y7m0RXmsSOUJGJuUhnIhqTKKrOoq3a96Yvb8E9+8Zx/vpe9GSTTVXyAMKCir63TCqB3ce9TDMJihMNxbCrYirpZEKtALiuN6NEvu6y652VqNgfy1dgOS7W+LV162WGaZ4BVV6omG7ourpQQUTXIFqlcVJzxk/OljE6Z8SO7EVdQjMSc3L9uRtxIw6xYjgdiOGzh7rhuBLH/d9ztTNMYjhwx+PiRt4KfY3vA8E2Aof48FQJr750I3Z95FV43tmDKBg2Dvoxkl/4cYon/GH+5qM63ufYP1ao6+aGa4yHR0902tERmA/6d/7FO72JsdHrRqlJo27b4UAMNzOZ/0ubn8Y3HxgOPaZ/tzuOzuDoVLlqjkItHjgw0ZGSeJ2gZTEshOgFcCOA/y2lrDpCQoj3CCG2CSG2jY+PV29gkblv/3iV41ULvZrEz3eeCAX/Cdtx8WhkAgtdPOmm1umKEmqI2oyKGhqabP+PWInhBYq2Y9NldRFuF/mKhY0DOQA0HBsMbwJBXGJkrgLHlTg2vbxW1CEHD+icM2zZrhJ5dF5GhQ6JppHI5IyiYWOiYGIsbyCbSuC89T2oWG7VMsyAPzRtuUq4AIHTpLubADBVNLCuN4tUIqi8YNoufudz9+AtX31Qa1f4mJArqt8QZkomBrsz6M4kMV0yNccn/njSEGB/Lq1ElIpJaEK+5nLM0Ql0TnVpNcvRYhJNiWEtJmG76POdYcN2VCbXqzjhhJzh7kxKdSpSCW/SWXcmGcQkLMrQ21WZYSm9SABds4YnS3jLlWfiq2+/Al3ziElEBVV/LoWLNvapmr10rh1r8NuvWI6KeaSSAumUd0w3DXYpYRCOSQR/62J/sDuN6ZK38A4J3Hpl4vT3AWFnGFh4xIrO0WlfdE1qnRiKI0XLxgHB9TW2moQfowHiRxzSfl6dtksLlCQTAsmEwLnregBA5Yaji/B0+zGJkhWcd3HnwdfuO4jf+6/76nx6j2h5tgPjBUgJXLChD7l0Er3ZFPIVG5du6g+9j/bdrDOstzHqcuqEaozXuU5UIt9Bp1HxF8fFD/2ox1qtRjTQfKWrkzMVZco1MxJaMOyqGITeGaQO7p1PNS41Zzsu/uRrD+OPtGv4cqYlMSyESMMTwt+VUv407jVSymullFdKKa9cv359K7trGceVePcNW3F9pOdTCz0z/KGfPo4bYt43XjDgyqirF2SGgc4Pr9Raga6oJsJ0ICZB+6wh2r7/yJG6F6KyZbe1py2lRMGwcfqA5wwXjOCGX7FcuH7+D4DK58Xlw5YSOoeECC6IU0Vz3rU9D08Wa86ONh1XZQHJMazqRPn/Ho3kGAuGjemSiZOz3gV2XY93fkd7/n3ZlBeTsB0lhpMJoSaPRZ3hyaKJtT2ZkPthOm7V+RF1sCkvq7d/omBiXW8GXemUyjZ7r4n/DRp+WSpykk2tFrA+gS7ZKDOsLZIRcob9leVIROVqrECnf3a6bpD47fMnwpm2qyoI9OXSMCwXk0VD1eHtziSDmITfXm9J5vCNs+xno4mi4YlHVyK0Stnzzl7jCeq0Vx+6Xi1WIjopsy+XxoUb+rBnZM7fl9eWZmISg5QN9oU9AGwa6FLnUbqGM1zUJvqQuJ8uWRjwBW6pzvWYjhEtUKFXeAHa4Az7vzk9M03HJC5H3WgCHT3fjDM82JVBLp1Ur33Geq+82i6/Nnv0mtHjT6DTO+lx16LDkyUMTxZjh863Dk/hM7d5FSKii7/s9/PCVOatP5dGwQiWfe7JJPGM9T3Bvpt0hvUOSz3DRTccgmth9fdL50S77uP37BvH27/xcM3fk34fp85w1FBT1Un8147MVvClu/ZXmSgjcxWct7YHqYRoGJOQUiJfsVT1jqA91e2Mm2sSZdi//q54Z1h4dYe+AeApKeXn2tekzjFTMmE5ElNNrppGJ1zR9PKtMzEnE5U2iRvipgtxp7OpwaIb4ZOWxEOhA84w3UwtJ37Voi/cuT9U7zdKyXSUI9UOiqYDV3oTMADvwqVfVMqWo5yxcX/oPFq3dKmhc2ioO6M6OG+Z5wp6hu3gNz99N/7y29tinzdtbUKWHa62QZBYGNNiElJ6dT6l9BYjOK0vqwTDyciiJpds6vcm0GnuXjopVFmn6A11umRiqCdT5Zrqbl8mVb3wQ9QZphz6ut4sujNJ5bit683UnUCXTSbQnU56tUxNZ16LbuQi1SRsJ5IZTgjYrqs6Xqf152K3QxOgkolg4lvF8py/XuUMB05gbzYFw88MD/VkcM5QN7ozSeVUUxu8JZnDMYmqzLAZ/FZ0B4qyuXS+NLPwRrRcX18uhYtO78dEwcR43mgqJkEu9YDv5KaSQondTYNdqkNRKzNcNm0l7Mhlny6ZGOhKoy+XClVQiELX0EF/3/oEOmBhQ+WWXwUECGISE4VqMRxXuiq6NG+0ugh9ztjSav7kTT0mkU0l1O9sqCeDy88axJ3+UuXURjrVc+kkhAjf2+I+f9n0rgtxz/3hVx7ElzY/rb5TbxveZzk0UYQQwDlrPcHbm02hULExW7bwxy84G3f//W/h0k0DsfueKBh43w8eiy2TSa9LJkTdcmi640/uedx1Qs/at4NvPjCM+/ZP4MB4/HkYrST0G+cNoWDaobkRdM7QMb15x3F85vZ9+Lsf7wq9d2yugg0DOfTlqgV13H4tR2KyaIT2FV1J9KXPXIedx2YbutJUGYQWsVnutOIMvwTA2wH8thBih//fa9rUro5AOa1m6+UVVI/dEwVxPSsaSp7VtlmxvKwkDbUtVkzC1CaNAEH7OzG8ozvCem9059EZOK7ERMGou9+yL17bVUIsry0QkPRXo6qEhjcdJcLG/NqzI7PLa1bsbNlCdyaJnmwKhr9a1ZHJ0ryWcSV3afPe8VBHw3Elvnz305gsmOjxxRWhOymuK5Xo0WMS5BwCwNPjBazvy2LIFxrU86fli599xgCmSxYKhiaGEwnVOZwoRrLIBRNDPVkl5Ii1vvMMAGcOdlU5evR7nsgb+B/XPIAtT3sTbdb1ZpSAA4BLNg3UdJVMO3CG6TiRsAhlhhvUGaYbgxmJSXgLH0i1CuKmwRpi2N9nXy6lxB79fvXMMImf3mwKpuNiPG9gbU8Gf/OKZ+HdLzlPi0loznBVneFg1KQ3m1LVQwBgSDvm5/oihY5NMyMU0ZhEXy6Fizf2AfBK9tFvsp5bZDneYgiDKiaRUOfopsGc+oyZGtUkdLFP55yUnmB88flrce++iZqdcDpP1ugxiXk4w7MlC7ftHondJhDce6a03wCZFVFnWK/SUismQdfe6G8HCM692bIFIbzvIptOhoTz71yyATuPeVUUyKmkeEQ6mUAmmQjd9+J+R3Tu1Ds2hlaRgkyJuYoVWgikN5dCvmJhrmJjqCeN9X1ZnD0UCCldfG0bnsLNO05gx9GZ2Pbk0gms6U7HGieezJcAACAASURBVFiqTdq5SqI5rsPXzgl0BcPG/fu969TuE/FzeSqWo9z7hACef/YgpAQK2nWaRDv9nqgj8/OdwTGRUmJkroINfVn0d6UbmnJBh0uGzb3IPfrFz1gLoPGEPFpMpdY1b7nRSjWJ+6WUQkr5XCnl5f5/v2xn49rBjqMz+Pp93qxScpLisllx0I+chg3ivvw4Z7hsOehKJ9UEls5PoNPL7eguht2x/etCk/7eN5rH66/egpseOw7Lqb9ikHLd2yTU6Yfcl0v5N/jqVbboYkoxicmiUTU02MxQcKeYKBhY2+vFBQzbiwmYjjuvY6RnSPUc+1Mn5/CpW/di72g+JBSBcPzAWzLW+1uPSeg3AseVOK0vp2IS5PJt7M+hO5PEeX4W8fh0CV1p74aaSgo1/D+eDzvOk0UTQz3pqtzjTDn4LGes6Qo52K4rlXjYM5LH9sPTuHmHN/t8bU9WxQUu3NCHizb21TyGVD2CBAAQ1ALWneFkLTEcrTMcswKd5bo4OVtGMiFwWl9jMUzbpA6eqiah1R2ma8uJ2QrW9WbxB5dtwu8953T1uQNnWI9JhOuFZlIJ9GSTKJnBCoDkpGZSCWz0XWwaLm/mPKSYBInx3mwKF/pi+FGtnFi9JdKpo01mQjohVAf2jMGuKvdb3x9AMZCwGKbP9LIL1uP4TFkN4QLha2ZFiWGaQBd2hhsdgxsfPYa//Pb20LwUvRNBS9pOxsQkoiaNHidSMYnQBLr6WfR0SngxiZKJvmwKiYTAOUPdOEsTmK+8eAMA4I6nRtW2qfOT8uM3oXub9lmOTZcwW7aqFmnR26d/xqrlvv3JrkRvNoWi6cBxpYpXnb02aKs+kkj7iqsZXzRs9GRSGOhK162kpDvDyrWO+X6bLa32xPHZhvOR7tk7ro5zrUlohu2q3+G563pi5x6peTB+23ShS9fjOd8Q2ug7w43Eq36N13PD+qjR+r4szlzTpbZfD3KGT5XCEyt+Bbr794/j337xFCqWo26e+k22HnRy0IkRN+RCw59zleCHWrFcZNNJlfVr94pn//Grp/DtB4fVv/WLuX6xKhiNe7Q/2na0Yc3POPQbhF6kHgC2+zNYo3VqdeLK7LQC/dD7cmlvIoYR4wwrMex9n1IiVN/z5GwZl/zzbXjsyDSWgpOzFWzszyGb9sQwfS/zEcP6Reymx46rv/XvuCcTcYYj+VFCj0lE27C+L6symHQ8//q3noFPvfm52oQNG7l0Etl0AqlkIlYMk/PmOcPhy1HFcnHJ6f14z1XnY013JpTnmy1bahSEPvOuY54jsrY3o87JV1+6Ad2ZJAzbjY3z0FK2egeBXJmkL4bTSRFajU5HTaCzvJu4K8MiLePnNk/OeA5NLVGt3LFsWh0H6sRSuUAqtQZoYnimHMr5KmdYZYa9mITtBBELWoEum0qgJ+MJELpu0LbOGepWEwjVRKp5OMM0+tCXS2NtbxYDXWk86S/IctZQF6aKZs3JaLTIgV4zmc6ZTYNd6jNGS9gRJctRgprOOcD7Ll72zHUAvPsC4GVan/PR29UoSKWBM9xo4Q36nR3TnG/9mkwxiamYmET0Oqxfv+gabthBlQ1L+07jYhLpZELVGabYxwdedSF+8J4XqddcsKEXfbkU9o3kVSWQrnQQQ+nJpEK/V/07e+u1D+G/7thf0xk+pC0uUjIddZ0J4odhMUznNAC1YMw5vnDf0J+FK4POAB2zuHkfZdNBVyaJwe5M3Xt93BLMsTGQJibQlU0Hb/ryA3jZpzarknBx3LtvHIPdaTznjIG6zvA6/7y9eGN/rI4oaSMFritDQplep0ez+nPphiPU+vbHQ2LYG+1a15vFs07rDRbzaaBraDnzxarC0SorXgxTzc6poqkm3Ew34QzrvW5aAStODJMz7LgyuGBZ3jAN/dDbGZMYma3gq/ccxIdv3q0e03u4+pAGXbhq/YjH8wb+4Se78L2HD8+7HfqFmm4W1COlCRn1Lx5BfrEdRJ3hQiXqDAdDwfrIgH4xPTJZgum4StR3GiklbthySN1sRucq2DjgOV+G7QQ5tnnMYCdn+Ipz1uC6LYfw6ye9PKDuOtHKUoTuhtA5s6E/i9G5isqORW90p/Vl1flN7X/uGYN47XM34Qy/vB3gTRijSTuUMdYFO7V3bUxmGACuumA9/vE1F6M7kww5w/piE7R/cvvW9WZVJvMVF29Q4r9keu7UtCY6aPnlLk0MkzNLznAtAQuEJ9CREKTKB4An5GxH4sRsGadrxyUKCfC+XEoJbDqne3NBZpicVzr2s2UrFCfJVWWG05jT3Ds6DlRCrjubRMkInGHaFuU49X3FdapnSxY+c9veqlq1VJaLBM7a3oyqrXvxRq9awJGp+GouKrer8uaBO+mJ4fAkwejfjitVR2Kd1lHIpBI4Z203zhjswiPDXod3z0gepu3i4HghtO81PUFmWL+ONBoqJ/GlZ6L1Y0/ncF4buaJtTkfEcNx+LUeGFsOga3/cbyejTaDTJ7LqHQchhD9xzVF57y41uiAw1JMJRVr0iVvHpssYy1dqjvLR0ux0DCraewF/6fSIM0xQvOqSTf143tmDuOpZ3uT7ckR4RyveAN71sieTwmBXuu4ocDQL632+GGdYfb7a96p9o3mY/oTG/9ZMiCgjcxWcPdSN55w5gCdPzMXGdTxn2PsdXrixT/2Gws5weDRvrmypvH/gmnvHZqMvhhuJV30EWc+0G7aDXCqJf3vjs/F/fv9itcx7PV1TsRy10uJyXeQqysoXw/4JMlkw1Y13tmQ1nLilizR6qe7+EiPaEqE/3HoUH7xxFyq24wmAVALZVKKt1SRufPQYgPANWncuKjEuX3T/Dx6YxJu+vEWJkr2jjZeujBLnDNMNgLJCteIZUga51Dihd/2WQ/j/vvfovNpDP/T+XAq9uRSKZtgZLtdYulLPDdOPu91Ofi1G5ir46M+fxM93nvDyXbMVbOz3HFLDcue1DDFBIvGatz0fF5zWh8/e7s3kji7OoKOf63TOXHJ6P2xX4pA/2Sh6I6Bsdm82cI5IRF6woU+Ju2za+w2k/IUThnoy/pLYXu1XcsOGejKxuUcSVd2ZVFXViODv8FDp2t4MPvK6S/CR112C5545EMq8fvLWPaFybfWcYeFfHWuVVQMCMbz7xCz+zC+Mr7+e3LmTsxU1uTMOEjP9uZTaZhCTSKu2kujs1Vy0sDOcDLXBuwnaoWNHMQmKhxTNoLIL5cDP0YanSbDE3dTu3jeGL21+Gk/6Q77Uvm7NGQa86zCJ30v80ln66mc65OqqCXQJob6TtT0ZLTMcX00CCDp/oZhEMgEhBM4e6lbXbSpxRk4YXSPUBDrTQUUrEdjoxj7rR1L0Kga1OvxTRTNUg3oqItxI6K7pTqtrqWk7QYbcCZb5jo1J+Au+6GI4jp5sEgXDgumXPMypmIT3e9XvH3Qe0bW+YNjK2IiKIyqnB3hCOToaWPRXOCT0c5ra25dL46a/fgmuOGeNt3+V3/X+H+cMl0wH3dkkBrrTdSfQxTnDsaXV7Nr3KoJc0E0Dubr7nC6ZWNOdwaWb+jFXsUNxHcKwXJy/rgd//fJn4E3PP0MTw/pExvB3kq/YON3P5tL3QB2FDf3ZpibQ6bGHiXzYGc6mk/iNc4dw6aaBpka8R+cqcKU372K+1ZCWipUvhrVJO3TjjSvbFM2Kxrmajj/bXOfkbEXdsL/3yBH89NHjqFiuumD3+TejZrl77xhsx8WdT41iZ8zkgBu3e2K4Ox3cvA1tcYNySNhQabXw/m/bPYJHj8yoYay9I80V0NapxOTsyEGgMH/RdEKzUoPXByuW6UJv17EZTBdNPHhgUk2GapbAGU6jx3eGQx0D0479UeoVJUhQd3rCI0EXzZmyhdmyBcN2saE/pzLDNKSqX4QfPzaLD964q8pFIiYLJjKpBNb3ZvHC84fUyIXuDEezufqFlf7+zQs8J2arvxRq9EZA2de+XEqJUX11q4t9wZNLe84wnZ/re7M4OF7Eyz99N3649agS/EO9mdihXhJitPADdUYnQ2JYr7WbRHcmhbOGuvGul5wHIUSQeTUd7Do2g8OTJbUdbynbhHLD9OOjnOEaZdUAz1nLphLYvGdcLRtbNYHOF8Ob6jjD2VRwvSBBS9cNar9hBcPidEMCEMqAVsUkulL+hFbvGHWlk6oObzadwGBXGmN5Q/021vdm8eYrzsRrnrNRbbM3SxP6qn8/c9o5DASZ4SAm4TvDPVklZC453XeGa4hhaos+ge6W974U//XWy5FIiNjMMB1z+h5p9C+aGQZo1MM7Z+k8og5dNCZR8Z3hvlwK6aRoWJmHftO6M1xr1v1kwQx1MqudYe+7XtvrHTvbH62kY2s5gTNcawKd40qvrFxdMexNoiRnuDviDBN92ZS6b9K1Xhe50fvMfs1kKWsxtbLlqFKY4ZhE0Mb+XLi90UmcVC60phjOJOeVGdbbWeuxeqbW3pE8cukEnnPmQF0xPOWXkbzqWeuRSSbwhTv3h56XUqJiezGPf/jdi3Dmmm5NfFZPoAO80eB8xcJgVwY9maRqJ8XXNvTn1AhRPfTPp8ckaKSbCBbzqX08yJE/Y7CrqSo0y4EVL4ZpmGyyYIaGVvVZpjMlE8/92O2hurgkCnojM+/1E911JUbnKrjAnyByaKII03ExV7aUMNBLGzVi59EZvPP6rfjXW57E//7hDvxnpKTWTMnEwYkiBrrSyGsTEgw7cC5iJ9BFfsS0og8JpYPjtevSAsDX7ztY1Za4CXRx5ZLietNx4qtk2njzVx7EV+89iMmiiXzEhd+8dyy0hGkUPSbRl02F6gwD4Ysx4DlwmVQCN24/hpseOxbaRr2sczuhC/Vc2VKifONATsUkplVm2Gv3Awcm8Lov3Y8fbD2K+2t0FiYKJtb1ZCCEwPrerC+ynVAeMTp0WDId3L13DIbtqM7es88YwFBPBlv94WTqWNFF8bR+T2T05VKq86NfMJ9zhid40klPLJJwWd+XxfbD0yhbDg5NFFV2cqi7lhj2fkddmSSkVn1E/y3rzrAufoiudLBE94Fx7zdK37HpxwVCMYkUTaDz/l1rKebg9YnQuZVOhUXaqL8ccF1nOEmZ4WACHf1usykvZkKLcADhzvDvXhoI17g6w0AgGtb2ZlD0S49lU0k898wBHBwv4qTvlHZnkvjMH16GK84ZUtukzHCcQaA6dH6nhgRVXEyC2DTYhf5cCof9Zb13Hp0JLUJB4lFfae6CDX14/eVn+J/Rzwxrx5k+L4k3ak9/V1od20AM5zA6V/Enb/rOMIlh//rZl0sjmRBq0Q1y0Rs6w/5+j8/ET6ADgiolk0UzVCGgOjMcTFwCvOuA6QRi2Ft0wwl9Nh2K60zkDZXvj6PXv16qzHAmKPOni+Gh3gzyhoVvPzishsDz2qhD9PwYz1eCVf9MRznI9DsuGk6dmET4vkvndeAqe/+PK49ZNGx0Z1IY7Mogb9hVFU6IuEpGcee4Kq3WICbxrNP6sKY7U1d0ThdNrOnJ4KyhbvzFVefhpseOhypiWI6ElMHnBYI5A7qpFucM9+VS6MulQ5lhWnWwP5dG0XSU6Vcw7KrFkuh9uXQi7Axrtde99jR2hklfbRro4szwcoGc4cmCEXKT9F748ZkySqaDBw9MqsfIAdAv4oA38UGf/Ws5Ehec5olh0m4TBUOdzI16pzq03W8+eBj5il1VfoiWqXzR+d6Nii7ghuWqi13ZrJ50of/AHVfiST/LdVJzcg9O1I5K3LLrZGgylrfP4AQnAX48ZoZ4XM5K/3HMVWzcu28c24anYdrerPupognblUpkSynx/35nO77mVwWJI1+xkEwIdKWT/rBf2BkuRcQwLQbw5Mk5fHnzAbUN/f/e+2wcGJ9/jKQZ6OI2W7ZUx+T0Aa90lGG7atiUHNEfbj2qBJruiGwdnsInb90DwBOJdM7T5KGJghlyhk/MlEPu5VMn5/DO67fip48eV/nhnmwKV56zRi3nSefQ2UPdECK4Qetuju5OXey7fydnKsilkyExTO7meD4YrRnqDTLDekSXhBhFO54eK8CwHUwUTCUqdDEc/b16n8Vr17HpshJvdC0wVEwiuPkGZY2CCXT1yKbDrpwek0glE0rA0oIwcSQSAmu60zh9MFcVk8imvMmHx2fK6sZOE2z+6fcvDgmhF52/Fq95zkYM+cP8lL0MxHAWFcsrc5VNJfB8f/j5+i3D6Mkkccaa6jaSYIkTCvo5DASlv6qd4eB76c2mcM7aHhWTeP3VW/Ci/7hTPU/XLfouo0thB1GQ8Ep/+nvofM+kEqoNdJxO68/BsF3MlW1VKYhcNLp+5tLeaEHF8sqX0QppDUurxTjDUWeMqnRMauUnacVEnWh5uLxhwbIlutIJCOE5w3SNjFt0gx7LG3Z9ZzjjVd9R1SS0CXS6GF7TncETx+fw4Zt34/otwwD8EbcapccmCqYqjaZXLAFIwFk1J9BF20sxpmjd5fG8UTUxtmw56MkkVTWSOHF6YLwQuxhVXOm4Zlag2zOSxwUb+rz7fQ0xXLE8s4GO6Z+/9HwAXpk49RpVKk93YqvFZygzbAViuDeXCuWpN2imBRB8R/988xN49w1bQ+2jKM65a3tC11TDckPivDuTRDIh6o6gktA+fTDX9II9S82KF8M9Ga+u4mTRVEMUQNgdo7/1wD+JAv1iAAAf+/lu/MGX7gcQXPjOWRcuKj2eN5R7Ue/HESX6uuPT5ZA7esif2PXC87w6f+MF7wanr/QVVyBeF6SHJorqcb1+7d6R6qWmibG5Ck7OVkKRh3Bm2EXFcjBRMLAp4n41uuD8cOsR/Nl1j+D9P9zhfaa8oVwi+vHPli1ULLcqG6pDFwMhBHqzaRQNJxTlKBp2yP3uySbx4796Md74vDPUcacbu94Dv37LMF77hftrugs6J2fLDQuRFwxbuXBzmqs2qvJdvjOsLXMspTckdddTY3jzFWcim0pgLG/gOw8dxpanJ/CzHSdwzd0HYDneIgwkCPTqDZPajckTw8FPn+Iyu47NqItsbzaFF5w3hMOTJVzxr79Wy28+87RebOjLqQUP9BuYHr+4yJ8kNV0y0Z9LqZuZPqFpLF/BZNFEOinQlw0mjuk3SN0ZBoDXfvF+XHf/MCYLBtb4brK+6lmcM0z7phERIMiKGpZTlRmmz0Fiu94EOqB6Jr8+gU4XKY3qbd7y3pfh3S85LxAx/nmYSSVw+VmDeOzItDoPr7pgPX79/qvw5y87P7SNZ58xgC//6RXq+wmcYe/zqutf2UQ2lcBlZw4iIbzJbL998Ya6ue04IRicwxa2Dk+pDjq9h/LOa7XvpSebwtlru3F0qqR+L5YjldtIv8c13RmvLF/Ema8Xk6CSaBSTyOpiOBnEJACvdOBE1Bn229Plx3uomkTWP0ca5ffJEdONjErEGaNFCEbngkVIzlrTjamiGbrek8lAozAzJcvvvHkjBabjYuvwFDKpBM4aqu7E6OXmGsckqp3hdMQZ1js0ZMzMFC01MqSLI3LdKcJT1iYwA14HoWg6VaXViL5oTKLKGfb25crqOQNFw0FXJqXEcLTW8J6RObzis/fg6s0HkEqIUGc3Kngd18tlC+EJ/xu3H6saAZ0qegvKXLSxD/1daRi2G3sfoM4OHdPB7jR6MkmcmKng2nsP4LsPH9ZiL8G5nUsnkIqIT33Sc8VyMFex0JdLh7LBT48XVK1w6hRTtOHwZAlPnJgLdSTyho1cOoHTB3LhahJ+Z5Dw7rH1R7xJU1E07FSISqx4MSyE8GeXG5gsmmoJSr3kSiCGA0Go3Ime8M112+FpHBgvomw6gRge6gm9pmg6yi0abEIMbx2ewid+tafqdUV/H8MTRfzDT3biiROz/7e9L4+P4yjTfqq7p3tuSTPS6JZ12rJ8O/IR23EcJ3ac+yAhyeaEQAghfMASdhPOZVmWc7kCuwQIy/KxfCFAgCwLISEBQkIO27nsOE5839Z9zSHNaNTfH9VVXd1zaGTLsmL18/vlF0nu6enu6qp666nnfV7IEuHJBMz6amR0jE96w6NpHOqNY+uBXt6xxRWlaOdyTEj+yxUMj43p6Byi27yiN6bVTSLNA2vGNLEAIpvGThwUuXYvZmZh24NSu74vG4aGU3zSY6vjYUM7BmQWevBrCtwuGZGgxgfLoSya4SP9CSRSaQsTu7crarFkY89g49efxref2o2e6EhONvmrf3gTN3zveeP+zGCfbfdFAsxazSpteHzHcQyNjOKi+ZUoD7pxfGAYX/r9Tvzk+QOmS0osiZ7oCH9nxWC4L55Ea0UAfk3BvRfNtUyUbOLedmSAb795VRkXzqvA8voQemJJLsv4xCVt+MGt7fyzVmbYHE6W1Bbjwxe04LOXz8enL5uHz1+1wHJN/LpitIIa097az2lnhgGqce8YHEaZUWkOMN+30izMMDvHa0IwzDS0LIHOkyWBzmSGx5dJiBCDc/acK4Ju7rebC9WGUwJ7Fuw9dMkSltQV43Bfgu++qLKElvL85wPM7Wb2frGApi+WguaiBV4Yi3/hvPKs51BkCW6XlHW702TaR3Dj91/gjKHXzgz7bcxwyIvDfQmLbdd/PUc/ywLsoFExzc7Ma1wmIbpJmAl2gMlM0WDYkFsIMgmAsuUZmmEjAHW7ZHhUifsMa4qM2pAX244MZORBvHa4H91RWrVrMJGCZnjzsjkkIQTYAO0DZQENe7uifIyuDXm4tzgDY4arjB2F3liSa9xVmSbZ/m7bMaybXZYRPNLnY76X+YJhv7GTZneTyGCGfZl9S5SUicRHzEg8ZMFwzJbA3BdLIj2mW5Lm2M8BTclYgHLNsMBCs0PsjhKJ5Ch8hmYYyCSZxMqomiJZFqz2d1z0nR7TgY/+/FXc/5RV58sSQxtKfULQmTnns/GcLdgIIags9uBofwI/fu4AHnnpiFlExRZ8BoyCJAyxkTTvF1FDt82clIaMfJn93TG0GmMO1/ka5+gz3qXDfab0cGh4FH7NhYoij8UHXMyBYgh68ifk8WDYIMfeDkl0Z3wwDNCBmAUEjUatc9FejQXG3dERdA2N4K2OITxmVBHKNrkCpuE4AFQUaRkDNtMMF8IMX/vd5/Ddv+yxTAz1Rjb3wd44PvLwK3h4y2H8bPMh1IW8fLXFVm8jo2N8FZxIpvH1J97CnT95ibN8sWSaMw6i0be4NX84Rw33vniSr/zF4Hl41OyMI6kxvlpeVk8lHMyYO5ujhKh3Es8JAAd6xc5pTZLI54c8NDyKgMFCsYSf3ngKPk2BKkt80mMDKNvGLfK4kDRW8mzVLA46TNN6tJ9qDL/+xFu44Gt/wccf2Wb5/lcPDWBoeBSb9/fiM4++jnf952akx3Q8tbPDwvbs6hzCYYPxZ+/FQCKFjkFaPIE5kNAEOvM6Ht/RAU2RsKo5jPKghp3HBzE0MorOoRG+1dsdTaI7luTvrBgM98ZSCPs1bP/shfi7FXWWAI8Fb28eH+JFAXwaTUJ7+M6zUeJ1IZ6kDGp1sQfzq80SqWyQ1RTJ4sUrSQQfvmA26sJeNJT60BzxW64JoFvTPbERPjmwIMfCDBuBrGgHd6A3jn3dMdSXevnEXRF0o6bEwxlpESxg3nbY1ObxZFqhHDODvejGeMyw2yaTEMtTsyzzD13QkpV1zQUaDNO2UBUaDAPAi/uolCvbtng2sEVypyCTAGiwyM6xoiEMj0vGujmRnOfJJRFgk+uuzigtD5zOrhlmQZVEKNM1K+zF6JhuYetf2Eu3i5kjQ9CtYG5VkL87DHb7OMCUTHCZRIwxwzJfELBFS7mR/Hmo15TNcDeJpLlN7RGYYbdLwhWLq3CkP4Hn95pyulR6DNc98Dy+++c9GBoZxZgOHoAc609gx9FBHnAw/azbJaOpzIfdQjDcZNzjZfc/w8c7JkVj431vLIlUmrLULkXCc3t60DE4gksWVma0i/355FuI+YxiF2znjDPDQgKdLJG8ATVg3YFkOy91nBketQSafAchS18PZvke1s/FZDZ27p88f4Dnk7AKml4xGI6LQeQoHnnJlPypimQJPO3BsGk5aMYBj7/eYWFU2b1EglrOABww30lxgVFV7MH+nhiO9ifQHR3hJJN9gR2w+QTHkqP8POz7g26X4Secwu7OKMZ08HwmuzcwI592d5qkDSOUZoXpLgUbf9hi0HI9mgsdg8O48QfPZ/VL7jMKvbBF2ttBNzwzgmGfir1dMeg6eDAslk8Wg44vP7YTl97/DO8wbHAtsSUgHO4zGcwijwtFHmvQLMokBodTFjbhUK+ZzS46RuztjkGRCH511yr8q8Gkfe2Jt/DywX64ZIJ4Mo2GUh9CPhUSoczwqFGCmcskRsdwuD/BtVQBN80mZyzD/u4YH0Q6BoehyhJqS7x8AE4k03j6rS5+TWKFH+tqUZBmjKY5u8hYa7Y9k20CFVeJqbQOQoDzWyO4dGGlpVqN3S8xXzA8kEjxSY9tzfZEqVzFo8oWCy96jBkMA/QdyOYmwT53bCCBn754EN98chcCbhe2HuizBLkvGBPk60cH8fzeXhzpT+CpnZ1494+28CQ0gEpfRg1PahZ8DyRGqa1aEQ1UaAIdtR1j8eXerihProsE3XjLyNTuFLZ6D/bGkRwd4+8sY4g7BofRF6dV3hjsOkyAtsUrh/ogEetgzCZjezIpYAY79oAwF8r8NBCpC3kxNDyKN44N8XdFlY2tdYEtYkGwGKzu747hYG8cDaV+PnH7NQVPf+w83HL2rIzvZMxwXzzFAys2WScNVwVFNhkidu9mAl1hMolrz6rBDcvrcLNwDe85pxEXzivHNWfV5H8w9nO6ZN73ijwuzKsqgiIRvHyQjhciK5oPXDM8ZGWG6e4Vve6PbGjB/3xwTdb2ZfAaulI72DvMqk0BNIjiFfWMvsjkKz6NSpmYVOAVY4GyoLoIB3pi0HUdg8Mp+FQZiizhaMTlIQAAIABJREFU4fednSEFyWqtZvzMykn3x2nfoRIcKzPMZAdMFhcJaDzQHDYCX0IID4ZZMHDhvAoENAW/MOwtAbqATKTS6Bwa4WwgY9qP9Cdwyw9fwPf/ug+A2RZeVUZzxI89nVG+c3bDsjp84eoF2Nsdw1M7qSSJM8OGvKY7SnfoXDK1mnuzYwiKRHgVOTvE57OktjjrMaxN0mM6D2Y5MyzIJDwumY8ZC2uKLMcxiOMm23mpKHJDkQhNoEul+cKSBXAB4Z2TJQKfKlukVwysD4vM8PzqIkQCGn6+9TA++evtAMwKml5N4fZ44i7wi/t6LXMSS04FaH/PxQyL8queWBJbhWqKbJcwEnDz+SSbhIDt4InjcFWRG292DGFMpzufI8LOhAi7NVo8mebjO9O7c4/9kVG+08sWZuI8N2p4TwOw7GBGDas7VuiEafrZYtB+PVsP9OHZ3T34n1eP4Z8efR1ff8JMsh9IpFDkdfHx+e3gNTwzgmG/xoO1+rDPSFawukmwTvrzrYexoiGEdXPKENAUnuxQHnRDLEJ1SGCGgx4XinJkvwY9Lui6OVC8eXwI53z5T3h2Nw2evve0mRS2tyuKIo8LS+pK0GoMqH9+swvNET+fTBtKfZAlKv3oGhrhAyazIRqxbemzLUH2/Uf6E2gq80GRCMZ0GniUF7n5Z979o8245Ycv4lBvHJfd/wwe3nKIn4uxuGkjuU3UKR/tT4AQoKXcj09eMhe3rqIBQbZg2D7gNIR9ePC2ZVhjVIdiYNfMOntfPJnVqg2gzE5ZwFo+tieahFuR4VVlvhJmAwib+IuNRcxAIpXVZ5hZf718sB+ffXQH1s4uwz0bZ6MvnrKw6S8aSRDRkVF0R+lChCWfMXZwbEznCwox+B5IJHGkP8ETrDRFMuywRkwWqy/BB+RyoaRv5+AIz/zdZXwPu0dVkVDidWFPVxTpMZ0zsECmQwJ7t1/Y1wufqlhYXhYM+7TMgJcxDvbBMhcW1xXj6iXVuH55LQD6Ps4upwEqC86ysUXi9fTFU0ildTSW+fhg69NkSFL2SnGiHnh1UxgBt8Lfh2R6jE+G7FwsaCIFyyTo52pKvPjC1QsszM+5s8vwwM3t454j85ymQ0XIq8LtktFWFeT9vVBmmAUWzE9blCuIshQ7+2oH05XawcZA0d5OkSS+sLEn0LF+xwqQMH/iJXXFiCXT6IklMTiOL26+ohtcBhJP8d0Kdg2sDdwu2aiIR7+bBa+UmTP1kZpgQ+d2SXC7ZFy6qBK/33acj2uswFBfPMlJldnlpruQaGnH3i+PKqOpzE99Zg3tbcCt4Lr2WnhcMl9YMP1oWUCDIhFTJqFIfDFUXeKx9BcRTMKwvjWSs4IiYLYJ07TycsyCTMLtkviOx1VLqlFV5MY5Ldbx+kBPDNc98BwO9cb5YpNJmRJGlUM2BjEm3n7tAbcra9vbNcPR4VFUFrnx4icuwEc3zMZf3urCro4h/u8+VeZzokh27eqkz5bttKiKxPt7iVfNCNp4MBywWvQ9buwcA2ZgH/ar3PkhOzPMCBnzXFXFHk4ARUdGOYudyQxTmUR/PIkPPfQyDvTEeF/mCwtBM/xmxxBUReJEg5lMPWLRUFuZYWp1VyfsSrNn4LYxw0wbDQDP7u7GT188iD8Iz6Q/nkSx18XHXocZniZgL42qSFjdXJpRmaY/nkIkoOGG5bW4Z+Ns/Ohdy/Gjdy3HS5/ewFfzQbfLsoo91Bs3tW1GB44IHUZkhgGzc2wztgW3Hx3A468fx/9uO8YHlb1dMX58idfFB4D1rRFsaKOr/4ZS8+XuHBrmLyS7zkQybdFQsWQRNpEd6U+gusRj6ms1BeUB6rsZGxnFcwbD+dLBPmw7MoCfbRaD4WH8cUcHmj7+O2ze38tX3iOpMRzpS6DMr0FTZLznnEYsqim2fK8Ixgwzwo21jz35yS6TGNMzkyEYuoZG+PNn99YdHYHGmWFrMOCzMcMDifzM8P9uO4Zkegwf3TAbi2sp+/2qwWql0mPYeqAPy+tNOyoAeMlgD/YYA05XdIRvI/fFk/ydoMlDcS4tYUFh59AI/1tSqFfPGGSArtrZDgWb3BnzBdD3hE2uYpBmJhzR+28I+7gcwl6hjlWUsxfrACbODPs1BV+7bjEPQABw7SsPzrIk0C2sKcLli6pw70Wt/N8aS33wupgPcW5WU2Swbl/TyHMIAPDgQjxOnIgkUkACncvqZDAZYNcgS4TveDB2jxRwTQwumSZ+sfsVZSoTkW34NdmyDb79yACefqsrKwOmyARzKvyYXe7n41KxVwUhZjsxmzkm21paR/vUgZ6YscuTOxhmz8Yqk7C2QULY2uWaYeH48qDGA3H2LnYZfsts4qduEsxajf7tmrNqkEil8fttxwCYJcB7Y2Z/ZsGwuOunSISf1+2S+eKDjSE+TYEkEbSU+7GrI4rrHngOXzQcYtwuGSU+lQbDhsad3XtdyJq8LaLJ2AW989ymnMew7wbMOUqsQFfscYEQeg1MGrCgugh/u+98XLWkmp9DVSTs6ozihX29eGz7cb4ICPtVbkmXSKX5YoUFcPbdiJBPtQSeDG6V3u+wUfY8kTJt2W5cOQuaIuE//7afJzh6VIW/Q+Jcv7szilK/hmYjd0gTguGwX0UilbYlihtez8Z1N0f8mFcV5GMtu5eQT4VLlvLKJHrjKRBi1W/b7RZZGe9MZpjKJF7Y14vfvHIUY7p5TYyZZgWn4sk0dhwdRHOZX9gxobvJooMPAOwRqq1GjST0OhszzIpuWK/HbLdtRwaQHB2z+Lf3J1Io8apOMDzdUGqsxNa2lMGnKSjxqRaf0r44rd3+hasX4u71LXyicckSfyn9bgVFXhcUiWBW2MttmjSFHnPb6gbcc+Ecfk428Nm3atjKdG9XFF98bCdaKwL49KVtAOhKnnVgQgi3OVo3pwxrW8rwj5tacamhD4sENHQOjfBtFb+RdHB80AyQAZNFjI6MIjYyiv54CtXFppG3X1NQUeRGIpXGj/62n3+OafkYO1VV5MYrB/vxnh9vAUBfbp/xncOjaRwdSFhsmdhAlZ0ZZk4dtF3sCV8MnBkWpBq9QrsNp9K48jvP4qmdHYgn00IwbMgkYpQZ9qkK1wzbZRI84zie5N8XN/wYqWG9mWCjSARzKgKYUxGAqkh8stvbRR06rltWa7G5etVgjdhWlMgk98VTliSLZHqMB51ikFIjPFPODAezuxIwv0q2BQ2wYDhquXfA3FZm5yz1a1jVRBdlXlvQy7ZpswWcomZ4IhAXjrN5MJzpJsGuxe2S8a0blmB1k8lGNZT6eLWsfFv8ktGfK4vcqAt7EfapprWaEAx7bcwwQHXD41qrKexZTmYwTK+lxKtyVm+JETC6ZCkv02eHWMBgoeEeQb+j8DajulKzL3/18Tdx7y9fy5oopMoSNs2vxOMfOZePpbJEEPKq/B1yu2SUeF3ojdFdOaZD398dx+BwKqPogghurWaRSdDvKfaa7JzJfFut1QDah9jYNreSvn+7O6MYHh0zGVwmkxg1JSVL60rQUOrDf79wEK8e6ud9ri+W5GN8WUBD2Kda/GOHRkb5OTwumSdyv3qon3txA0BLJICtB/rwwr5eHrS4XTLCPhXd0SRSo2NQZXNrvzZPMLyqqRQ7P7cJyxtCOY8BzByL/njScFeg51YkKh8q8lBi5jOXt+FTl7ZxKZw4Hoj9efP+Xs4Mh3wqL6UeF2zFcgXD37x+MT5+8dyMa1Rlifo+J9N8TmGfDflUXNBWjj/u6ODvqM+w/yryuCxz/e7OKJojPh5wswqZAB0DdZ0m2jL9NHtHmFRqaV0x6sM+7O+O4UBPDL/YehidAhGTTafM0BdLotjjsixkq22FeFjlwuzM8KhlDmHzJ5sfKTNMv//lg31cIgHQ/hc2dpPZe9VQ6sPuzigPYKnVHT1HSKgYSa0FbQl0WfonkwsBdAFS5HGZ8hZHJjE9wNiCTfOpOX2VkcHJMJBI8i0VO9gq2a8pKPK4MCvsRX3Yh0N9cct23uWLqvDO9lo+oYo+w/Q7aOfYbQQmWw70YW9XDFcuqUa5sDosFrTJNSUe+FQZ7bNCUGQJ71/XxIPrlvIA3uoY4klwmktCqV/FSwetVesiQTMYZlIRCzPsVvgxj7x0mN/vNiGxpcTrwqywDy/u77VavhjJXsOpMRztt1bYYlngWYNhrsMytK22hC+JUPaLM8NDwzwgER0l3uoYwiuH+vFLQ9/NPl8hBItuY3uSrUxZ8Md0qRZmOJHik0xshDp5iBrm2eUBXma7rTLIg102aDRF/FjREOLV29iAyphhMXO3P57E4PCoRXrDmWHhGYsMKkt+YtXf7FvlxwaGQQgs7RARJBVWZtjKpIV8KlYbMhV7VScm38gaDGtmUtBEwNpKkQjf7WDBip+zzVIGA8pYi6BboROti8kkcgfDAPCne9bhqY+uA0DvuSdGpSzpMZ1v6bMgSFyMSBIZt+gGu/eQL5PROlFwtllos8UGM6xNUHIhJpAVeVy8HLK9EmE++GwJdAd74jg6QEuu2gs+ZNOjA/S5+4VdhwrjvSrxqqgNeSARmhw5kBjNywy7BebS/E4z0Kw2FoNqnmD4QqFQyaqmUjSW+vDxX23Ds7u7ef/zqDIvusG+kxCCd7bX4pVD/bjiO89i5/EhSITKqdgYX+RxoTzozii1ywgSryqjssgNnypjcHiU66gBYHa5P8PrVlMkhP0qemMjGEmPwaUQPrbkY4bFZ5UPrO/0xVNQZMLbTyxk4lFlRAJu3L6mgV+rqO0Xg+EtB/rQHR0xSovL8GpUJjGcTPNy3zwYtumDW8oDGQEiAK7hjifTfLdRDKTPaS5F59AIZ+qZm0lzxM/JAF3XsasziuaIn88Dqmwyw2x8vPI7z+Kff/s6AHMXc0NbOe69qBWfuWwe6sM+HB0Yxv1P7cY9P38VezqjfDzjbhJZEsd7DeccEUwuxK7hSA5muCxAA1mx8BRjhJnkhBbdMDzBk+mMpMkyWzC8rL4EA4kUl4wNCeWx60JeHDSK4og7IwxswcnmJ9YWzKbTkUlMU6xpLsUNy2t5MFxT4uEZ/QBdxRTnqNDjFibbq5bU4MYVs1Ab8vBMZLu+iW8bqzmCYYMl3GtsTyyqKUbAcDwQjweAu89rxleuXZS1utDVS6uRSuv4uaHp1RQZbZVBi1cyYJVJsFVndbEZDAcMmQRAt0zWzi4FIcDrR8zzRAJmvfUPrm/G/Gprqd3hFE2gEwcxWgZXye4mMZIGIebgwyZ8MTDzqwofUDoHRziTYt3ioc9yq5GgxgK/iiK3hf0SAwr2M5sA2ODVFaX6a1bffXA4xVlo9m4sEFwUFtUUYfuRAaTHdD5A1ZZ48OCty/CDW9stbXZ0YJg+f2EB1mdoIyuFwL3GmMTFIGW1oKMuM54Pa9NFteb1MFQG3ZbvZu/T/OogZ2ABM5BgAXbYr3LNtn3g4pphNXNiZc/PrikbD2GfBokA9aU+fr0sCOElmLPIMoq8LgTdChrK/HSCLIAZBigLwo4N+zX0RJM8oLAzw3aZRK7gjoEdfypkEiVCss2ssBclXpfFMqsQiNvbALC8Pjzh6/ELCXTpMR2HhIVdra1QR67FwwfOa8Ztqxr478x2KexToSkyqoo9ONATw6CQDJsNJV4X3re2EetbzcQx5ibhUeWMRSVjscR2vWnlLPzLlfOxaV4FSv0qHr7zbBR7qBSBjflul4xEciyjAtcdaxvx2w+uwVevXYQL5kZw5eJqDKfGuFd4kceFiizVBkVmmBCC9Ubim2XBncX1gSayaegxNMOaLHGrPHEX6ETB+lt/PAmXbFrZsXasCLqz6nj9FmaY3q+mSOiNJbF5fx8POL0u+u7ERZkE1wwXPm6wxQlnhoVAeo0hNXz89Q76nUZfbqsM4o2jg9B1HV1DIxgaHkVLJMCJGDGBTpTp/fSFg3jz+BDXDPs0BXee2wSfpqDeqCvAdMN7u2MoMz7LZElZZRLZgmHjPVlkJCUyks7ODDeV+ZFMj+H5vT382jfNr4CqSJwZDnqsUk77u1QW0NAVFYNhumOwu5PmlESFYHhW2IsDPXHus5yZQEffhxUNIdx69iz8oyFf298dw9gYdUoq8apCAp0TDE8LRIJufOHqhbzz1pR4EU+muZaIySSygTGlAbeC29c04N1rGlBb4sVAgiZQ2RkM9h1uJVMzPJxK42BvnAdXhAALaopACOETqTjotNeHcPGC7LY5rRVBLKwpwv97kQXDEuZVmcERm2gigkyC6ZFqSjz8ZfYZMgmGBdVFCPs0i39kJKjhznVNWNNciveubURDKQ1M3S4JbkXC0f4EkqNjGQU3/DmSbuLJNLyGxylgBmSaQqsGhX0aAm4F248MYMPX/oIj/Qm0GSvQnlgSz+zqxmX3P4Nth2nAzj16g+aAxKQEbpfMPyt+F2OoApoCiZgSBhbQf+bR1/Eff97LnwkAzK8xn+/CmmLEk2ns6YriUF8cXlVGyKdyPR8b5Nh37+2K0ffFbbIwg4kUagRmJ5tMoqXcTGxig3VlkQde1WqFxT5r3za9fU0DPnNZG35x5yoL28C2/cp4MKzxxAk7uGY4n0xiAiwjQLft7N67bAJgjLBdu8ywaX4FNsyl9y4m0BWKUp+KvniST3QmE2gtzgBQmcT4bhJGkJ3Fh/VEYZ7TnKAJIVhSV1Jw8hzDvRfNxX0XteL/3r4CALB0FmWY8xXascMraIaPDw5bvJTt7GS2xTsAXLG4muc+AODjDgsQZoW92N8THzeBjhCC+y6ea0n6W9VUiuvaa1FT4uHvq2o8w/PnRnDXuiY+bjHctHIWvnvzWdyL/rplNKmTJ5K5ZAwN090hMThhso5rzqrBD25dhhVGRdC93TGoCvVkZvcmjvVcM2y8s+89hy4MxMCJLVhFcoYQgrBP5QlsAbeLBxfjMcOFgM1Z/YkUXEKRE7YI/OLVC/H5Kxfk/Bxg7vRctqgKAM1fYHOaV6PBYXpM523NrP7YzlIh8LhkJJKjGTIJgM7p9WEvnjScOHgwXBXE0AiVF7BkseaIn1+vaK0m9l+fquDBZ/Zyll4cO1lSmsj+lgl5GsxONT2mcxu+kdE03uoYyqhC6XbJeMfSGty0kiac52KGmf575/EhzKsqwr4vXIxLF1bBLSTasnLMDK3ZgmHD2x2wBsPU4cpcXM0K+3C0P8H7QjZ3C4C6c332ivm4YVktXDLB/p44txgUZRJvBzeJ/HTKGQrGHBzuS6DY66IyiZzMcKb3aX0pezEHcU5LmeX4gNu6bWxqUlPY0xWFrgMXzC3HL7YeRkvEz88b9qs4NjA8rpejiOuX1eG1w9TvVlNkvv1JCM3aP7rtONfxdkdplRyXTFDm16wyCWErfW5lEJEATTCqLvYgPaajLuTF5YuqcLkx0DUYQZNECDSXzKsRVdtYCr+mWIJqhkRqFB7VrEomMmplfo0PmFsE+xq2yu2NJfHm8SFsOzJgkR2wzzLUlHhwbGAYmkuyBLF2bZckEQQ9Ls7uMhaU2RsBNHHpr7u6LfZEjJV99VA/DvUmUFviteg4K4JuHOiJY+3sMuw4Noi3OoZwpC+BurAXB3vi6I6OYMjwynxxX6+lfCibeCuManQumSCV1rnOzaPK+PM96xDyqfjmk7uQHB3D7HI/jvQnMoLh2pAX71rdADvsRQrY/x//yNoMVqIsQH20s1kemZrhiTHDAPDtG5da2oxNSqyoQDZmGAC+fM0i/jNbrNp1zvlQX+rDmA5eYpwF1F4XZYkkIfiVCIE8jkzCq9HP5dO5ThTZmGEAuGfjHAsrWwhoAGoGoayC5bL6/FpSEX5DM6zrOg7atv9ZQFYRdOP44PC4iwcG1tdYf68P+/Doq0dp7sQEn2Vd2IsvXbMQgDm+s8VO2K/hHza15vwswzVn1eCbT+7iiUMeVeL5F/nkBswhYVdHFCFD482kWhVBN5fnfOOP1HqKvbMLa4oR8qmWJKqqIjf+YdMclAfc+OjPX+V/D/lUnnzLChsBkxMMiwl0lUE3Z4bZ/3MtkrNphi9ZUIn+eAp/fKOD+9OLbj4BtwsumVqtMc/pQuFVZXRFR/huo3036LzWCC/6wsYORka8fnSAJ0y2lPstrg0sX45JOCqL3GiO+PHGsSG0z6J9RCzKw4JhEeI4xoLhx7Yfxwd++hJ++f6zcbgvgZ5YEu/IYrH4b++k49knf7WdkzL2MbhRWMjVlHj4XOMxpDZuFyVhGFsedCsWuSAAPq/3xKgHcF3IC68qY09XlJNHjHxpn1WCMZ06RQAmucfASED2LBRZQm3Ii4c2H+Q7tsVeNcMFZDpjRjDDdpjBcBzxZBqptJ5TMyzKJBjYKi2V1nPLJIStNlWRMJhI4Q/GFs4mQ6+2sMYMrpjecCLB8DvOsmbzzjOC4bBPwyzjJa0p8SDsU/HW8SFu3yVJhE82AU2BR5U5Y9lWFeQMa3lQw8/vPBsfExIDAaDBuP/OoRFoisQnD3u5Wb+m4Ehfgg9CDDT5TuYBjMh+fezCObh7fbPF7eJjF87B1UuqEdAU9MaSXM8s2uOpsmRZ0Igs63yBMV/eGMI3rltsWcQUe1x8EKrKole7fnkdHrpjpaXYRGMpXci8ergfh/viGeVQ2QS3prkUfk3BSwf78PrRAbREAgj5VBwyvo+txKuFAY4NHGxxYz4nc9EQCdKSyGV+DR6XzCUWhW6bsokuZNNtzy4P8HeHQZYI7r9hCW49uz7jPIEJWquJWFpXYgneRZ9fe4nkXPAWKJMQcfGCSpQFNPzzb3fAryncp9WryhmsJvOqzYfbVtXjB7e2W4LokwVbGNh1yG1VQYve9URQFtDwyqc3jOsyIMKnKdB1miTDFo5s7cfakLH8hdrIscmaBcOzywM8iXUi46AdrC8wy8FCURvy4tqzavApI6FZdCHJl2zIrv/NjiE0RWjf4cFwkRshn4qQT+VzgvheP3ffevzqrtX8d0II7lrXjHPnWEkW1j89Lpn7/AJUNnSy8Bvji67TvAfODI+zCBRlU21VQfhUGQtqivDvNy7Fu1c34H1rm4xrVnhCnVeVLXk4E0kE3dBWjmd393A7Urve+K51zfxn9oznVAQgEeC5PT34z7/tx8a2ckQCblMzrJgJdOy9Pa81gpZIALs7o5zRFIPBIq+Lu/CwxYiY/B30uDCYSGG7UYzimV09+PFzB1Af9uIcm32oiNKAhvSYbngtW9u1xKfy8V9MVGfvFNPusnmztSKY8WzLAhpSaR17u2MI+VVIEkFjGU2iY4n9bLelvb4ELplwUsjuJrGqKYzbVtVbFtTMpeuJHR38d1kicLskpwLddAUbLLcc6MMDf9kDABYPVhFlAQ2Laot58goA1IV8PLHHPmhn810t8rjwsy2H8K0nd+HyRVU4rzWC81sjuGJxFT+m1JcpkxgPmiLj9jWU9Qu4FdSWeA13CA3nt0awsa0cJV4VcyoC2Hl8EPu6o7zzisEmQAftYq8LFUE3X+WXB92oDXkzJCRsNXi0P2FhTOyJD8Ojaew4NogrvvMML48K0GDP45L5YCpm4W+cV4HVzaX8+lrK/fjAec2IBN0I+VV0Dg1bbG3EwUjs/GzAUGViGai8LhlXLqm2TNhFAjNckyUYLvVrWNlo1VlKEsGC6iK8dngAB3vj/J1iYEmRdSEvltQV47evHUN3NImVjSEUe1Uc6KFselWxG7JELM+ODUjvWl0PwGyjbJZDkaCG0oDKJ+S6cOb1Z4Mi0YBTzMjOh03zK/mOiAguC5pgAl02MKkF8/4cLylO/N5CjhU/8x6j39x1XhN/P8qCWobuVyKEs+i5UFnkwdrZZXmPmSjYBB2ahGAnG4q9asH2bIDVHeZgbxyyRDDXqPbHqqexxdt4iweGSptMQpTM5EugGw+M7Mi2KzUevnLtIj6mui3BcB5mWOg7LRF6D6z/i+ycKQOynjebrMQuuWG/0yBFwuWLqixOMycDUWI0pzyQkUCXC4pMq/RpioT1rRG89OkNvIrmpy9r4zk6Pk3m7KvotzyRBSwAfPiC2ThvThkvDW/fOSoLaPjUpW2oLvbwOcttOHf8+PkDGBoexUc2zAZAx3xFItAUmbfLpvkV+OQlc/GpS9rQYiQyshwfj21hXl/qg19T+D2Kc0yRERQyGdLPNh/E1gN9uGnlrLwLZnYdly+uyvpOsLwZca5gbi5sQc80w9kqDrJrfPP4II93msv82NsVw66OKCqL3DwI96oKltSV4E9GMGwvsFLsVfFPl8+zPJc7z23CzStn4QbDQ57NwdRab/oHwzNSJlHkoebUDz6zz/xbngS633xgteVvqiKhLuTFvu5YxqCdzXfVq8o40DOCeVVBfOXahZAlggdvW2b5XDbNcCH45CVzcdWSar4y3NBWjpBPRXt9CO3Gqq21IoifvHAAqfQYPnR+i+U62ep6cW0xhlNjIMQMHnNZeDHmsDbk5WL8qqLMJItVTaXY1RHFyOgYntvTg4sM/XMiSctlevMEYqxTtgi6wPKAG8/u7kFydAwtET92dUaxvjWCH/1tf0agWF1Mg1O7L3G2wCbocfEtPZEZdsmEM/vZsKi2GN81FlP27co1zaV45WA/KovdWFYfwl930QF8ZWMYj20/zm2Xijwu1IW8FteIuZVB7PnXi3mwwhjLQJbJY3VTKbqjI3xBMRFm2KtSPXVjqc+SXDcRyBKVT9gHyxMBmwwYW5NLJiHCZIYn9v23rqpHiU/FlYvN3ZUPrm/BbavqLcdJZPwKdKcCPBj2Zy6ATgfY842NpHGgN47qYg+aI37sODaIJbXF+P4t7ZhbGcB//HnPuIsHBtbX2LsrahyDWSQ5hWKygkRxDM9XzjgkkAWsH4nMsP18hfQVO6vHdgjYovxbNywZ9xyFQpHNEvBzKgKoC1FSpZCxhFavo/NGrgWDGDD5NAUhH/W1z5WnkwuYE4nTAAAPNklEQVSyRPCVaxeh/V/+CABZZVu3r2ngixmGey9qxZ/e7MSy+hAfZyVD991Y5uNEiF9VeLVDVgiIFVWxJwhfvbQGxwcSWFpXggef2WeRTswu9+OpnZ3c2/vowDDcLgnXnlWb9/52GsHzdcvqsv57Yxl1dBLfb7Y7yrT4JT4Vi2uLcf7czNLqTMrRMTjCd0ubyvz49StH8erh/ozCO6uawnhxXy+qiz0ZBVayYeO8Cmw0dq0+uL6F92/mAjLdcVLBMCFkE4BvApAB/EDX9S9OylVNAaqLPfzlA7L75uVDY6mPBsO2Dmkm0Jmdh8kI7r2oNeeAwRK7JhoME0Is2/dfv25xxjGtFQGeOc/cCUSfYcCqw2QaYrF4g4iQT8XD7zsbcyoCWPzPjwMAPnvF/IwB/L6LWnHPxjlY+rkn8Mzubh4Mx5Oj8KoKVjaGsPNYeVZWng10YpB248o6fOihVwAAH904G/c9sg1XLanGj/6232LtA5iSDWbF9uV3LLTogEWwQdmvKZzdairzIehx8WSDbLhxRR0Phu0T8DktZVyK0V5PNX6VRW7UhbyW+y3xqfj1Xau5qTyDyNr5NAVlfi3rliLztn6rYwirmsKWoDofXLIEr0tGS3kAT92zrqDP5MK/XbuIs4MnA9Y3XLKESxZWoql0/HN6ToAZBmhg8s526+Tk15QMtqqQBLpTAfYsQhMMGE4V2MIkNjKKPZ10h6m1MoCndtJntqGtnI8xhTLDs8JefOkdC7BpHh0Xir0qyoM0UDoZmcTJfFaEWIRFlCbYEfS4IBFaFIgFUXUhL+aUByyFeETLtkJw8YIKnuTdVhXEpnkVPG9jsuHXFIyMJtFaEURjmR/bP3thgZ+TkUqPJ6cw+9TSumI8eGs7XjnUz5nOiaDUr+HBW9vx0OZDBc/Z588tx/lZSlb/2iC5Pv2b7XC7rLkCzQbD/8axQXhccgaje7OR8KbrOp67b70l7+bc2RF850970BNLYll9CTbv78OVi6vHlbR87sr5eGz7Me4sYcfs8gAIye4tzUgjlyzx+7JDZK9bDW9t5l2+tyuG8+ZYA+irl9Rgx9FB3HfxXB6fFAqRVPKq8pmdQEcIkQF8B8AGAIcBbCaEPKrr+o7JurhTiZoSL3Z1RvGT21fgoc0HsSDPYJcNTRE/ntzZmUUznCmTeN/aRrx8qD+j3LAIpmGa6Gq5ELAX36fKXO7BrdWyrK5ZYCl2cDuYkfuXrl6I/T0xS5Y4AyEEqkKwsjGEp3d14aEXD+K3rx3DK4f6cdH8SqxqKuWFHuzgzLAQDF+2sAo/fGYf9nXHsLGtApvm00m0IujOYGbF8pMA8M5ltXjnsuwrc+YT/J5zGhDyqfjJ7SswvzqI3Z1Riy7ZjtqQF5+/aj4+8avtPIjOhsW1xXDJBCsbwyCE8DauLvZgSW3xuExaidc17pb27PIAfvrelXmPsV97NvufE8HGk9SwMkQCGgihOxKXLixs0jfdJE7NJpdHlU/ZufOBSUbGk69MFdgz+PXLR7Dj2CA+eclc3LRyFi5bWMXfX1WR4FPlgjXDhJAMFmxORRAdg10nJZMghODvVtRZpG0nAuZacMXiqrzaVlmifbo3luTjlUeV8YePrLUcFzSquRXKev/7jWfxn/2agu/efFaeo08OPk1BXzxpca8pBH63wqu05YJoc8aCqmy5GYUiV3B7otg0vyJjHqde0XRhdtPK3IwuISRjnlxSR+1Sh0ZG8e7VDWitCOKOtY3jXsfNK2fxIDsbblheR3N6hO977MPnYDStF6S9FoPhD66nO8RrWkpxzVk1PKFfRF3Yi+/d0j7ueceDVz3zmeHlAHbrur4XAAghDwG4AsDbIhi+Y20jNraV4+ymMM5umrjvZqOhnxwvgQ4A7stSUceOTfMrkEiOcmZhMtESoSvK5Q0hPlGJ1mp2NEf8IAQZnSMbcgWYIlY3l+KPb3Ti3ke2YVbYi/eva8KNK3J3esB8ruLzkCSCB25ux7GBhGWl/ov3n53RDk1lfkQCGu7ZaE3+y4Z3LK3Gq4f6+fYa86xsLyDb/sYVs3Bde23egNarKvj+Le18G4rZ1dy2qr6gLeXPXj4fo2P5J5yJ4t5NrdDHP2xKURvy4oX7zudFYArBguoizK8O8v442Xjg5rMyKiNOBbhMYpoFww8+uw+tFQHcuqoeLiODXESxVy04GM6G1ooAnn6r66TZ3X+9KtMKbKK4aH4FXj7Yh09c3DbusSVe6pKQ77ovX1SFhlLfhFm2qYBPU1Af9k1Y++9TFUgkf6DDCmwUstV+OpCLlJlfVQRdH8DfGzrjQuGSJaxpKcXvtx/HwtpiviN6svCockbuSmtFYTuBAJ3z779hCdrrSyzt/Lkr5qM+7OX658lGW1Vw3GTM6YCTCYarARwSfj8MYIX9IELIHQDuAIC6uuxamNOB5Q2hcctU5sPSWdTvs9G21bO8IYQ1zaU5JQa54NcU3JwlW38y4FFl/OOmViytMy15VjSE8IHzmrLaK7WUB7D1kxsmbSK+tr0W6TEdKxvDmFeVmeWaDe9YWo1IQMvwZawocmcY2tuT1wC6GHnxExcUdH23nF2Pm1bkT27Ih0ICWtET+KaVdeiJJfF3KwrrD7msjU4Gk+l8MJmYSCAMAI1lfvz2g+ecoquBxbt7KlFbQstGT5dguDnix6qmMEI+FR++oCVnwDu3MnhSdl+XLKjEvu7YaVmA2BEJuvGN6wvT5jZH/OMuAtwueUJ2dlOJi4wCDhPFe89p5JZvubCyMYwHnt5bEHEynfClaxYilR7LcHYoBLetqkfArWR4759uXJZFZuNRZdxtMMWnAl+4euEpO/dkguj6ifFDhJBrAGzSdf09xu83A1ih6/rduT7T3t6ub9my5YS+bzoiPaZPKCPbgQMHDgqBrtPKTyfi3+xg6pFKM7309GfATgfGxvRpuwB3cOaCELJV1/WCtB4nwwwfASAu9WqMv80YOIGwAwcOTgXyZec7mH5wguD8cAJhB9MdJ9ODNwNoIYQ0EEJUANcDeHRyLsuBAwcOHDhw4MCBg1OPE2aGdV0fJYTcDeAPoNZqP9R1/fVJuzIHDhw4cODAgQMHDk4xTsozSNf13wH43SRdiwMHDhw4cODAgQMHUwpH6OTAgQMHDhw4cOBgxsIJhh04cODAgQMHDhzMWJywtdoJfRkhQwDeHOewOgAHp+ByphOKAAyc7ouYYszEdgactp4pcNr5zIfTxjMHTlu/PTFH1/XA+IdNfTC8ZTzPN0JIl67rZVN1TdMBhJDv6bp+x+m+jqnETGxnwGnrmQKnnc98OG08c+C09dsThcScDNNRJtF/ui/gNOB/TvcFnAbMxHYGnLaeKXDa+cyH08YzB05bn+GYjsHwTNuKgK7rM7Gjzbh2Bpy2nilw2vnMh9PGMwdOW5/5mOpg+HuTdIyDtz+cdp45cNp6ZsBp5zMfThvPHJwJbV3wPUypZtiBAwcOHDhw4MCBg+mE6SiTcODAgQMHDhw4cOBgSnDKg2FCSC0h5E+EkB2EkNcJIR8y/h4ihDxBCNll/L/E+DshhHyLELKbEPIaIWSp7XxBQshhQsi3T/W1Oygck9nOhJA6QsjjhJA3jPPVn567cpANk9zWXzbO8YZxDDld9+XAihNo51ZCyHOEkBFCyD22c20ihLxpvAP3no77cZCJyWrjXOdxMH0wmf3Z+HeZEPIyIeS3U30vpwJTwQyPAvioruttAFYC+AAhpA3AvQCe1HW9BcCTxu8AcBGAFuO/OwD8h+18nwPw9BRct4OJYTLb+ccAvqLr+lwAywF0Ts0tOCgQk9LWhJBVAFYDWAhgPoBlAM6dwvtwkB8TbedeAP8HwFfFkxBCZADfAX0P2gDcYJzHwenHpLRxnvM4mD6YrLZm+BCAN07tJU8dTnkwrOv6MV3XXzJ+HgJ9eNUArgDwX8Zh/wXgSuPnKwD8WKd4HkAxIaQSAAghZwEoB/D4qb5uBxPDZLWz0TkVXdefMM4V1XU9PpX34iA/JrFP6wDcAFQAGgAXgI4puxEHeTHRdtZ1vVPX9c0AUrZTLQewW9f1vbquJwE8ZJzDwWnGZLVxnvM4mCaYxP4MQkgNgEsA/GAKLn1KMKWaYWO7ewmAFwCU67p+zPin46BBLkAb55DwscMAqgkhEoB/A5BB1zuYXjiZdgYwG0A/IeQRYwvmKwaz5GAa4mTaWtf15wD8CcAx478/6Lp+xjANZxIKbOdcyNXXHUwjnGQb5zqPg2mISWjrbwD4BwBjp+L6TgemLBgmhPgB/BLAh3VdHxT/TaeWFuPZWtwF4He6rh8+RZfoYBIwCe2sADgHdNGzDEAjgNsm/0odnCxOtq0JIc0A5gKoAQ2O1hNCzjlFl+vgBDEJfdrBNMdktXG+8ziYHpiEcftSAJ26rm89dVc59ZiSYJgQ4gJ9+P+t6/ojxp87BPlDJUxd6BEAtcLHa4y/nQ3gbkLIflANyy2EkC9OweU7KBCT1M6HAbxibKmOAvg1AEsSpYPTj0lq66sAPG9IYaIAfg/azx1ME0ywnXMhV/s7mAaYpDbOdR4H0wiT1NarAVxuxGIPgZIYPzlFlzxlmAo3CQLgQQBv6Lr+NeGfHgVwq/HzrQB+I/z9FkKxEsCAoXW5Udf1Ol3X60FZwx/ruu5kJU8TTFY7A9gMqillNdHXA9hxym/AQcGYxLY+COBcQohiDNLn4gxKyHi74wTaORc2A2ghhDQQQlQA1xvncHCaMVltnOc8DqYJJqutdV2/T9f1GiMWux7AU7qu33QKLnlKccqLbhBC1gD4K4BtMPUlHwfVqjwMoA7AAQDv1HW912iwbwPYBCAO4F26rm+xnfM2AO26rt99Si/eQcGYzHYmhGwA1YcTAFsB3GEk3jiYBpistja04P8OYC3o1txjuq7//ZTejIOcOIF2rgCwBUDQOD4KoE3X9UFCyMWgOkMZwA91Xf/8lN6Mg6yYrDYGdYTJOI+u67+boltxMA4msz8L51wH4B5d1y+dqvs4VXAq0Dlw4MCBAwcOHDiYsXAq0Dlw4MCBAwcOHDiYsXCCYQcOHDhw4MCBAwczFk4w7MCBAwcOHDhw4GDGwgmGHThw4MCBAwcOHMxYOMGwAwcOHDhw4MCBgxkLJxh24MCBAwcOHDhwMGPhBMMOHDhw4MCBAwcOZiycYNiBAwcOHDhw4MDBjMX/B17Mg46yVzpQAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the federal funds rate data\n", "from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns\n", "dta_areturns = pd.Series(areturns, index=pd.date_range('2004-05-04', '2014-5-03', freq='W'))\n", "\n", "# Plot the data\n", "dta_areturns.plot(title='Absolute returns, S&P500', figsize=(12,3))\n", "\n", "# Fit the model\n", "mod_areturns = sm.tsa.MarkovRegression(\n", " dta_areturns.iloc[1:], k_regimes=2, exog=dta_areturns.iloc[:-1], switching_variance=True)\n", "res_areturns = mod_areturns.fit()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>y</td> <th> No. Observations: </th> <td>520</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-745.798</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 12 Jun 2020</td> <th> AIC </th> <td>1507.595</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>07:42:18</td> <th> BIC </th> <td>1541.626</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>05-16-2004</td> <th> HQIC </th> <td>1520.926</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 04-27-2014</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 0.7641</td> <td> 0.078</td> <td> 9.761</td> <td> 0.000</td> <td> 0.611</td> <td> 0.918</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.0791</td> <td> 0.030</td> <td> 2.620</td> <td> 0.009</td> <td> 0.020</td> <td> 0.138</td>\n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.3476</td> <td> 0.061</td> <td> 5.694</td> <td> 0.000</td> <td> 0.228</td> <td> 0.467</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 1.9728</td> <td> 0.278</td> <td> 7.086</td> <td> 0.000</td> <td> 1.427</td> <td> 2.518</td>\n", "</tr>\n", "<tr>\n", " <th>x1</th> <td> 0.5280</td> <td> 0.086</td> <td> 6.155</td> <td> 0.000</td> <td> 0.360</td> <td> 0.696</td>\n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 2.5771</td> <td> 0.405</td> <td> 6.357</td> <td> 0.000</td> <td> 1.783</td> <td> 3.372</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.7531</td> <td> 0.063</td> <td> 11.871</td> <td> 0.000</td> <td> 0.629</td> <td> 0.877</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.6825</td> <td> 0.066</td> <td> 10.301</td> <td> 0.000</td> <td> 0.553</td> <td> 0.812</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 520\n", "Model: MarkovRegression Log Likelihood -745.798\n", "Date: Fri, 12 Jun 2020 AIC 1507.595\n", "Time: 07:42:18 BIC 1541.626\n", "Sample: 05-16-2004 HQIC 1520.926\n", " - 04-27-2014 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.7641 0.078 9.761 0.000 0.611 0.918\n", "x1 0.0791 0.030 2.620 0.009 0.020 0.138\n", "sigma2 0.3476 0.061 5.694 0.000 0.228 0.467\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 1.9728 0.278 7.086 0.000 1.427 2.518\n", "x1 0.5280 0.086 6.155 0.000 0.360 0.696\n", "sigma2 2.5771 0.405 6.357 0.000 1.783 3.372\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.7531 0.063 11.871 0.000 0.629 0.877\n", "p[1->0] 0.6825 0.066 10.301 0.000 0.553 0.812\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical differentiation.\n", "\"\"\"" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_areturns.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "res_areturns.smoothed_marginal_probabilities[0].plot(\n", " title='Probability of being in a low-variance regime', figsize=(12,3));" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp_xg47s43/94f52b81-26ab-4a18-938c-c9cf412a497a vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ordinary Least Squares" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "\n", "np.random.seed(9876789)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS estimation\n", "\n", "Artificial data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 100\n", "x = np.linspace(0, 10, 100)\n", "X = np.column_stack((x, x**2))\n", "beta = np.array([1, 0.1, 10])\n", "e = np.random.normal(size=nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model needs an intercept so we add a column of 1s:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = sm.add_constant(X)\n", "y = np.dot(X, beta) + e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 1.000\n", "Model: OLS Adj. R-squared: 1.000\n", "Method: Least Squares F-statistic: 4.020e+06\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 2.83e-239\n", "Time: 07:45:51 Log-Likelihood: -146.51\n", "No. Observations: 100 AIC: 299.0\n", "Df Residuals: 97 BIC: 306.8\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 1.3423 0.313 4.292 0.000 0.722 1.963\n", "x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247\n", "x2 10.0103 0.014 715.745 0.000 9.982 10.038\n", "==============================================================================\n", "Omnibus: 2.042 Durbin-Watson: 2.274\n", "Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875\n", "Skew: 0.234 Prob(JB): 0.392\n", "Kurtosis: 2.519 Cond. No. 144.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "model = sm.OLS(y, X)\n", "results = model.fit()\n", "print(results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quantities of interest can be extracted directly from the fitted model. Type ``dir(results)`` for a full list. Here are some examples: " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [ 1.34233516 -0.04024948 10.01025357]\n", "R2: 0.9999879365025871\n" ] } ], "source": [ "print('Parameters: ', results.params)\n", "print('R2: ', results.rsquared)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS non-linear curve but linear in parameters\n", "\n", "We simulate artificial data with a non-linear relationship between x and y:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "sig = 0.5\n", "x = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n", "beta = [0.5, 0.5, -0.02, 5.]\n", "\n", "y_true = np.dot(X, beta)\n", "y = y_true + sig * np.random.normal(size=nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.933\n", "Model: OLS Adj. R-squared: 0.928\n", "Method: Least Squares F-statistic: 211.8\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 6.30e-27\n", "Time: 07:45:51 Log-Likelihood: -34.438\n", "No. Observations: 50 AIC: 76.88\n", "Df Residuals: 46 BIC: 84.52\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 0.4687 0.026 17.751 0.000 0.416 0.522\n", "x2 0.4836 0.104 4.659 0.000 0.275 0.693\n", "x3 -0.0174 0.002 -7.507 0.000 -0.022 -0.013\n", "const 5.2058 0.171 30.405 0.000 4.861 5.550\n", "==============================================================================\n", "Omnibus: 0.655 Durbin-Watson: 2.896\n", "Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.360\n", "Skew: 0.207 Prob(JB): 0.835\n", "Kurtosis: 3.026 Cond. No. 221.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "res = sm.OLS(y, X).fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract other quantities of interest:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [ 0.46872448 0.48360119 -0.01740479 5.20584496]\n", "Standard errors: [0.02640602 0.10380518 0.00231847 0.17121765]\n", "Predicted values: [ 4.77072516 5.22213464 5.63620761 5.98658823 6.25643234 6.44117491\n", " 6.54928009 6.60085051 6.62432454 6.6518039 6.71377946 6.83412169\n", " 7.02615877 7.29048685 7.61487206 7.97626054 8.34456611 8.68761335\n", " 8.97642389 9.18997755 9.31866582 9.36587056 9.34740836 9.28893189\n", " 9.22171529 9.17751587 9.1833565 9.25708583 9.40444579 9.61812821\n", " 9.87897556 10.15912843 10.42660281 10.65054491 10.8063004 10.87946503\n", " 10.86825119 10.78378163 10.64826203 10.49133265 10.34519853 10.23933827\n", " 10.19566084 10.22490593 10.32487947 10.48081414 10.66779556 10.85485568\n", " 11.01006072 11.10575781]\n" ] } ], "source": [ "print('Parameters: ', res.params)\n", "print('Standard errors: ', res.bse)\n", "print('Predicted values: ', res.predict())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare the true relationship to OLS predictions. Confidence intervals around the predictions are built using the ``wls_prediction_std`` command." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(res)\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(x, y, 'o', label=\"data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "ax.plot(x, res.fittedvalues, 'r--.', label=\"OLS\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "ax.legend(loc='best');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS with dummy variables\n", "\n", "We generate some artificial data. There are 3 groups which will be modelled using dummy variables. Group 0 is the omitted/benchmark category." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "groups = np.zeros(nsample, int)\n", "groups[20:40] = 1\n", "groups[40:] = 2\n", "#dummy = (groups[:,None] == np.unique(groups)).astype(float)\n", "\n", "dummy = sm.categorical(groups, drop=True)\n", "x = np.linspace(0, 20, nsample)\n", "# drop reference category\n", "X = np.column_stack((x, dummy[:,1:]))\n", "X = sm.add_constant(X, prepend=False)\n", "\n", "beta = [1., 3, -3, 10]\n", "y_true = np.dot(X, beta)\n", "e = np.random.normal(size=nsample)\n", "y = y_true + e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the data:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0. 0. 0. 1. ]\n", " [0.40816327 0. 0. 1. ]\n", " [0.81632653 0. 0. 1. ]\n", " [1.2244898 0. 0. 1. ]\n", " [1.63265306 0. 0. 1. ]]\n", "[ 9.28223335 10.50481865 11.84389206 10.38508408 12.37941998]\n", "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", " 1 1 1 2 2 2 2 2 2 2 2 2 2]\n", "[[1. 0. 0.]\n", " [1. 0. 0.]\n", " [1. 0. 0.]\n", " [1. 0. 0.]\n", " [1. 0. 0.]]\n" ] } ], "source": [ "print(X[:5,:])\n", "print(y[:5])\n", "print(groups)\n", "print(dummy[:5,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.978\n", "Model: OLS Adj. R-squared: 0.976\n", "Method: Least Squares F-statistic: 671.7\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 5.69e-38\n", "Time: 07:45:53 Log-Likelihood: -64.643\n", "No. Observations: 50 AIC: 137.3\n", "Df Residuals: 46 BIC: 144.9\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "x1 0.9999 0.060 16.689 0.000 0.879 1.121\n", "x2 2.8909 0.569 5.081 0.000 1.746 4.036\n", "x3 -3.2232 0.927 -3.477 0.001 -5.089 -1.357\n", "const 10.1031 0.310 32.573 0.000 9.479 10.727\n", "==============================================================================\n", "Omnibus: 2.831 Durbin-Watson: 1.998\n", "Prob(Omnibus): 0.243 Jarque-Bera (JB): 1.927\n", "Skew: -0.279 Prob(JB): 0.382\n", "Kurtosis: 2.217 Cond. No. 96.3\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "res2 = sm.OLS(y, X).fit()\n", "print(res2.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare the true relationship to OLS predictions:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(res2)\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(x, y, 'o', label=\"Data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "ax.plot(x, res2.fittedvalues, 'r--.', label=\"Predicted\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "legend = ax.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Joint hypothesis test\n", "\n", "### F test\n", "\n", "We want to test the hypothesis that both coefficients on the dummy variables are equal to zero, that is, $R \\times \\beta = 0$. An F test leads us to strongly reject the null hypothesis of identical constant in the 3 groups:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0 1 0 0]\n", " [0 0 1 0]]\n", "<F test: F=array([[145.49268198]]), p=1.2834419617285915e-20, df_denom=46, df_num=2>\n" ] } ], "source": [ "R = [[0, 1, 0, 0], [0, 0, 1, 0]]\n", "print(np.array(R))\n", "print(res2.f_test(R))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use formula-like syntax to test hypotheses" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<F test: F=array([[145.49268198]]), p=1.2834419617285975e-20, df_denom=46, df_num=2>\n" ] } ], "source": [ "print(res2.f_test(\"x2 = x3 = 0\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Small group effects\n", "\n", "If we generate artificial data with smaller group effects, the T test can no longer reject the Null hypothesis: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta = [1., 0.3, -0.0, 10]\n", "y_true = np.dot(X, beta)\n", "y = y_true + np.random.normal(size=nsample)\n", "\n", "res3 = sm.OLS(y, X).fit()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<F test: F=array([[1.22491119]]), p=0.30318644106317366, df_denom=46, df_num=2>\n" ] } ], "source": [ "print(res3.f_test(R))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<F test: F=array([[1.22491119]]), p=0.30318644106317366, df_denom=46, df_num=2>\n" ] } ], "source": [ "print(res3.f_test(\"x2 = x3 = 0\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multicollinearity\n", "\n", "The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.datasets.longley import load_pandas\n", "y = load_pandas().endog\n", "X = load_pandas().exog\n", "X = sm.add_constant(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fit and summary:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: TOTEMP R-squared: 0.995\n", "Model: OLS Adj. R-squared: 0.992\n", "Method: Least Squares F-statistic: 330.3\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 4.98e-10\n", "Time: 07:45:56 Log-Likelihood: -109.62\n", "No. Observations: 16 AIC: 233.2\n", "Df Residuals: 9 BIC: 238.6\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -3.482e+06 8.9e+05 -3.911 0.004 -5.5e+06 -1.47e+06\n", "GNPDEFL 15.0619 84.915 0.177 0.863 -177.029 207.153\n", "GNP -0.0358 0.033 -1.070 0.313 -0.112 0.040\n", "UNEMP -2.0202 0.488 -4.136 0.003 -3.125 -0.915\n", "ARMED -1.0332 0.214 -4.822 0.001 -1.518 -0.549\n", "POP -0.0511 0.226 -0.226 0.826 -0.563 0.460\n", "YEAR 1829.1515 455.478 4.016 0.003 798.788 2859.515\n", "==============================================================================\n", "Omnibus: 0.749 Durbin-Watson: 2.559\n", "Prob(Omnibus): 0.688 Jarque-Bera (JB): 0.684\n", "Skew: 0.420 Prob(JB): 0.710\n", "Kurtosis: 2.434 Cond. No. 4.86e+09\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 4.86e+09. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n", " \"anyway, n=%i\" % int(n))\n" ] } ], "source": [ "ols_model = sm.OLS(y, X)\n", "ols_results = ols_model.fit()\n", "print(ols_results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Condition number\n", "\n", "One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length: " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "norm_x = X.values\n", "for i, name in enumerate(X):\n", " if name == \"const\":\n", " continue\n", " norm_x[:,i] = X[name]/np.linalg.norm(X[name])\n", "norm_xtx = np.dot(norm_x.T,norm_x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we take the square root of the ratio of the biggest to the smallest eigen values. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "56240.868936151135\n" ] } ], "source": [ "eigs = np.linalg.eigvals(norm_xtx)\n", "condition_number = np.sqrt(eigs.max() / eigs.min())\n", "print(condition_number)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Dropping an observation\n", "\n", "Greene also points out that dropping a single observation can have a dramatic effect on the coefficient estimates: " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Percentage change -13.35%\n", "Percentage change -55778.29%\n", "Percentage change 121935204.58%\n", "Percentage change 1282863.00%\n", "Percentage change 998687.27%\n", "Percentage change -47263797.15%\n", "Percentage change 677415.26%\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "ols_results2 = sm.OLS(y.ix[:14], X.ix[:14]).fit()\n", "print(\"Percentage change %4.2f%%\\n\"*7 % tuple([i for i in (ols_results2.params - ols_results.params)/ols_results.params*100]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at formal statistics for this such as the DFBETAS -- a standardized measure of how much each coefficient changes when that observation is left out." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infl = ols_results.get_influence()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general we may consider DBETAS in absolute value greater than $2/\\sqrt{N}$ to be influential observations" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.5" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2./len(X)**.5" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:309: RuntimeWarning: invalid value encountered in sqrt\n", " return self.results.resid / sigma / np.sqrt(1 - hii)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " dfb_const dfb_GNPDEFL dfb_GNP dfb_UNEMP dfb_ARMED \\\n", "0 -0.016406 -169.822675 1.673981e+06 54490.318088 51447.824036 \n", "1 -0.020608 -187.251727 1.829990e+06 54495.312977 52659.808664 \n", "2 -0.008382 -65.417834 1.587601e+06 52002.330476 49078.352378 \n", "3 0.018093 288.503914 1.155359e+06 56211.331922 60350.723082 \n", "4 1.871260 -171.109595 4.498197e+06 82532.785818 71034.429294 \n", "5 -0.321373 -104.123822 1.398891e+06 52559.760056 47486.527649 \n", "6 0.315945 -169.413317 2.364827e+06 59754.651394 50371.817827 \n", "7 0.015816 -69.343793 1.641243e+06 51849.056936 48628.749338 \n", "8 -0.004019 -86.903523 1.649443e+06 52023.265116 49114.178265 \n", "9 -1.018242 -201.315802 1.371257e+06 56432.027292 53997.742487 \n", "10 0.030947 -78.359439 1.658753e+06 52254.848135 49341.055289 \n", "11 0.005987 -100.926843 1.662425e+06 51744.606934 48968.560299 \n", "12 -0.135883 -32.093127 1.245487e+06 50203.467593 51148.376274 \n", "13 0.032736 -78.513866 1.648417e+06 52509.194459 50212.844641 \n", "14 0.305868 -16.833121 1.829996e+06 60975.868083 58263.878679 \n", "15 -0.538323 102.027105 1.344844e+06 54721.897640 49660.474568 \n", "\n", " dfb_POP dfb_YEAR \n", "0 207954.113589 -31969.158503 \n", "1 25343.938289 -29760.155888 \n", "2 107465.770565 -29593.195253 \n", "3 456190.215132 -36213.129569 \n", "4 -389122.401700 -49905.782854 \n", "5 144354.586054 -28985.057609 \n", "6 -107413.074918 -32984.462465 \n", "7 92843.959345 -29724.975873 \n", "8 83931.635336 -29563.619222 \n", "9 18392.575057 -29203.217108 \n", "10 93617.648517 -29846.022426 \n", "11 95414.217290 -29690.904188 \n", "12 258559.048569 -29296.334617 \n", "13 104434.061226 -30025.564763 \n", "14 275103.677859 -36060.612522 \n", "15 -110176.960671 -28053.834556 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n", " return (self.a < x) & (x < self.b)\n", "/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n", " return (self.a < x) & (x < self.b)\n", "/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n", " cond2 = cond0 & (x <= self.a)\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:323: RuntimeWarning: invalid value encountered in sqrt\n", " dffits_ = self.resid_studentized_internal * np.sqrt(hii / (1 - hii))\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:352: RuntimeWarning: invalid value encountered in sqrt\n", " dffits_ = self.resid_studentized_external * np.sqrt(hii / (1 - hii))\n" ] } ], "source": [ "print(infl.summary_frame().filter(regex=\"dfb\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Statsmodels Principal Component Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Key ideas:* Principal component analysis, world bank data, fertility\n", "\n", "In this notebook, we use principal components analysis (PCA) to analyze the time series of fertility rates in 192 countries, using data obtained from the World Bank. The main goal is to understand how the trends in fertility over time differ from country to country. This is a slightly atypical illustration of PCA because the data are time series. Methods such as functional PCA have been developed for this setting, but since the fertility data are very smooth, there is no real disadvantage to using standard PCA in this case." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from statsmodels.multivariate.pca import PCA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data can be obtained from the [World Bank web site](http://data.worldbank.org/indicator/SP.DYN.TFRT.IN), but here we work with a slightly cleaned-up version of the data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Country Name</th>\n", " <th>Country Code</th>\n", " <th>Indicator Name</th>\n", " <th>Indicator Code</th>\n", " <th>1960</th>\n", " <th>1961</th>\n", " <th>1962</th>\n", " <th>1963</th>\n", " <th>1964</th>\n", " <th>1965</th>\n", " <th>...</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Aruba</td>\n", " <td>ABW</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>4.820</td>\n", " <td>4.655</td>\n", " <td>4.471</td>\n", " <td>4.271</td>\n", " <td>4.059</td>\n", " <td>3.842</td>\n", " <td>...</td>\n", " <td>1.786</td>\n", " <td>1.769</td>\n", " <td>1.754</td>\n", " <td>1.739</td>\n", " <td>1.726</td>\n", " <td>1.713</td>\n", " <td>1.701</td>\n", " <td>1.690</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Andorra</td>\n", " <td>AND</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.240</td>\n", " <td>1.180</td>\n", " <td>1.250</td>\n", " <td>1.190</td>\n", " <td>1.220</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Afghanistan</td>\n", " <td>AFG</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>...</td>\n", " <td>7.136</td>\n", " <td>6.930</td>\n", " <td>6.702</td>\n", " <td>6.456</td>\n", " <td>6.196</td>\n", " <td>5.928</td>\n", " <td>5.659</td>\n", " <td>5.395</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Angola</td>\n", " <td>AGO</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>7.316</td>\n", " <td>7.354</td>\n", " <td>7.385</td>\n", " <td>7.410</td>\n", " <td>7.425</td>\n", " <td>7.430</td>\n", " <td>...</td>\n", " <td>6.704</td>\n", " <td>6.657</td>\n", " <td>6.598</td>\n", " <td>6.523</td>\n", " <td>6.434</td>\n", " <td>6.331</td>\n", " <td>6.218</td>\n", " <td>6.099</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Albania</td>\n", " <td>ALB</td>\n", " <td>Fertility rate, total (births per woman)</td>\n", " <td>SP.DYN.TFRT.IN</td>\n", " <td>6.186</td>\n", " <td>6.076</td>\n", " <td>5.956</td>\n", " <td>5.833</td>\n", " <td>5.711</td>\n", " <td>5.594</td>\n", " <td>...</td>\n", " <td>2.004</td>\n", " <td>1.919</td>\n", " <td>1.849</td>\n", " <td>1.796</td>\n", " <td>1.761</td>\n", " <td>1.744</td>\n", " <td>1.741</td>\n", " <td>1.748</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 58 columns</p>\n", "</div>" ], "text/plain": [ " Country Name Country Code Indicator Name \\\n", "0 Aruba ABW Fertility rate, total (births per woman) \n", "1 Andorra AND Fertility rate, total (births per woman) \n", "2 Afghanistan AFG Fertility rate, total (births per woman) \n", "3 Angola AGO Fertility rate, total (births per woman) \n", "4 Albania ALB Fertility rate, total (births per woman) \n", "\n", " Indicator Code 1960 1961 1962 1963 1964 1965 ... 2004 \\\n", "0 SP.DYN.TFRT.IN 4.820 4.655 4.471 4.271 4.059 3.842 ... 1.786 \n", "1 SP.DYN.TFRT.IN NaN NaN NaN NaN NaN NaN ... NaN \n", "2 SP.DYN.TFRT.IN 7.671 7.671 7.671 7.671 7.671 7.671 ... 7.136 \n", "3 SP.DYN.TFRT.IN 7.316 7.354 7.385 7.410 7.425 7.430 ... 6.704 \n", "4 SP.DYN.TFRT.IN 6.186 6.076 5.956 5.833 5.711 5.594 ... 2.004 \n", "\n", " 2005 2006 2007 2008 2009 2010 2011 2012 2013 \n", "0 1.769 1.754 1.739 1.726 1.713 1.701 1.690 NaN NaN \n", "1 NaN 1.240 1.180 1.250 1.190 1.220 NaN NaN NaN \n", "2 6.930 6.702 6.456 6.196 5.928 5.659 5.395 NaN NaN \n", "3 6.657 6.598 6.523 6.434 6.331 6.218 6.099 NaN NaN \n", "4 1.919 1.849 1.796 1.761 1.744 1.741 1.748 NaN NaN \n", "\n", "[5 rows x 58 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = sm.datasets.fertility.load_pandas().data\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we construct a DataFrame that contains only the numerical fertility rate data and set the index to the country names. We also drop all the countries with any missing data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>1960</th>\n", " <th>1961</th>\n", " <th>1962</th>\n", " <th>1963</th>\n", " <th>1964</th>\n", " <th>1965</th>\n", " <th>1966</th>\n", " <th>1967</th>\n", " <th>1968</th>\n", " <th>1969</th>\n", " <th>...</th>\n", " <th>2002</th>\n", " <th>2003</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " </tr>\n", " <tr>\n", " <th>Country Name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Aruba</th>\n", " <td>4.820</td>\n", " <td>4.655</td>\n", " <td>4.471</td>\n", " <td>4.271</td>\n", " <td>4.059</td>\n", " <td>3.842</td>\n", " <td>3.625</td>\n", " <td>3.417</td>\n", " <td>3.226</td>\n", " <td>3.054</td>\n", " <td>...</td>\n", " <td>1.825</td>\n", " <td>1.805</td>\n", " <td>1.786</td>\n", " <td>1.769</td>\n", " <td>1.754</td>\n", " <td>1.739</td>\n", " <td>1.726</td>\n", " <td>1.713</td>\n", " <td>1.701</td>\n", " <td>1.690</td>\n", " </tr>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>7.671</td>\n", " <td>...</td>\n", " <td>7.484</td>\n", " <td>7.321</td>\n", " <td>7.136</td>\n", " <td>6.930</td>\n", " <td>6.702</td>\n", " <td>6.456</td>\n", " <td>6.196</td>\n", " <td>5.928</td>\n", " <td>5.659</td>\n", " <td>5.395</td>\n", " </tr>\n", " <tr>\n", " <th>Angola</th>\n", " <td>7.316</td>\n", " <td>7.354</td>\n", " <td>7.385</td>\n", " <td>7.410</td>\n", " <td>7.425</td>\n", " <td>7.430</td>\n", " <td>7.422</td>\n", " <td>7.403</td>\n", " <td>7.375</td>\n", " <td>7.339</td>\n", " <td>...</td>\n", " <td>6.778</td>\n", " <td>6.743</td>\n", " <td>6.704</td>\n", " <td>6.657</td>\n", " <td>6.598</td>\n", " <td>6.523</td>\n", " <td>6.434</td>\n", " <td>6.331</td>\n", " <td>6.218</td>\n", " <td>6.099</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>6.186</td>\n", " <td>6.076</td>\n", " <td>5.956</td>\n", " <td>5.833</td>\n", " <td>5.711</td>\n", " <td>5.594</td>\n", " <td>5.483</td>\n", " <td>5.376</td>\n", " <td>5.268</td>\n", " <td>5.160</td>\n", " <td>...</td>\n", " <td>2.195</td>\n", " <td>2.097</td>\n", " <td>2.004</td>\n", " <td>1.919</td>\n", " <td>1.849</td>\n", " <td>1.796</td>\n", " <td>1.761</td>\n", " <td>1.744</td>\n", " <td>1.741</td>\n", " <td>1.748</td>\n", " </tr>\n", " <tr>\n", " <th>United Arab Emirates</th>\n", " <td>6.928</td>\n", " <td>6.910</td>\n", " <td>6.893</td>\n", " <td>6.877</td>\n", " <td>6.861</td>\n", " <td>6.841</td>\n", " <td>6.816</td>\n", " <td>6.783</td>\n", " <td>6.738</td>\n", " <td>6.679</td>\n", " <td>...</td>\n", " <td>2.428</td>\n", " <td>2.329</td>\n", " <td>2.236</td>\n", " <td>2.149</td>\n", " <td>2.071</td>\n", " <td>2.004</td>\n", " <td>1.948</td>\n", " <td>1.903</td>\n", " <td>1.868</td>\n", " <td>1.841</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows \u00d7 52 columns</p>\n", "</div>" ], "text/plain": [ " 1960 1961 1962 1963 1964 1965 1966 1967 \\\n", "Country Name \n", "Aruba 4.820 4.655 4.471 4.271 4.059 3.842 3.625 3.417 \n", "Afghanistan 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 \n", "Angola 7.316 7.354 7.385 7.410 7.425 7.430 7.422 7.403 \n", "Albania 6.186 6.076 5.956 5.833 5.711 5.594 5.483 5.376 \n", "United Arab Emirates 6.928 6.910 6.893 6.877 6.861 6.841 6.816 6.783 \n", "\n", " 1968 1969 ... 2002 2003 2004 2005 2006 \\\n", "Country Name ... \n", "Aruba 3.226 3.054 ... 1.825 1.805 1.786 1.769 1.754 \n", "Afghanistan 7.671 7.671 ... 7.484 7.321 7.136 6.930 6.702 \n", "Angola 7.375 7.339 ... 6.778 6.743 6.704 6.657 6.598 \n", "Albania 5.268 5.160 ... 2.195 2.097 2.004 1.919 1.849 \n", "United Arab Emirates 6.738 6.679 ... 2.428 2.329 2.236 2.149 2.071 \n", "\n", " 2007 2008 2009 2010 2011 \n", "Country Name \n", "Aruba 1.739 1.726 1.713 1.701 1.690 \n", "Afghanistan 6.456 6.196 5.928 5.659 5.395 \n", "Angola 6.523 6.434 6.331 6.218 6.099 \n", "Albania 1.796 1.761 1.744 1.741 1.748 \n", "United Arab Emirates 2.004 1.948 1.903 1.868 1.841 \n", "\n", "[5 rows x 52 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns = list(map(str, range(1960, 2012)))\n", "data.set_index('Country Name', inplace=True)\n", "dta = data[columns]\n", "dta = dta.dropna()\n", "dta.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways to use PCA to analyze a rectangular matrix: we can treat the rows as the \"objects\" and the columns as the \"variables\", or vice-versa. Here we will treat the fertility measures as \"variables\" used to measure the countries as \"objects\". Thus the goal will be to reduce the yearly fertility rate values to a small number of fertility rate \"profiles\" or \"basis functions\" that capture most of the variation over time in the different countries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean trend is removed in PCA, but its worthwhile taking a look at it. It shows that fertility has dropped steadily over the time period covered in this dataset. Note that the mean is calculated using a country as the unit of analysis, ignoring population size. This is also true for the PC analysis conducted below. A more sophisticated analysis might weight the countries, say by population in 1980." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0, 51)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = dta.mean().plot(grid=False)\n", "ax.set_xlabel(\"Year\", size=17)\n", "ax.set_ylabel(\"Fertility rate\", size=17);\n", "ax.set_xlim(0, 51)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we perform the PCA:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pca_model = PCA(dta.T, standardize=False, demean=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the eigenvalues, we see that the first PC dominates, with perhaps a small amount of meaningful variation captured in the second and third PC's." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = pca_model.plot_scree(log_scale=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will plot the PC factors. The dominant factor is monotonically increasing. Countries with a positive score on the first factor will increase faster (or decrease slower) compared to the mean shown above. Countries with a negative score on the first factor will decrease faster than the mean. The second factor is U-shaped with a positive peak at around 1985. Countries with a large positive score on the second factor will have lower than average fertilities at the beginning and end of the data range, but higher than average fertility in the middle of the range." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 4))\n", "lines = ax.plot(pca_model.factors.ix[:,:3], lw=4, alpha=.6)\n", "ax.set_xticklabels(dta.columns.values[::10])\n", "ax.set_xlim(0, 51)\n", "ax.set_xlabel(\"Year\", size=17)\n", "fig.subplots_adjust(.1, .1, .85, .9)\n", "legend = fig.legend(lines, ['PC 1', 'PC 2', 'PC 3'], loc='center right')\n", "legend.draw_frame(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To better understand what is going on, we will plot the fertility trajectories for sets of countries with similar PC scores. The following convenience function produces such a plot." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "idx = pca_model.loadings.ix[:,0].argsort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we plot the five countries with the greatest scores on PC 1. These countries have a higher rate of fertility increase than the global mean (which is decreasing)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def make_plot(labels):\n", " fig, ax = plt.subplots(figsize=(9,5))\n", " ax = dta.ix[labels].T.plot(legend=False, grid=False, ax=ax)\n", " dta.mean().plot(ax=ax, grid=False, label='Mean')\n", " ax.set_xlim(0, 51);\n", " fig.subplots_adjust(.1, .1, .75, .9)\n", " ax.set_xlabel(\"Year\", size=17)\n", " ax.set_ylabel(\"Fertility\", size=17);\n", " legend = ax.legend(*ax.get_legend_handles_labels(), loc='center left', bbox_to_anchor=(1, .5))\n", " legend.draw_frame(False)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "labels = dta.index[idx[-5:]]\n", "make_plot(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are the five countries with the greatest scores on factor 2. These are countries that reached peak fertility around 1980, later than much of the rest of the world, followed by a rapid decrease in fertility." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"Entry point for launching an IPython kernel.\n", "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "idx = pca_model.loadings.ix[:,1].argsort()\n", "make_plot(dta.index[idx[-5:]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we have the countries with the most negative scores on PC 2. These are the countries where the fertility rate declined much faster than the global mean during the 1960's and 1970's, then flattened out." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "make_plot(dta.index[idx[:5]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at a scatterplot of the first two principal component scores. We see that the variation among countries is fairly continuous, except perhaps that the two countries with highest scores for PC 2 are somewhat separated from the other points. These countries, Oman and Yemen, are unique in having a sharp spike in fertility around 1980. No other country has such a spike. In contrast, the countries with high scores on PC 1 (that have continuously increasing fertility), are part of a continuum of variation." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"\n" ] }, { "data": { "text/plain": [ "array(['Oman', 'Yemen, Rep.'], dtype=object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "pca_model.loadings.plot.scatter(x='comp_00', y='comp_01', ax=ax)\n", "ax.set_xlabel(\"PC 1\", size=17)\n", "ax.set_ylabel(\"PC 2\", size=17)\n", "dta.index[pca_model.loadings.ix[:, 1] > .2].values" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp7a8kgck5/3d657aeb-5c2e-4de7-bcbd-3809e4588fae vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Box Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following illustrates some options for the boxplot in statsmodels. These include `violin_plot` and `bean_plot`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bean Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example is taken from the docstring of `beanplot`.\n", "\n", "We use the American National Election Survey 1996 dataset, which has Party\n", "Identification of respondents as independent variable and (among other\n", "data) age as dependent variable." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = sm.datasets.anes96.load_pandas()\n", "party_ID = np.arange(7)\n", "labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n", " \"Independent-Independent\", \"Independent-Republican\",\n", " \"Weak Republican\", \"Strong Republican\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Group age by party ID, and create a violin plot with it:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Age')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # make plot larger in notebook\n", "age = [data.exog['age'][data.endog == id] for id in party_ID]\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n", " 'label_fontsize':'small',\n", " 'label_rotation':30}\n", "sm.graphics.beanplot(age, ax=ax, labels=labels,\n", " plot_opts=plot_opts)\n", "ax.set_xlabel(\"Party identification of respondent.\")\n", "ax.set_ylabel(\"Age\")\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def beanplot(data, plot_opts={}, jitter=False):\n", " \"\"\"helper function to try out different plot options\n", " \"\"\"\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " plot_opts_ = {'cutoff_val':5, 'cutoff_type':'abs',\n", " 'label_fontsize':'small',\n", " 'label_rotation':30}\n", " plot_opts_.update(plot_opts)\n", " sm.graphics.beanplot(data, ax=ax, labels=labels,\n", " jitter=jitter, plot_opts=plot_opts_)\n", " ax.set_xlabel(\"Party identification of respondent.\")\n", " ax.set_ylabel(\"Age\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = beanplot(age, jitter=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = beanplot(age, plot_opts={'violin_width': 0.5, 'violin_fc':'#66c2a5'})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = beanplot(age, plot_opts={'violin_fc':'#66c2a5'})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = beanplot(age, plot_opts={'bean_size': 0.2, 'violin_width': 0.75, 'violin_fc':'#66c2a5'})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = beanplot(age, jitter=True, plot_opts={'violin_fc':'#66c2a5'})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = beanplot(age, jitter=True, plot_opts={'violin_width': 0.5, 'violin_fc':'#66c2a5'})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced Box Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based of example script `example_enhanced_boxplots.py` (by Ralf Gommers)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm\n", "\n", "\n", "# Necessary to make horizontal axis labels fit\n", "plt.rcParams['figure.subplot.bottom'] = 0.23\n", "\n", "data = sm.datasets.anes96.load_pandas()\n", "party_ID = np.arange(7)\n", "labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n", " \"Independent-Independent\", \"Independent-Republican\",\n", " \"Weak Republican\", \"Strong Republican\"]\n", "\n", "# Group age by party ID.\n", "age = [data.exog['age'][data.endog == id] for id in party_ID]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a violin plot.\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "\n", "sm.graphics.violinplot(age, ax=ax, labels=labels,\n", " plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n", " 'label_fontsize':'small',\n", " 'label_rotation':30})\n", "\n", "ax.set_xlabel(\"Party identification of respondent.\")\n", "ax.set_ylabel(\"Age\")\n", "ax.set_title(\"US national election '96 - Age & Party Identification\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a bean plot.\n", "fig2 = plt.figure()\n", "ax = fig2.add_subplot(111)\n", "\n", "sm.graphics.beanplot(age, ax=ax, labels=labels,\n", " plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n", " 'label_fontsize':'small',\n", " 'label_rotation':30})\n", "\n", "ax.set_xlabel(\"Party identification of respondent.\")\n", "ax.set_ylabel(\"Age\")\n", "ax.set_title(\"US national election '96 - Age & Party Identification\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create a jitter plot.\n", "fig3 = plt.figure()\n", "ax = fig3.add_subplot(111)\n", "\n", "plot_opts={'cutoff_val':5, 'cutoff_type':'abs', 'label_fontsize':'small',\n", " 'label_rotation':30, 'violin_fc':(0.8, 0.8, 0.8),\n", " 'jitter_marker':'.', 'jitter_marker_size':3, 'bean_color':'#FF6F00',\n", " 'bean_mean_color':'#009D91'}\n", "sm.graphics.beanplot(age, ax=ax, labels=labels, jitter=True,\n", " plot_opts=plot_opts)\n", "\n", "ax.set_xlabel(\"Party identification of respondent.\")\n", "ax.set_ylabel(\"Age\")\n", "ax.set_title(\"US national election '96 - Age & Party Identification\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create an asymmetrical jitter plot.\n", "ix = data.exog['income'] < 16 # incomes < $30k\n", "age = data.exog['age'][ix]\n", "endog = data.endog[ix]\n", "age_lower_income = [age[endog == id] for id in party_ID]\n", "\n", "ix = data.exog['income'] >= 20 # incomes > $50k\n", "age = data.exog['age'][ix]\n", "endog = data.endog[ix]\n", "age_higher_income = [age[endog == id] for id in party_ID]\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "\n", "plot_opts['violin_fc'] = (0.5, 0.5, 0.5)\n", "plot_opts['bean_show_mean'] = False\n", "plot_opts['bean_show_median'] = False\n", "plot_opts['bean_legend_text'] = 'Income < \\$30k'\n", "plot_opts['cutoff_val'] = 10\n", "sm.graphics.beanplot(age_lower_income, ax=ax, labels=labels, side='left',\n", " jitter=True, plot_opts=plot_opts)\n", "plot_opts['violin_fc'] = (0.7, 0.7, 0.7)\n", "plot_opts['bean_color'] = '#009D91'\n", "plot_opts['bean_legend_text'] = 'Income > \\$50k'\n", "sm.graphics.beanplot(age_higher_income, ax=ax, labels=labels, side='right',\n", " jitter=True, plot_opts=plot_opts)\n", "\n", "ax.set_xlabel(\"Party identification of respondent.\")\n", "ax.set_ylabel(\"Age\")\n", "ax.set_title(\"US national election '96 - Age & Party Identification\")\n", "\n", "\n", "# Show all plots.\n", "#plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp6dqs6vij/50a214a7-a1ee-467b-98cc-743c06107078 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction (out of sample)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Artificial data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "sig = 0.25\n", "x1 = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x1, np.sin(x1), (x1-5)**2))\n", "X = sm.add_constant(X)\n", "beta = [5., 0.5, 0.5, -0.02]\n", "y_true = np.dot(X, beta)\n", "y = y_true + sig * np.random.normal(size=nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.987\n", "Model: OLS Adj. R-squared: 0.987\n", "Method: Least Squares F-statistic: 1199.\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 1.16e-43\n", "Time: 07:43:46 Log-Likelihood: 8.2729\n", "No. Observations: 50 AIC: -8.546\n", "Df Residuals: 46 BIC: -0.8976\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 5.0303 0.073 69.028 0.000 4.884 5.177\n", "x1 0.5037 0.011 44.815 0.000 0.481 0.526\n", "x2 0.5070 0.044 11.475 0.000 0.418 0.596\n", "x3 -0.0211 0.001 -21.409 0.000 -0.023 -0.019\n", "==============================================================================\n", "Omnibus: 1.524 Durbin-Watson: 2.635\n", "Prob(Omnibus): 0.467 Jarque-Bera (JB): 1.153\n", "Skew: -0.116 Prob(JB): 0.562\n", "Kurtosis: 2.293 Cond. No. 221.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "olsmod = sm.OLS(y, X)\n", "olsres = olsmod.fit()\n", "print(olsres.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In-sample prediction" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4.50212995 4.99165165 5.4410721 5.82276099 6.11905964 6.32518228\n", " 6.45000234 6.51459454 6.54877243 6.58619005 6.65881271 6.79166515\n", " 6.99872007 7.28060242 7.62448678 8.00620447 8.39421446 8.75478543\n", " 9.05753774 9.28043474 9.41340328 9.45998877 9.43677262 9.37064799\n", " 9.2944013 9.24132554 9.2397503 9.30838733 9.45325622 9.6666954\n", " 9.92862095 10.2098263 10.47678078 10.69713864 10.84505336 10.90542413\n", " 10.87637693 10.76957235 10.60828985 10.423603 10.24927414 10.11620736\n", " 10.04737107 10.05402427 10.13386683 10.2714173 10.44055656 10.60881904\n", " 10.74272676 10.81328967]\n" ] } ], "source": [ "ypred = olsres.predict(X)\n", "print(ypred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a new sample of explanatory variables Xnew, predict and plot" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10.78548971 10.62339258 10.34688037 10.00126184 9.64617928 9.34100596\n", " 9.13030951 9.0329402 9.03741575 9.10473255]\n" ] } ], "source": [ "x1n = np.linspace(20.5,25, 10)\n", "Xnew = np.column_stack((x1n, np.sin(x1n), (x1n-5)**2))\n", "Xnew = sm.add_constant(Xnew)\n", "ynewpred = olsres.predict(Xnew) # predict out of sample\n", "print(ynewpred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot comparison" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x1, y, 'o', label=\"Data\")\n", "ax.plot(x1, y_true, 'b-', label=\"True\")\n", "ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label=\"OLS prediction\")\n", "ax.legend(loc=\"best\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting with Formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using formulas can make both estimation and prediction a lot easier" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.formula.api import ols\n", "\n", "data = {\"x1\" : x1, \"y\" : y}\n", "\n", "res = ols(\"y ~ x1 + np.sin(x1) + I((x1-5)**2)\", data=data).fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the `I` to indicate use of the Identity transform. Ie., we don't want any expansion magic from using `**2`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Intercept 5.030267\n", "x1 0.503670\n", "np.sin(x1) 0.506986\n", "I((x1 - 5) ** 2) -0.021125\n", "dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we only have to pass the single variable and we get the transformed right-hand side variables automatically" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 10.785490\n", "1 10.623393\n", "2 10.346880\n", "3 10.001262\n", "4 9.646179\n", "5 9.341006\n", "6 9.130310\n", "7 9.032940\n", "8 9.037416\n", "9 9.104733\n", "dtype: float64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.predict(exog=dict(x1=x1n))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmpo1fb724p/22b7b85d-58f3-4ff0-bb66-9f554202ef72 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quantile regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in \n", "\n", "* Koenker, Roger and Kevin F. Hallock. \"Quantile Regressioin\". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143\u2013156\n", "\n", "We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). \n", "\n", "## Setup\n", "\n", "We first need to load some modules and to retrieve the data. Conveniently, the Engel dataset is shipped with ``statsmodels``." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>income</th>\n", " <th>foodexp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>420.157651</td>\n", " <td>255.839425</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>541.411707</td>\n", " <td>310.958667</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>901.157457</td>\n", " <td>485.680014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>639.080229</td>\n", " <td>402.997356</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>750.875606</td>\n", " <td>495.560775</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " income foodexp\n", "0 420.157651 255.839425\n", "1 541.411707 310.958667\n", "2 901.157457 485.680014\n", "3 639.080229 402.997356\n", "4 750.875606 495.560775" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import patsy\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "import matplotlib.pyplot as plt\n", "from statsmodels.regression.quantile_regression import QuantReg\n", "\n", "data = sm.datasets.engel.load_pandas().data\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Least Absolute Deviation\n", "\n", "The LAD model is a special case of quantile regression where q=0.5" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " QuantReg Regression Results \n", "==============================================================================\n", "Dep. Variable: foodexp Pseudo R-squared: 0.6206\n", "Model: QuantReg Bandwidth: 64.51\n", "Method: Least Squares Sparsity: 209.3\n", "Date: Fri, 12 Jun 2020 No. Observations: 235\n", "Time: 07:41:53 Df Residuals: 233\n", " Df Model: 1\n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 81.4823 14.634 5.568 0.000 52.649 110.315\n", "income 0.5602 0.013 42.516 0.000 0.534 0.586\n", "==============================================================================\n", "\n", "The condition number is large, 2.38e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] } ], "source": [ "mod = smf.quantreg('foodexp ~ income', data)\n", "res = mod.fit(q=.5)\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the results\n", "\n", "We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare data for plotting\n", "\n", "For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n", "/usr/lib/python3/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " after removing the cwd from sys.path.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " q a b lb ub\n", "0 0.05 124.880099 0.343361 0.268632 0.418090\n", "1 0.15 111.693660 0.423708 0.382780 0.464636\n", "2 0.25 95.483539 0.474103 0.439900 0.508306\n", "3 0.35 105.841294 0.488901 0.457759 0.520043\n", "4 0.45 81.083647 0.552428 0.525021 0.579835\n", "5 0.55 89.661370 0.565601 0.540955 0.590247\n", "6 0.65 74.033434 0.604576 0.582169 0.626982\n", "7 0.75 62.396584 0.644014 0.622411 0.665617\n", "8 0.85 52.272216 0.677603 0.657383 0.697823\n", "9 0.95 64.103964 0.709069 0.687831 0.730306\n", "{'a': 147.47538852370633, 'b': 0.48517842367692343, 'lb': 0.45687381301842317, 'ub': 0.5134830343354236}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " # Remove the CWD from sys.path while we load stuff.\n" ] } ], "source": [ "quantiles = np.arange(.05, .96, .1)\n", "def fit_model(q):\n", " res = mod.fit(q=q)\n", " return [q, res.params['Intercept'], res.params['income']] + \\\n", " res.conf_int().ix['income'].tolist()\n", " \n", "models = [fit_model(x) for x in quantiles]\n", "models = pd.DataFrame(models, columns=['q', 'a', 'b','lb','ub'])\n", "\n", "ols = smf.ols('foodexp ~ income', data).fit()\n", "ols_ci = ols.conf_int().ix['income'].tolist()\n", "ols = dict(a = ols.params['Intercept'],\n", " b = ols.params['income'],\n", " lb = ols_ci[0],\n", " ub = ols_ci[1])\n", "\n", "print(models)\n", "print(ols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First plot\n", "\n", "This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:\n", "\n", "1. Food expenditure increases with income\n", "2. The *dispersion* of food expenditure increases with income\n", "3. The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.arange(data.income.min(), data.income.max(), 50)\n", "get_y = lambda a, b: a + b * x\n", "\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "for i in range(models.shape[0]):\n", " y = get_y(models.a[i], models.b[i])\n", " ax.plot(x, y, linestyle='dotted', color='grey')\n", " \n", "y = get_y(ols['a'], ols['b'])\n", "\n", "ax.plot(x, y, color='red', label='OLS')\n", "ax.scatter(data.income, data.foodexp, alpha=.2)\n", "ax.set_xlim((240, 3000))\n", "ax.set_ylim((240, 2000))\n", "legend = ax.legend()\n", "ax.set_xlabel('Income', fontsize=16)\n", "ax.set_ylabel('Food expenditure', fontsize=16);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Second plot\n", "\n", "The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confindence interval.\n", "\n", "In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0oFq1arZ9FexJEFFRUaxevZpvvvnGNhz2rbfeYuDAgXbPaXjIYrHg4eHBf/7zH1asWEH+/Pm5evUqmTJlSlC9yUGThFIqxYqKisLFxYUMGTKwefNm2rVrR/ny5SlTpgxlypSxq85r167h5eXF7NmzuXz5MiVLlmTy5Mn07NmTXLly2R3rrFmz2LRpEz4+Pjg5OTF79mxKly5tez41JgjQJKGUSqF69uxJcHAwq1at4vnnnycwMDBBH7T79u3jm2++4ccffyQyMpKmTZsyZ84cmjVrZldLJCwsjDVr1tChQwdcXFywWCzExMQQFhZGlixZaNWq1bMrSQV0dFMicXNzA6yzvVu0aJHM0SiVOhw7dsz2deXKlalatSoSu9anPQkiIiKCpUuXUrt2bWrXro2Pjw99+/bFz8+PTZs20aJFC7sm0wFs3bqVbt26sW3bNgDeffdd1q9fT5YsWeIdZ0qmSUIplSKsXLmSKlWqsHPnTgAGDx7M6NGj7Vq25tKlS4wcOZKiRYvSvXt3goOD+eabbwgKCuLrr7+mbNmy8a7z3r17vPTSS7ZNh5o1a8aOHTvsnpyXWmiSSAL37t2jbdu2VKhQgX79+j22ppNS6ZWIsG3bNn7//XcAmjdvzrRp06hWrZrd9e3atYtOnTpRvHhxxo0bR61atdi8eTMnTpxg4MCBthvdcXXv3j12794NWBcDLFKkCDlz5gSsqyq8/PLLaX/ttbjMuEvJj7jMuK5fv74sXLhQREQiIyOlfv36smTJEhERuX//vtSvX1+WL18uIiJ3796V+vXry08//SQiIjdu3JD69evL2rVrRUTkypUr/zKH8S9Zs2YVEZHt27dLpkyZ5OzZsxIdHS2NGzeWFStWxKmOtEpnXCsRkaioKClRooS0aNEiQfWEhYXJ/PnzxcPDQwDJmTOnDBkyRM6ePZvgGLt06SLPPfdcgmdXp0TojOuUo1atWpQsWRJnZ2e6dOliW7teqfTmt99+o3PnzrYVTtevX8+KFSvsquvChQuMGDGCwoUL88477xAdHc2cOXMICgpi0qRJlCxZMt51nj59mi5dunDt2jUAPv30UzZt2pSg2dWpXboY3ZQcS4U/SpcGV+mZxWLBYrGQIUMGbt68yf79+7l48SIlSpSgQoUK8apLYvdt+Oabb1i71rpXWZs2bXjvvfeoX7++XX9b0dHR3L9/nxw5ciAibNmyhWPHjpE/f/4EreaaVmhLIgns27ePc+fOYbFY8Pb25qWXXkrukJRKEjdv3qRy5crMnTsXgHbt2uHn50eJEiXiVU9kZCTz58+ncuXKNGrUiJ07dzJixAjOnTvHTz/9RIMGDexOEJUqVWLEiBEAlC1blsuXL9O4ceN415VWaZJIAnXq1OGjjz6iUqVKlChRgrZt2yZ3SEolmqioKI4fPw5A7ty5qVWrFkWKFAGsi+25uLjEua7IyEjmzZtH2bJl6d27NxkzZmThwoUEBQUxfvx4ihYtGu/4AgICbEkrQ4YM9OvXj5YtW9qeT62T3hJNXG5cpOSHLhWe+ujvJ23r1auX5MmTR0JDQ+2uIyIiQry8vKRYsWICSK1ateTnn3+2e8lvi8ViO/ezzz6TTJkyyfXr1+2OLy1Ab1wrpZJCeHg4s2bN4ubNmwAMHDiQBQsW2DWpLDIykrlz51KmTBk8PT3Jnz8/P//8M3v37qVZs2Z2dSmdPn2aWrVqsWfPHgAGDRpEQEAAefPmjXdd6ZEmCaVUggQEBDBgwADbKKVq1arRsmXLeH2g/z05PP/882zcuNHu5HDnzh38/PwAKFSoEC4uLoSGhgLWLrCCBQvGq770LF2MblJKOdb06dO5ffs2Y8aMoUKFChw9etSupa8jIyNZtGgR48aN4+LFi9SuXZvZs2fTtGlTu0cBigj169cne/bs7Nq1i6xZs9omxKn405aEUipOIiIibF8fP36cw4cP29ZVqly5crxbDl5eXri7u9O3b18KFCjAxo0b2bNnD6+99lq8E8T+/fvp27cvFosFYwyTJ0+2LZ+hEkaThFLqmbZs2UKhQoU4c+YMADNnzsTHxyfeH+aOTA6RkZFERkYCcO7cOVatWoW/vz8ATZo0oUqVKvGKTT2ZJgml1BMFBgZy9uxZAKpWrUrDhg1tz8VnGCtYP9DnzJnzWHLYtGmT3S2Hy5cvU7x4cRYtWgRY519cvHjR7j0m1NNpkkhEQUFBtG7dGnd3d0qVKsUHH3xAZGTkU5cPX79+PdWqVaNq1apUqFCBOXPmJEPUSlnnOtSsWZPhw4cDkD9/fn788Ufc3d3jVc+jyaFfv34ULFjQlhzie9/h9OnTbNiwAYACBQrwxhtv2GZsZ8iQgcyZM8crNhVHcRknm5IfKXWehMVikZo1a8qCBQtERCQ6Olp69eolQ4cOle3bt8vrr7/+2PGRkZFSoEABCQwMFBGR8PBw8fPzS/K4k0JK+P2of/rjjz/ko48+spU3bNgg58+ft6uuiIgImT17thQpUkQAefHFF2XTpk12z3MQEWnWrJkUK1ZMYmJi7K5D/QWdJ5G8fv31V1xdXenZsydg3YN36tSpLFiwgLCwsH8cHxISQnR0NLlz5wassz7tWfNeqfh4uK4SwN69e5k7dy5XrlwBrEt3FytWLF71RURE2Lbt7NevH4UKFWLz5s3s3r073i2HoKAg+vfvz+3btwH45ptv2LdvH05O+rGVlNL+ENhBg+DwYcfW6eEB06b96yG+vr5Ur179se9lz56dokWL2m6uPeq5556jVatWFCtWjEaNGtGiRQu6dOmifxAq0Zw/f57XX3+dL7/8klatWuHp6ck777xD1qxZ411XREQECxcuZPz48QQGBlKnTh3mzZtHkyZN7B7KevfuXRYvXszrr79OixYtKFWqlF31qITRT6AUZN68eWzbto1atWoxadIkevXqldwhqTQmNDTUtq5S4cKFKVmypG0ZbFdX13gniIctB3d3d/r370/hwoXZvHkzv//+O6+++mq8EoSIMGTIEIYNGwZApUqVuHz5sm7/m8zSfkviGf/xJ5YKFSqwcuXKx7537949Ll68SOnSpdmyZcsTz6tcuTKVK1eme/fulChRwjZ6QylHaNOmDYGBgZw8eZIMGTKwbt06u+qJiIhgwYIFjB8/nqCgIOrUqcP8+fNp3LhxvFsO9+/fJ2vWrBhjCA8Px8nJCRHBGGPbBU4lH21JJJJGjRoRFhbGd999B0BMTAxDhgyhR48eT1zTJjQ09LF9Kw4fPhzv/mCl/u7ixYsMHz6c8PBwAEaPHs2iRYvs7saMiIhg1qxZlC5dmgEDBlC0aFG2bNnC77//blfX0vr16ylYsKBt/sW3337LN998o3uupCRxubvtqAfwGnAK8Ac+esoxHYETgC/ww7PqTKmjm0RELl68KC1atJDSpUtLyZIlZeDAgRIeHi7bt28XV1dXKVSokO3xf//3f9KsWTMpU6aMVK1aVerWrSv79+9P7h8hUaSU309a9nAU0bZt28TFxUW2b9+eoPrCw8Nl5syZUrhwYQGkbt26smXLFrtGK926dUsuXrwoIiJXr16Vt99+2+5RVMp+xHF0U1ImCGfgLFASyAgcASr87Rh34E8gV2w537PqTclJQj2Z/n4ST3h4uLz22msybtw4EbEmi7juy/4kDx48kBkzZjyWHH755Re7h7JGRUVJsWLFpHXr1nbHpBwjrkkiKbubagH+IhIgIpHAcqD1347pA8wQkTsAInI9CeNTKlWKiYnh6NGjgHXodP78+W19+cYYu7bcffDgAV9//TWlSpXi3XffpWjRovzyyy/s2rUr3vcdQkJCWLJkCWCd9DZp0iTGjh0b75hU8kjKG9eFgMBHykFA7b8dUwbAGPM71pbH5yKyKWnCUyp1+uijj5g5cyYXL14kd+7cCRrsEBYWxpw5c5g4cSJXr17l5ZdfZsmSJbzyyit23ydYtGgR77//PlWrVqVKlSq88cYbdsenkl5Ku3GdAWuXUwOgCzDXGPOP4Q3GGE9jzAFjzIEbN248sSJra0qlNPp7SbiQkBAmTpzI+fPnAXjnnXdYtGhRgkYChYaG8r///Y8SJUowePBgKlSowG+//caOHTto2LBhvBfemzZtGlu3bgWgd+/e/PHHH7rgXiqVlEniElDkkXLh2O89KghYKyJRInIOOI01aTxGRLxEpIaI1HjS7lKurq7cunVLP5BSGBHh1q1btnH5Kn4evp+Dg4P57LPPbMNXy5UrR4cOHXB2do53nSEhIUyYMIESJUowfPhwqlatys6dO9m2bRv169e3K05jDF9//TVr164FIHPmzNSqVcuuulTyS8rupv2AuzGmBNbk0Bno+rdj1mBtQSw0xuTB2v0UEN8LFS5cmKCgIJ7WylDJx9XVlcKFCyd3GKnOkCFDuH37NgsXLqRw4cKcPXuWIkWKPPvEpwgODubbb79lypQp3L59m2bNmjFy5Ejq1KljV32rVq1i9uzZbNy4ERcXF/bt20eePHnsjk+lHEmWJEQk2hgzENiM9X7DAhHxNcaMxXqXfW3sc68aY04AMcAwEbkV32u5uLhQokQJR4avVJLy8/Njy5YtDBgwgAwZMuDm5kZkZKRtkpm9CeLu3bt8/fXXTJ06lbt379KiRQtGjhxp13/60dHRiAguLi6ICKGhoVy/fp0CBQpogkhL4jIEKiU/njQEVqmU7tKlS7Jw4UK5deuWiIj4+PjI888/b5svMH/+fAHk7NmzDrnerVu3ZOTIkZI9e3YBpHXr1nLgwAG767t69aqULl1aZs6cKSLWobYJWeFVJT1S4BBYpdKN4OBgVq1axaVL1ttuBw4coGzZsuzZswewLgDZs2dPjh07Bli7SF9//XXb+W+88QbXr19PcIv45s2bfPrppxQvXpwvvviCJk2a8Oeff7JmzZp/LED5LBaLhVOnTgGQL18+XnnlFUqXLg1Y70PoLOk0Ki6ZJCU/tCWhkkNERIT88ssv4u/vLyIiQUFBUqNGDVm1apWIiJw8eVIAWbp0qYiInDt3Tjp06CAHDx4UEZHQ0FDx9/eXyMjIRInv2rVrMnz4cMmaNasYY6Rjx45y9OjRBNXZr18/yZ07t9y7d89BUarkREqbcZ1YD00SKjFYLBb5448/bBs/RURESMOGDWXu3LkiIhISEiKAfPnllyIicv/+fXn11Vfl559/FhHrJlKHDh2S0NDQJI37ypUrMmTIEMmSJYs4OTlJ165dxdfX1666LBaLrF+/Xm7evCkiIocOHZLly5frpj9phCYJpRIoV65cMmDAAFu5adOmMn/+fFt5586dcv369eQI7R8uXbokgwYNEldXV3FycpLu3bsneGfD06dPizHGtsSHSls0SSgVTzdv3pSRI0dKdHS0iIj89ttvEhAQkMxR/bvAwEAZOHCgZMqUSZydnaVHjx5y5swZu+vbt2+fzJkzx1b+5ZdfEq1LTCUvTRJKxdOSJUskY8aM8scffyR3KM904cIF6d+/v2TMmFEyZMgg77zzjkNGQvXp00cKFSokDx48cECUKiWLa5LQ0U0qXRMRzp07B0C3bt3w8/NL0bODz58/j6enJ6VLl2bevHn07NmTM2fOMG/ePEqWLBnv+i5fvsxbb71l289h/PjxnDhxQmfFKxtNEipd+/jjj6lZsybXrl3DGJNiJ2GePXuWd955B3d3dxYvXkyfPn04e/Yss2fPpnjx4vGuz/qPJDg7O7Nx40YOHjwIQJ48eciePbsjQ1epXNrfvlSpf9GrVy8KFChAvnz5kjuUJzpz5gzjxo1j6dKluLi4MGDAAIYPH06hQoXsrnPMmDGcPHmS5cuXkz9/fi5evEjmzJkdGLVKSzRJqHRn1qxZnD9/nq+++ooyZcpQpkyZ5A6J8PBwTp06ha+vL76+vpw4cQJfX1/8/f1xdXXl/fffZ9iwYRQoUMCu+h/uIw2QMWNGMmfOTHR0NBkyZNAEof6VJgmV7pw6dYrTp08TFRWFi4tLkl77acng7NmzWCwWwNoF5O7uTpUqVXj77bfp3bs3+fPnt/ua+/fvp2nTpqxatYoGDRrw8ccfO+rHUemAJgmVLhw9epTMmTPj7u7O//73P5ydnXFySrxbco8mg4eJ4N/+7SVzAAAgAElEQVSSQZcuXahQoQIVK1akTJkyZMyYMUHXj4iI4MqVKxQvXpxKlSrRokULXXRP2cU8vIGVWtWoUUMOHDiQ3GGoFCwyMpJSpUpRqVIlNm7c6NC645MMKlasaEsEFStWxN3dnUyZMjk0nocaNmzI3bt3OXDgQKImQ5V6GWMOikiNZx2nLQmVZkVERJApUyYyZsyIt7c3pUqVsruu+LYMOnfunCTJ4CGLxcKGDRto3rw5zs7ODBs2DGdnZ110TyWYtiRUmnT16lUaN27M0KFD6dGjR7zOtVgs+Pj4cPDgQVtS8Pf3/0cyeLRVUKFCBcqUKZPoyeBpNm7cSPPmzVmxYoXuIa3iRFsSKl3LmzcvlSpVivdQ0eDgYN58803Wr1+Ps7MzpUuXplKlSnTq1OmxewbJlQwetWPHDoKDg2nVqhVNmzZl9erVtGzZMrnDUmmMtiRUmhEeHs64ceMYNmyYXRPCTp06RevWrTl79ixTpkzB09MzRSSDJxER6tWrR2RkJH/88Yd2K6l4i2tLQu9oqTTjyJEjTJgwgZ9//jne565bt45atWpx+/Zttm7dynvvvZfiEsSZM2d4++23CQkJwRjD999/z44dOzRBqESlSUKlegEBAQDUrl2b06dP07lz5zifa7FY+OKLL2jVqhWlS5fmwIED1K9fP7FCTZA7d+6wZs0a/vzzTwCKFSumE+FUotMkoVK1BQsWUK5cOQ4fPgwQr7WXQkJC6NChA6NGjeLNN99k165dFC1aNLFCtctHH33EqFGjAKhVqxaXLl3i5ZdfTuaoVHqiN65Vqta2bVuCgoKoUKFCvM7z9/enTZs2+Pn5MXXqVD744INk67YJCwsjS5YsAIwdO5ajR4+ycuVKwLqw36OJz83NLVliVOmXtiRUqrNu3Tq6deuGxWIhV65cjBo1Kl4zlDdt2kTNmjW5cuUKmzdvZtCgQUmWIIKDg9m5c6et/PHHH1OkSBHbqqyurq62hAGwfPlyJk6cmCSxKfUkmiRUqhMUFISfnx937tyJ13kiwldffUXz5s0pVqwYBw4coFGjRokUpVVAQACTJ08mLCwMAC8vL15++WVu3rwJWGdGDxs2jMjISACGDx/Od999Zzvf2dk5UeNT6pnisjNRSn7oznTpQ2BgoOzdu1dERCwWi0RERMTr/NDQUOnYsaMA0qlTJwkNDU2MMOXEiRPSrVs3OXXqlIiIrFmzRgDbbncBAQGyadMm3flNJTsSc2c6Y8wMY8yi2K9fdVzKUurJunXrxptvvkl0dDTGmHh1L507d466deuyYsUKvvrqK5YtW2ZbNtse9+/fJzg4GLC2FCpWrMjatWsB6z9dv/32G4GBgQA0adKEa9eu2Xa7K1GiBE2bNtWd31SqYe+N60jgWuzXDYEtjglHqb/ExMQgImTIkIHZs2fj5OREhgzxe8tu27aNjh07YrFY2LhxI02bNo3X+SLC3r17yZYtG5UqVSI4OJjcuXMzfvx4hg8fToECBShdurTthnL58uUJCgqynZ8lS5bH7jEoldrYe08iDMhhjHEBUtaYQZUmhIeH06RJEz777DPA+uFbtmzZOJ8vIkydOpVXX32VAgUK2PZUiIsbN26wZs0aW7lly5ZMnz4dgBw5cjBhwgReeeUVADJnzoyPjw8NGzYE0IltKs2xtyUxGvAEZgA/OC4cpaxcXV2pWrUq5cuXj/e5Dx48wNPTk6VLl9KuXTsWLVpEtmzZ4nx+165dCQ8Pp3Xr1hhj8PHxeWwY6tChQ+Mdk1KplV1rNxljKgDFgGMiEvSs4xOTrt2Udty/f5+xY8fSr1+/eE2Ke9TFixdp27Ytf/75J2PHjuWTTz6J934KJ06c4N69e7z44ot2xaBUapDYazeNAbIBnsaYxXbWodIZEWHr1q22ZSWioqIoU6YMEyZMACBDhgxMnjyZH36wr3G6Y8cOatSogb+/P2vXruWzzz6Lc4KYN28eY8eOBaBChQqaIJSKZW930y8i8iPwoyODUanfrVu3CAkJoXjx4gD06dOH4sWL8+mnn2KM4c033+T1119n/vz5uLi40KhRI9zd3QHIlCkTt27dIkeOHPG6pogwY8YMPvzwQ0qVKoWPj0+87l8A7Nmzh0uXLhEdHR3vm+NKpWX2tiTqGmNWGWPmGmMGOzSipNSgASxaZP06KspaXrrUWg4Ls5a9va3l4GBredUqa/nmTWt53Tpr+epVa3nTJms5MNBa3rrVWg4IsJZ37LCWT52ylnfvtpaPH7eW9++3lg8ftpZj1yRi/35r+fhxa3n3bmv51ClreccOazl2sTu2brWWY4dismmTtXz1qrW8bp21HDupi1WrrOXYoZ14e1vLsZPAWLrUWo6KIioqyvq6NWjAggULmDx5MsydC40b89prr9G3b1+YOROaNSM0NNQ6kWz6dGjVip9//pnx48fDpEnQvj2zZs2iffv2MGECdO78V4L44gt4882/flejRkHPnn+VP/4YPD0JDw/nnXfeIfy991hTuDB//PGHNUEMGmR9PPTuu/DovQRPT6KGDuXaNesgvbnR0fxco8ZfCeLNN60xPNS5szXGh9q3t/4MD7VqZf0ZH2rWzPoaPNS4sfU1ekjfe3a99wDbe88m9r1nE/ves4l979nEvvdsYt97NnF879kMHWp9fz0Uh/ceH3/8V7lnT+s1Horvey+R2fsv03ERmWSMyQBUdGRAKmU5ePAgvufO8VZst03fvn3Z9ccf+A4bBsDmzZu5fPkyQ956C4AxY8ZYVyY9eRKAZcuWWSuK/QB94YUXHBbb/fv3aVi/Pvv27WPXiy9St1o1TBxbISLCihUrmPZ//8fevXut3VK6F7RS/xSXGXd/fwA7gPeBMvac78iHzrh2rDlz5ki1atXEYrGIiMjgwYMlc+bMEhMTIyIi3t7e8tVXX9mOj46OTpY4d+3aJfnz5xc3NzdZvXq1XXVs2LBB1q5d6+DIlEodSMwZ10BH4BTQxhgz91kHP2SMec0Yc8oY42+M+egJz/cwxtwwxhyOffS2Mz4VR76+vrz66qtcuXIFsM4DKFmypG2toREjRnDx4kXbDeCOHTsyfPhw2/nJsbbQnDlzeOWVV8iWLRt79+6lTZs2cT73559/ZlVst03z5s11u0+lnsHe7qZ3gfLAfeDLuJxgjHHGOq+iCRAE7DfGrBWRE3871FtEBtoZl4ojEcEYQ6ZMmTh16hT+/v4UKFCATp060alTJ9tx+fLlS8YoHxcZGcl7772Hl5cXzZo14/vvvydXrlxxPl9EmDBhAhaLhbZt2+rEN6XiwN4kkUtEOhhjMgJTsSaNZ6kF+ItIAIAxZjnQGvh7klCJbPjw4YSGhjJz5kxKly7N2bNnU/yInitXrvDGG2+we/duPv74Y7744os4t2Kio6OJjo7G1dWVVatWkTlzZk0QSsWRvd1NkcaY6rFfx3WltEJA4CPloNjv/V17Y8xRY8xKY0wRO+NTfxMTE2P7+tH+RiDFJ4h9+/ZRo0YNDh8+jLe3N+PHj49zgrBYLLRp04YePXogIuTJkydBi/spld7YmyQ+BV4B5gDejguHdUBxEakC/AI8caKeMcbTGHPAGHPgxo0bDrx82nTo0CHKlCnDkSNHAJg4cSKzZs1KFf9NL1y4kHr16pEpUyb27NlDx44d43W+k5MTDRo04JVXXkkVP69SKY29/0L2FpFJAMaYnHE85xLwaMugcOz3bETk1iPFecATt+QSES/AC6zLcsTx+unOgwcPyJw5MyVKlKBEiRLW+Q2kjkXooqKiGDx4MN9++y2NGzdm+fLl5M6dO87n79+/HxcXFzw8PHStJaUSwN6WRLFHvv4kjufsB9yNMSVi72V0BtY+eoAxpsAjxVbASTvjS/feeecdWrZsiYiQK1cutm7dSo0az1ymJUW4fv06jRs35ttvv2XIkCFs3LgxXgkiJiaG7t278/7779u61JRS9rG3JeFkjKkH7Aaei8sJIhJtjBkIbAacgQUi4muMGYt1vO5a4H1jTCsgGrgN9LAzvnTp1q1bPPfccxhjqFOnDqVLl8ZisaSaLTBFhH379tGhQwdu3LjB999/T9euXeN1PliH5a5atYo8efKkilaTUimZvavAOgH9gReANSKyztGBxZWuAmt16NAhGjRowPfff58qxv5HRUXh5+fH4cOHH3vcvn2bokWLsnr16njNzo6OjqZv3764u7vz0Uf/mIKjlPqbuK4Ca2+SWI71v30BrojI8GeckmjsThKDBv21Nk0qJSJEREbimikTFhHO+vtTqHBhsmTOnNyhPSY6OprQ+/cJDQ21Pe7fv2/7z9/JGLK6ueEW+8iXNy8uLi7xuoYAJ0+eJEvmzLbFBZVK8zw8YNo0u06Na5Kwt7tpj4hMj71Q3DuLlUOdOHmS0NBQatasiZMxttVUk4sAERERjyWD0NBQwsPDbce4uLjg5uZG4cKFbUkhSwLmLTx48ADnDBnI6OJC+fLl0c4lpRzL3iTR2hgjwCYROe3IgJKMndk3ufn6+uLu7k7GjBm5um0bN27coGbHjkm+OF1UVBQnT578R3fRnTt3AOsIKnd3dzwaNqRq1ap4eHjg4eFBgQIFHHafICIigrLu7lSpUoX169drglAqEdibJN4EqgLtjDGlRKSPA2NST3Hs2DE8PDyYPn06AwcOpFGjRkly3eDgYI4cOfJYMvD19SUyMhKwbjVapUoVOnToYEsGlStXxs3NLVHjypQpE7Nnz6ZMmTKJeh2l0rN435OIHY2UATgMHE7ulkRav3EdGhrKsWPHqFOnDiLCrFmz6Ny5M889F6dBZfEiIgQGBv6jdXDu3DnbMXnz5qVatWq2ZODh4YG7u3uSzdoWEcaMGUP16tVTxQ16pVKqxL5xnR/wiH2UTs6WRFpPEl27dmXz5s0EBgaSJUuWRLnG5cuXGTZsGBs3bvxnd9EjycDDw4Pnn38+WYeVhoeHU69ePWrXrs23336bbHEoldo5LEnELtfdBvgJWAYMxjrPYY2IHHNArAmS1pKEiPDzzz9Tp04dnnvuOU6ePElwcHCi7LlssViYO3cuw4cPJzIykm7dulG9evUk6y6Kj5s3b5I9e3YyZsxISEgIbm5uOgdCqQSIa5KIywZDfkAlYDZwDFgEeAK/AW/HZdOKxHyktU2Hzpw5I05OTjJ27NhEvc7JkyelXr16Asgrr7wiZ86cSdTrJURISIiULFlS+vbtm9yhKJVmEMdNh+LSkRwpIseNMYOAm0ANEYkwxiwGdvKURfhU3F28eJGdO3fSrVs3SpcuzZYtW3j55ZcT5VqRkZF89dVX/Pe//yVr1qwsWLCAHj16pOj/yt3c3Hj33Xd56aWXkjsUpdKduHQ3jcF672ERkE1Evov9vhPW/SFKJnaQ/yYtdDcNGDCApUuXEhgYSI447tFsjz179tCnTx98fX3p1KkT06dPJ3/+/Il2vYSaP38+derUoUKFCskdilJpTly7m545uF5ERmPdUa4R0N8Yc9EYsw34P+COMaZ8bMJQcSQieHt74+/vD8CYMWM4fvx4oiWIkJAQ3nvvPf7zn/8QHBzMunXrWL58eYpOEMHBwXz22WdMnz49uUNRKl2zZwisAcoC1bC2MKoBZUWk2L+emEhSY0vixo0blCpVinfeeYepU6cm6rXWr19P//79uXTpEgMHDmTcuHFky5YtUa+ZEBEREWTMmBFjDAEBARQtWjTFb4qkVGrksJbE38Xe8/ATkWUiMkJEXk2uBJFgDRrAokXWr6OirOWlS63lsDBr2Tt2T6XgYGt51Spr+eZNa3ld7NqGV69ay5s2WcuBgdby1q3W0//8kytly8KOHeTNm5f9S5cy5dAh2L3bevzx49bj9++3lg8ftpYfri+1f7+1fPy4tbx7t7V86pS1vGOHtRwQAMDtH3/EN18++rdsSY4cOfCdPJmvjx4l2/371uPXrbMef/OmtbxqlbUcHGwte3tby2Fh1vLSpdZy7J4ULFpkLT80dy40bvxXeeZMaNbsr/L06dCq1V/lSZOgfXvAep+ECROwdOxI5cqVmTZtGnzxBSVHjforQYwaBT17/nX+xx+Dp+df5aFD4d1HdtEdNMj6eOjdd63HPOTpaa3joZ49rdd46M034Ysv/ip37gwTJvxVbt/e+jM81KqV9Wd8qFkz62vwUOPG1tfooSR87xEQYC3v2GEtnzplLSfSe4+tW63lwNiNKDdtspavXrWWU9B7D7D+Xjt3/qv8xRfW3/9DKf29l8i0myiJeHt7c+r0aS5evAhA2bJlE+VmsYiwYMECevXqxc2bNxk2dCiHDh2ifPnyDr+WPaKjo7l8+bKtXLt2bfr0sU6zcXJyolatWpQtWza5wlNK/Y1dk+lSkpTc3bR3717bB194eDinT5+mSpUqiXa9M2fO0LdvX7Zv387LL7+Ml5dXsn/g3r59m7Nnz1KzZk0AmjRpQmhoKHv27AHgf//7H88//zzdu3dPzjCVSncSdcZ1SpJSk0R0dDTu7u6UK1eOjRs3Juq1oqKimDx5MmPGjCFTpkxMnDiR3r1745TEi/4BXLhwgT179tA5tvneu3dvVq9ezY0bN3BycmLt2rVERkbyxhtvJHlsSqm/OGwyXUp/pKTJdBEREbJgwQKJjo4WEZEjR45ISEhIol5z3759UrVqVQGkXbt2cunSpUS93t+dPn1a/vvf/8qDBw9EROSrr74SQK5duyYiIocPH5bt27dLTExMksallPp3xHEyXbJ/yCf0kZKSxOrVqwWQdevWJfq1QkJCZNCgQeLk5CQFCxaU1atXJ/o1RUT8/f1l4MCB4u/vLyIia9asEUD2798vIiJXrlyRkydPalJQKoXTJJFEzp07J7/++quIiFgsFtmxY0eiX3Pjxo1SrFgxAaR///5y9+7dRLvW5cuXpX379vLLL7+IiHU5j6xZs8qGDRtERCQsLExu376daNdXSiWOuCYJHYCeQL169eLChQucPn0aZ2fnRFtOA6zzKz788EO+//57ypUrx86dOx2+VEV4eDiNGzemY8eOvP/+++TKlYtjx45xM3a4YtmyZbl7965taGrmzJnJnMK2S1VKOY4miXgSEXx8fGjcuDFubm7MnDmTrFmz4uzsnKjXXLJkCYMHD+bevXuMGjWKTz75hEyZMjmk/jVr1nDmzBmGDRuGq6srhQoVss3+dnV15dTD8fBYlxDXyW1KpR86TyKejh8/Ttu2bfHy8gKgXLlyFClSJNGuFxAQQNOmTXn77bcpU6YMf/75p20Uk6OsWbOGlStXEhU7Wcnb25u3337bYfUrpVIv/ZcwDoKDg9m7dy9NmzalcuXKbNmyhVdeeSVRrxkdHc20adMYFTvreMaMGfTr189hw1qPHz9O9uzZKVq0KDNmzMDFxQUXFxeH1K2USju0JREHI0aMoF27drZd25o0aZKoXS5//vkntWvXZtiwYTRu3JgTJ04wYMAAhyWIsLAwGjZsyODBgwHImjUrGTNmdEjdSqm0RZPEUxw4cICgoCAARo4cyf/93/+RK1euRL1mWFgYI0aMoGbNmly6dIkff/wRHx8fChcu7LD6AbJkycKyZcuY+ejaQkop9QSaJJ7gzp071K9fny9iF9kqVKgQ1atXT9Rrbt26lcqVKzNx4kR69uzJyZMn6dChg8PWd/Lz88Pd3Z01a9YA0KhRI/Lly+eQupVSaZcmiVjR0dFs3rwZgFy5crF69Wr+97//Jfp1b9++Tc+ePWnSpAnOzs5s376duXPnOrzVUrJkSerXr0/x4sUdWq9SKm3TJBFr+vTpvPbaaxw7dgyAV199lezZsyfqNa9du8ZLL73E0qVL+eSTTzhy5AgNHl0COYECAgLo0aMHDx48IGPGjPzwww94eHg4rH6lVNqXrpPExYsX8fPzA6Bv3774+PhQqVKlJLn2jRs3aNSoERcuXGDr1q2MGzfO4ZPSTp8+zdq1azlx4oRD61VKpR/pdhXYmJgYypQpQ7Fixfj1118TIbKnu3XrFg0bNuTMmTNs2LDBocNpQ0JCOHjwoK1FEhwcnKj7ZiulUqe4rgKbbudJODs7M2/ePEqWLJmk1719+zaNGzfm9OnTrFu3zuHzLQYNGsSKFSu4cOECuXLl0gShlEqQdNuSSA53796lcePGHDt2jLVr19K0aVOH1GuxWAgPDydLlixcvnyZs2fPUq9ePYfUrZRKm7QlkcIEBwfTtGlTjh49ypo1axyaIFq0aEH27NlZtmwZBQsWpGDBgg6pWymlNEkkgZCQEJo1a8ahQ4f46aefaN68ucPqdnJyokmTJri5uTmsTqWUeihJRzcZY14zxpwyxvgbYz76l+PaG2PEGPPsrfVSuNDQUJo3b86+ffv48ccfadWqVYLrDA8PZ9CgQezduxeADz/8kD59+jhs4p1SSj2UZEnCGOMMzACaARWALsaYCk84LhvwAfBHUsWWWO7fv0+LFi3Ys2cPy5Yto23btg6pNzw8nLVr17Jjxw6H1KeUUk+TlC2JWoC/iASISCSwHGj9hOO+AL4CwpMwNocLCwujVatW7Ny5kyVLltChQ4cE1ScirF27FovFQs6cOTly5AgjRoxwULRKKfVkSZkkCgGBj5SDYr9nY4x5ASgiIhuSMC6HCw8Pp02bNmzfvp3FixfTpUuXBNe5ZcsWWrdujbe3NwDZsmVLcJ1KKfUsKWbGtTHGCZgCDInDsZ7GmAPGmAM3btxI/ODiISIigrZt27J161YWLFjAm2++maD6QkJCAOsyIatXr6ZTp06OCFMppeIkKZPEJeDRLdwKx37voWxAJeA3Y8x54EVg7ZNuXouIl4jUEJEaefPmTcSQ4ycyMpI33niDTZs24eXlRY8ePRJU35w5cyhbtixXr17FGEObNm0ctqeEUkrFRVJ+4uwH3I0xJYwxGYHOwNqHT4pIsIjkEZHiIlIc2Au0EpFUMVMuKiqKjh07sn79embPnk3v3r0TXGe9evVo0aIFWbNmdUCESikVf0mWJEQkGhgIbAZOAj+KiK8xZqwxJuHjQpNRVFQUXbp0wcfHh2+//Za+ffvaXZe3tzdjx44FoEKFCnh5een9B6VUsknSvgsR+VlEyohIKREZF/u9USKy9gnHNkgNrYjo6Gi6d+/OTz/9xNSpU3n33XcTVN9vv/3G5s2biYyMdFCESillP51xnQAxMTG8/fbbeHt7M2nSJAYNGmRXPYcOHSJHjhyUKlWKqVOn4uzsjIuLi4OjVUqp+NO7oHaKiYmhV69e/PDDD3z55ZcMGfLMQVlPFB4eTvPmzW3nu7q6aoJQSqUY2pKwg8ViwdPTk++++44vvviCjz566gojz+Tq6srKlSspX768AyNUSinH0JZEPFksFvr378+CBQsYPXo0n332mV31REVF2TY7eumll8idO7cjw1RKKYfQJBEPIsJ7772Hl5cXn3zyCaNHj7a7rtmzZ9OoUSMOHjzowAiVUsqxtLspjkSEQYMGMXPmTIYPH85///vfBK266unpSb58+ahevboDo1RKKcfSJBEHIsLQoUP5+uuvGTx4MBMmTLA7QTx48AAnJycyZcqkS2wopVI87W56BhHho48+YsqUKbz//vtMmjQpQS2IoUOHUrt2bcLDU/Uit0qpdEJbEv9CRPjss8+YOHEi/fv3Z9q0aQne2Of111+nYMGCuLq6OihKpZRKPEZEkjuGBKlRo4YcOJA4E7M///xzxowZg6enJ7NmzdLF9ZRSaYYx5qCIPHP3T/3Ue4r//ve/jBkzhl69ejkkQfTu3Zv58+c7KDqllEoamiSeYMKECYwcOZK33noLLy+vBCeIsLAwzp8/z7Vr1xwUoVJKJQ29J/E3kydP5uOPP6Zr164sWLAAZ2fnBNeZJUsWtmzZQmrv2lNKpT/aknjE9OnTGTp0KJ06dWLx4sUJThAWi4WJEydy9+5dnJycHJJwlFIqKWmSiDVjxgwGDRpE+/btWbJkCRkyJLyRdeDAAT755BNWr17tgAiVUirpaXcT1m1CBw4cSJs2bVi2bJnDVmGtVasWx44do1y5cg6pTymlklq6b0nMnz+ffv360aJFC7y9vR2SIKKiojhy5AgA5cuXT/DcCqWUSi7pOkksWrSIPn360KxZM1auXEnGjBkdUu+0adOoXr06fn5+DqlPKaWSS7rtbvr+++/p1asXjRs3ZtWqVWTKlMlhdffp04ccOXJoN5NSKtVLty2JokWL0qpVK3x8fBy2REZERAQWi4WcOXPi6enpkDqVUio5pdskUa9ePdasWUPmzJkdVueQIUNo2rQp0dHRDqtTKaWSU7rtbkoMHh4e5MiRwyHDZ5VSKiXQTzMH6t27d3KHoJRSDpVuu5sc6YMPPmDDhg3JHYZSSjmcJokECg4OZvv27fz555/JHYpSSjmcdjclUI4cOdi/f7+uy6SUSpO0JWEnEWHu3LlERESQKVMmvVmtlEqTNEnYafv27Xh6erJy5crkDkUppRKN/vtrp4YNG/L7779Tp06d5A5FKaUSjbYk4ikqKooLFy4AULduXV28TymVpmmSiKeJEydSqVIlW6JQSqm0TLub4ql79+5kzJiRYsWKJXcoSimV6Exq33e5Ro0acuDAgUS/TkxMjA5zVUqlGcaYgyJS41nHaXdTHH344Ye8/fbbWCyW5A5FKaWSTJImCWPMa8aYU8YYf2PMR094vp8x5pgx5rAxZpcxpkJSxvc0IkLu3LnJkycPTk6aV5VS6UeSdTcZY5yB00ATIAjYD3QRkROPHJNdRO7Fft0KGCAir/1bvUnV3aSUUmlJSuxuqgX4i0iAiEQCy4HWjx7wMEHEygok+w2TTz/9FE1CSqn0KilHNxUCAh8pBwG1/36QMeZdYDCQEWj4pIqMMZ6AJ1h3mEss169fZ/Hixbi6ulKjxjMTrlJKpTkpbgisiMwAZhhjugKfAW8/4RgvwNWMahIAAA1CSURBVAus3U2JFUu+fPk4duwY2bJlS6xLKKVUipaU3U2XgCKPlAvHfu9plgNtEjWipxARVq1ahcViIVeuXLp4n1Iq3UrKJLEfcDfGlDDGZAQ6A2sfPcAY4/5I8XXgTBLGZ7N27Vrat2+Pj49PclxeKaVSjCT7F1lEoo0xA4HNgDOwQER8jTFjgQMishYYaIxpDEQBd3hCV1NSaNWqFT4+PrRs2TI5Lq+UUimGzrh+RHR0NMHBweTOndsh9SmlVEqVEofApnhffvkllSpV4tq1a8kdilJKpQh6R/YRLVu2JCoqivz58yd3KEoplSJoksA6mskYg4eHBx4eHskdjlJKpRja3QQMGTKETz75hNR+f0YppRwt3bckLBYLISEhxMTE6C5zSin1N+k+STg5OTF37lxdAlwppZ4gXXc3ffXVV5w7dw5AlwBXSqknSLefjIGBgYwfP54ffvghuUNRSqkUK912NxUpUoTjx49ToECB5A5FKaVSrHSbJMCaKJRSSj1duu1uUkop9WyaJJRSSj2VJgmllFJPpUlCKaXUU2mSUEop9VSaJJRSSj2VJgmllFJPpUlCKaXUU6X67UuNMTeAC8kdRwqQB7iZ3EGkIPp6/EVfi8fp62FVTETyPuugVJ8klJUx5kBc9qtNL/T1+Iu+Fo/T1yN+tLtJKaXUU2mSUEop9VSaJNIOr+QOIIXR1+Mv+lo8Tl+PeNB7EkoppZ5KWxJKKaWeSpNEKmOMec0Yc8oY42+M+egJzw82xpwwxhw1xmwzxhRLjjiTwrNei0eOa2+Mkf9v78yDrCquOPz9GBdEXKKAEkWHWKhBtCRiFNwGYxk1ETFxiaUx4xpJRUri8odGC5OyoiFoYlyRxKmgQcAoAiZBjcCgYFxmZFVccIwEE5eoCRhcyMkffd7M5fLuvDfOzHtvxv6qXr2+fbtvnz739O3lvndaUrf+RUsx+pB0mtvHCkndelvGItrKHpLmSWr09nJCOeSseMwsfrrIB6gCXgW+BGwFLAEGp9KMBHp5eAwwrdxyl0sXnm47oB54ChhWbrnLbBuDgEbgC37cr9xyl1kfk4AxHh4MNJVb7kr8xJlE1+KrwCtmttrMPgbuA05KJjCzeWb2oR8+BexeYhlLRUFdOD8FbgA2lFK4MlCMPi4AbjWz9wDM7K0Sy1hKitGHAdt7eAdgbQnl6zLETqJrsRvwRuJ4jcdlcR7wp06VqHwU1IWkrwADzOzhUgpWJoqxjb2BvSU9KekpSceVTLrSU4w+xgNnSVoD/BG4uDSidS0+13tcd2cknQUMA44qtyzlQFIP4EagtsyiVBJbEJacaggzzHpJ+5vZ+2WVqnycAdSZ2URJw4EpkoaY2f/KLVglEWcSXYu/AwMSx7t73CZIOga4ChhlZh+VSLZSU0gX2wFDgPmSmoBDgVnd+OV1MbaxBphlZp+Y2WvAS4ROoztSjD7OA6YDmNlioCfBr1MkQewkuhbPAIMkDZS0FfAdYFYygaShwJ2EDqI7rzm3qgsz+8DM+phZtZlVE97PjDKzZ8sjbqdT0DaAmYRZBJL6EJafVpdSyBJSjD7+BnwNQNKXCZ3E2yWVsgsQO4kuhJl9CvwQmAu8AEw3sxWSfiJplCebAPQGZkh6XlK6YXQLitTF54Yi9TEXeFfSSmAecLmZvVseiTuXIvVxKXCBpCXAVKDW/KdOkRbiP64jkUgkkkmcSUQikUgkk9hJRCKRSCST2ElEIpFIJJPYSUQikUgkk9hJRCKRSCST2EkUgaTdJT0k6WVJqyXdImnrTiinRtKIxPFFks72cJ2kUzq6zERZU90T5rhU/GhJgxPH8yvtD2lJ3UianJNX0pWpdIs6s+xU/L7+E+RGSXu1s4zxki5rzzU6E7fbOR4elfO4mradTpahVtItHm5uN63IO6KV88k6tLndlcLuSknsJAogScADwEwzG0T4h+o2wM87obgaoNl4zewOM/tdJ5SzCZJ2BUaY2QFmdlPq9GiCh8wugZmdb2Yr/fDK1LnMB0MnMBp4yMyGmtmrJSy3rJjZLDO73g/bbDuS2u0qqIh2U0OinaXLT9Xhs1BOu+twYidRmKOBDWZ2N4CZbQTGAWdL6p0cwQBImiOpxsO3S3rWffdfm0jTJOlaSQ2Slvmosxq4CBjnI9AjskaQkg6StEDSc5LmSurv8WPVspfEfXny9ZR0t5fZKGmkn3oE6JcrN5F+BDAKmODnciPiUyU9LemlXHpJVZImSHrGy/9+PmVKOtvPL5E0xeOqJT2ulj0w9vD4Okk3S1rkM7jcbEE+m1sp6WGgX+L68yUNk3Q9sI3Lfa+fW5fIP0HSctfF6R5f4/nvl/SipHt9kICka7xuyyVNysVn1PEE4BLgfEnzPO5Hnne5pEsSabPir1LYC+ExYJ+McvpK+oPL9Yykwzz+V5Ku8fDXJdVL6uH6vEPSQr9332zt3hXQx3Ee9wTwrYRMtX5vNrMdJWahkvoouEvJ5ZkhaTbBFpF0eUKe5raTqv85Xo8FwGGJ+OZ2o1SbUP52VifpRr9XNyjVpoFj8ugsb7vvaLurCMrtq7zSP8BY4KY88Y3AgQQHcrck4ucANR7eyb+rgPnAAX7cBFzs4R8Akz08Hrgsca3mY6AOOAXYElgE9PX404HfengtsLWHd8wj86WJtPsS3BL0BKqB5Rn1rwNOSRzPByZ6+ATgMQ9fCPzYw1sDzwIDU9faj+AvqE9KP7OB73n4XMKsLVf2DMJgZjDB9TOEh9KjrtcvAu/nZHT5hnl4Xar8df797UT+XVwP/QkjzA8Ifn56AIuBw5OyengKcGI+/WTcu4OAZcC2hH/DrwCGFhHfi+DK+pWkXSTK+H1Cvj2AFzzcy681ElgF7JWQ9c9et0EEX049s+5dlj48zxt+DRH8H83x/LV4e0jrJnVv+uD7N3ieNbTYw7GEvR7k5c4BjkzVvb/ft76E/SKeTJSb1P1mbYLN21mdl1GVUYd8OmtOk6fdd5jdVcIneoHtXE6TdCHB+2Z/woNuqZ97wL+fIzESK4J9CI7rHvXBRhXwpp9bCtwraSbBT0+aw4FfA5jZi5JeJ/jv+Xcbyk/LXu3hY4ED1LJ+uwOhUb2WyHc0MMPM3nEZ/uXxw2nRwRQ2XcqbacEr50pJu3jckcBUC7O6tZIeb6P8hyfy/9NHogcT9PC0ma0BkPS81+8JYKSkKwgP4J0ID+HZbSjvQTNb79d9ADiC8BDMF9/D4z/0+CzXKscAgxODzu0l9TazdZIuIGy2NM42Xe6a7vp8WdJqwmAh6959nKGPdcBrZvayx99D6Gjaw6MJezjWP41+3NvlqU+kPwSYb2ZvuwzTCLacplCbyDHD7SEf+XT2Wfgsdld2YidRmJWEEXwzkrYHdiWM0oaw6bJdT08zELgMONjM3pNUlzvn5LyzbqRt90HACjMbnufcNwgP0FHA1ZL2s+DDpqPJJ7sIs6O5nVRWrozOJlneRmALST2B2wij4DckjWfTe1kuegCHmlm+DZX2B94lzLSSpP3wGBn3TmHZdDN9tEPeT2lpK2n9rU8WDfzMzO5sR1k5NmsTGenWZ8RDfp0l6wLtt4eO1HOHEt9JFOYvQC+1/MqoCphImGr+l7B0dKCv+Q4g7IgFYZlgPfCBj4CPL6Ks/xBcXLfGKqCvgv97JG0paT+F/RMGmNk84ApgR8IILMlC4EzPtzdhiWJVB8gEwZHaGElb5q4vadtUmscJ7zN29jQ7efwigpdOXL6FBcqqB073tfT+hGWVfHySkyfFwkT+voSHyNOtlJd7ALwjqTepQUMRLARGS+rlOjnZ47Li6z1+G0nbASdmXPcREhvlSDrQv/ckLC0OBY6XdEgiz6luq3sRtvZcRXH3LsmLQLVa3lGdkZEubTtNhKU0aF2Hc4FzXddI2k1Sv1SavwJHSdrZ5T41fZFW2kSxNp0jn86ayN/uoePsriKomN6qUjEzk3QycKukqwlroNPM7DpP8iRhSWUZsBxo8HxLJDUSliVWe7pCzAbul3QSGbtkmdnHvixws6QdCPfwl4S1/ns8ToT3KOnNZG4Dbpe0jDASqjWzjwq8I7sPuEvSWFpv2JMJU+QGf+n2NuHXLUnZV0i6DlggaSNhOaHW63q3pMs93zmtCQQ8SFi6Wub1XpCRbhKwVFKDmZ2Zyj+csO+xAVeY2T8k5V1GMLP3Jd3l5TUR3FAXjZk1+Ewy90CYbGaNEF7OZ8RPA54HXie70xxLsMulBDuolzQG+A1hzX2tpPOAOkkHe55VBH3tAlxkZhskFbx3qfps8GXUhyW9Q1gWGZInadp2fgFMl/RdwuAr6/qPKLjuXuy2uQ44C3grkeZNn9EtJiy3NhCWXpNUkadNKLwgb7Wdpcins7zt3ukQu6sUohfYNqLwq42pwMlm1lAofSRSKXiHNMfM7i+3LJGuQ5xJtBEzWwTsWW45IpFIpBTEmUQkEolEMokvriORSCSSSewkIpFIJJJJ7CQikUgkkknsJCKRSCSSSewkIpFIJJJJ7CQikUgkksn/AfZnQ9xjXyVIAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "n = models.shape[0]\n", "p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')\n", "p2 = plt.plot(models.q, models.ub, linestyle='dotted', color='black')\n", "p3 = plt.plot(models.q, models.lb, linestyle='dotted', color='black')\n", "p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')\n", "p5 = plt.plot(models.q, [ols['lb']] * n, linestyle='dotted', color='red')\n", "p6 = plt.plot(models.q, [ols['ub']] * n, linestyle='dotted', color='red')\n", "plt.ylabel(r'$\\beta_{income}$')\n", "plt.xlabel('Quantiles of the conditional food expenditure distribution')\n", "plt.legend()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp6ri7rt_h/e47a6629-8550-4ab3-b43b-eb83f0fb6785 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression diagnostics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example file shows how to use a few of the ``statsmodels`` regression diagnostic tests in a real-life context. You can learn about more tests and find out more information about the tests here on the [Regression Diagnostics page.](http://www.statsmodels.org/stable/diagnostic.html) \n", "\n", "Note that most of the tests described here only return a tuple of numbers, without any annotation. A full description of outputs is always included in the docstring and in the online ``statsmodels`` documentation. For presentation purposes, we use the ``zip(name,test)`` construct to pretty-print short descriptions in the examples below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate a regression model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Lottery R-squared: 0.348\n", "Model: OLS Adj. R-squared: 0.333\n", "Method: Least Squares F-statistic: 22.20\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 1.90e-08\n", "Time: 07:45:14 Log-Likelihood: -379.82\n", "No. Observations: 86 AIC: 765.6\n", "Df Residuals: 83 BIC: 773.0\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "===================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-----------------------------------------------------------------------------------\n", "Intercept 246.4341 35.233 6.995 0.000 176.358 316.510\n", "Literacy -0.4889 0.128 -3.832 0.000 -0.743 -0.235\n", "np.log(Pop1831) -31.3114 5.977 -5.239 0.000 -43.199 -19.424\n", "==============================================================================\n", "Omnibus: 3.713 Durbin-Watson: 2.019\n", "Prob(Omnibus): 0.156 Jarque-Bera (JB): 3.394\n", "Skew: -0.487 Prob(JB): 0.183\n", "Kurtosis: 3.003 Cond. No. 702.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "from statsmodels.compat import lzip\n", "import statsmodels\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.formula.api as smf\n", "import statsmodels.stats.api as sms\n", "import matplotlib.pyplot as plt\n", "\n", "# Load data\n", "dat = statsmodels.datasets.get_rdataset(\"Guerry\", \"HistData\", cache=True).data\n", "\n", "# Fit regression model (using the natural log of one of the regressaors)\n", "results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()\n", "\n", "# Inspect the results\n", "print(results.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normality of the residuals" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jarque-Bera test:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('Jarque-Bera', 3.3936080248431755),\n", " ('Chi^2 two-tail prob.', 0.18326831231663288),\n", " ('Skew', -0.48658034311223436),\n", " ('Kurtosis', 3.0034177578816346)]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = ['Jarque-Bera', 'Chi^2 two-tail prob.', 'Skew', 'Kurtosis']\n", "test = sms.jarque_bera(results.resid)\n", "lzip(name, test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Omni test:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('Chi^2', 3.7134378115971938), ('Two-tail probability', 0.15618424580304724)]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = ['Chi^2', 'Two-tail probability']\n", "test = sms.omni_normtest(results.resid)\n", "lzip(name, test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Influence tests\n", "\n", "Once created, an object of class ``OLSInfluence`` holds attributes and methods that allow users to assess the influence of each observation. For example, we can compute and extract the first few rows of DFbetas by:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.00301154, 0.00290872, 0.00118179],\n", " [-0.06425662, 0.04043093, 0.06281609],\n", " [ 0.01554894, -0.03556038, -0.00905336],\n", " [ 0.17899858, 0.04098207, -0.18062352],\n", " [ 0.29679073, 0.21249207, -0.3213655 ]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from statsmodels.stats.outliers_influence import OLSInfluence\n", "test_class = OLSInfluence(results)\n", "test_class.dfbetas[:5,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Explore other options by typing ``dir(influence_test)``\n", "\n", "Useful information on leverage can also be plotted:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics.regressionplots import plot_leverage_resid2\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "fig = plot_leverage_resid2(results, ax = ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other plotting options can be found on the [Graphics page.](http://www.statsmodels.org/stable/graphics.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multicollinearity\n", "\n", "Condition number:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "702.179214549006" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.cond(results.model.exog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Heteroskedasticity tests\n", "\n", "Breush-Pagan test:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: `het_breushpagan` is deprecated, use `het_breuschpagan` instead!\n", "Use het_breuschpagan, het_breushpagan will be removed in 0.9 \n", "(Note: misspelling missing 'c')\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "text/plain": [ "[('Lagrange multiplier statistic', 4.893213374093967),\n", " ('p-value', 0.0865869050235217),\n", " ('f-value', 2.50371594625644),\n", " ('f p-value', 0.08794028782672986)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = ['Lagrange multiplier statistic', 'p-value', \n", " 'f-value', 'f p-value']\n", "test = sms.het_breushpagan(results.resid, results.model.exog)\n", "lzip(name, test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Goldfeld-Quandt test" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('F statistic', 1.1002422436378148), ('p-value', 0.3820295068692508)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = ['F statistic', 'p-value']\n", "test = sms.het_goldfeldquandt(results.resid, results.model.exog)\n", "lzip(name, test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linearity\n", "\n", "Harvey-Collier multiplier test for Null hypothesis that the linear specification is correct:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('t value', -1.0796490077783811), ('p value', 0.283463924755859)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name = ['t value', 'p value']\n", "test = sms.linear_harvey_collier(results)\n", "lzip(name, test)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmpo4ip02n3/2d0426e5-d41f-4e16-9f92-ea01da75b45a vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression Plots" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "from statsmodels.compat import lzip\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import statsmodels.api as sm\n", "from statsmodels.formula.api import ols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Duncan's Prestige Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use a utility function to load any R dataset available from the great <a href=\"http://vincentarelbundock.github.com/Rdatasets/\">Rdatasets package</a>." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prestige = sm.datasets.get_rdataset(\"Duncan\", \"car\", cache=True).data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>type</th>\n", " <th>income</th>\n", " <th>education</th>\n", " <th>prestige</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>accountant</th>\n", " <td>prof</td>\n", " <td>62</td>\n", " <td>86</td>\n", " <td>82</td>\n", " </tr>\n", " <tr>\n", " <th>pilot</th>\n", " <td>prof</td>\n", " <td>72</td>\n", " <td>76</td>\n", " <td>83</td>\n", " </tr>\n", " <tr>\n", " <th>architect</th>\n", " <td>prof</td>\n", " <td>75</td>\n", " <td>92</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>author</th>\n", " <td>prof</td>\n", " <td>55</td>\n", " <td>90</td>\n", " <td>76</td>\n", " </tr>\n", " <tr>\n", " <th>chemist</th>\n", " <td>prof</td>\n", " <td>64</td>\n", " <td>86</td>\n", " <td>90</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " type income education prestige\n", "accountant prof 62 86 82\n", "pilot prof 72 76 83\n", "architect prof 75 92 90\n", "author prof 55 90 76\n", "chemist prof 64 86 90" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prestige.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prestige_model = ols(\"prestige ~ income + education\", data=prestige).fit()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: prestige R-squared: 0.828\n", "Model: OLS Adj. R-squared: 0.820\n", "Method: Least Squares F-statistic: 101.2\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 8.65e-17\n", "Time: 07:43:40 Log-Likelihood: -178.98\n", "No. Observations: 45 AIC: 364.0\n", "Df Residuals: 42 BIC: 369.4\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -6.0647 4.272 -1.420 0.163 -14.686 2.556\n", "income 0.5987 0.120 5.003 0.000 0.357 0.840\n", "education 0.5458 0.098 5.555 0.000 0.348 0.744\n", "==============================================================================\n", "Omnibus: 1.279 Durbin-Watson: 1.458\n", "Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.520\n", "Skew: 0.155 Prob(JB): 0.771\n", "Kurtosis: 3.426 Cond. No. 163.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "print(prestige_model.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Influence plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Influence plots show the (externally) studentized residuals vs. the leverage of each observation as measured by the hat matrix.\n", "\n", "Externally studentized residuals are residuals that are scaled by their standard deviation where \n", "\n", "$$var(\\hat{\\epsilon}_i)=\\hat{\\sigma}^2_i(1-h_{ii})$$\n", "\n", "with\n", "\n", "$$\\hat{\\sigma}^2_i=\\frac{1}{n - p - 1 \\;\\;}\\sum_{j}^{n}\\;\\;\\;\\forall \\;\\;\\; j \\neq i$$\n", "\n", "$n$ is the number of observations and $p$ is the number of regressors. $h_{ii}$ is the $i$-th diagonal element of the hat matrix\n", "\n", "$$H=X(X^{\\;\\prime}X)^{-1}X^{\\;\\prime}$$\n", "\n", "The influence of each point can be visualized by the criterion keyword argument. Options are Cook's distance and DFFITS, two measures of influence." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12,8))\n", "fig = sm.graphics.influence_plot(prestige_model, ax=ax, criterion=\"cooks\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see there are a few worrisome observations. Both contractor and reporter have low leverage but a large residual. <br />\n", "RR.engineer has small residual and large leverage. Conductor and minister have both high leverage and large residuals, and, <br />\n", "therefore, large influence." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Partial Regression Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are doing multivariate regressions, we cannot just look at individual bivariate plots to discern relationships. <br />\n", "Instead, we want to look at the relationship of the dependent variable and independent variables conditional on the other <br />\n", "independent variables. We can do this through using partial regression plots, otherwise known as added variable plots. <br />\n", "\n", "In a partial regression plot, to discern the relationship between the response variable and the $k$-th variabe, we compute <br />\n", "the residuals by regressing the response variable versus the independent variables excluding $X_k$. We can denote this by <br />\n", "$X_{\\sim k}$. We then compute the residuals by regressing $X_k$ on $X_{\\sim k}$. The partial regression plot is the plot <br />\n", "of the former versus the latter residuals. <br />\n", "\n", "The notable points of this plot are that the fitted line has slope $\\beta_k$ and intercept zero. The residuals of this plot <br />\n", "are the same as those of the least squares fit of the original model with full $X$. You can discern the effects of the <br />\n", "individual data values on the estimation of a coefficient easily. If obs_labels is True, then these points are annotated <br />\n", "with their observation label. You can also see the violation of underlying assumptions such as homooskedasticity and <br />\n", "linearity." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"income\", \"education\"], data=prestige, ax=ax)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x1008 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fix, ax = plt.subplots(figsize=(12,14))\n", "fig = sm.graphics.plot_partregress(\"prestige\", \"income\", [\"education\"], data=prestige, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the partial regression plot confirms the influence of conductor, minister, and RR.engineer on the partial relationship between income and prestige. The cases greatly decrease the effect of income on prestige. Dropping these cases confirms this." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: prestige R-squared: 0.876\n", "Model: OLS Adj. R-squared: 0.870\n", "Method: Least Squares F-statistic: 138.1\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 2.02e-18\n", "Time: 07:43:53 Log-Likelihood: -160.59\n", "No. Observations: 42 AIC: 327.2\n", "Df Residuals: 39 BIC: 332.4\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -6.3174 3.680 -1.717 0.094 -13.760 1.125\n", "income 0.9307 0.154 6.053 0.000 0.620 1.242\n", "education 0.2846 0.121 2.345 0.024 0.039 0.530\n", "==============================================================================\n", "Omnibus: 3.811 Durbin-Watson: 1.468\n", "Prob(Omnibus): 0.149 Jarque-Bera (JB): 2.802\n", "Skew: -0.614 Prob(JB): 0.246\n", "Kurtosis: 3.303 Cond. No. 158.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "subset = ~prestige.index.isin([\"conductor\", \"RR.engineer\", \"minister\"])\n", "prestige_model2 = ols(\"prestige ~ income + education\", data=prestige, subset=subset).fit()\n", "print(prestige_model2.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a quick check of all the regressors, you can use plot_partregress_grid. These plots will not label the <br />\n", "points, but you can use them to identify problems and then use plot_partregress to get more information." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress_grid(prestige_model, fig=fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Component-Component plus Residual (CCPR) Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CCPR plot provides a way to judge the effect of one regressor on the <br />\n", "response variable by taking into account the effects of the other <br />\n", "independent variables. The partial residuals plot is defined as <br />\n", "$\\text{Residuals} + B_iX_i \\text{ }\\text{ }$ versus $X_i$. The component adds $B_iX_i$ versus <br />\n", "$X_i$ to show where the fitted line would lie. Care should be taken if $X_i$ <br />\n", "is highly correlated with any of the other independent variables. If this <br />\n", "is the case, the variance evident in the plot will be an underestimate of <br />\n", "the true variance." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "fig = sm.graphics.plot_ccpr(prestige_model, \"education\", ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the relationship between the variation in prestige explained by education conditional on income seems to be linear, though you can see there are some observations that are exerting considerable influence on the relationship. We can quickly look at more than one variable by using plot_ccpr_grid." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12, 8))\n", "fig = sm.graphics.plot_ccpr_grid(prestige_model, fig=fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Regression Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot_regress_exog function is a convenience function that gives a 2x2 plot containing the dependent variable and fitted values with confidence intervals vs. the independent variable chosen, the residuals of the model vs. the chosen independent variable, a partial regression plot, and a CCPR plot. This function can be used for quickly checking modeling assumptions with respect to a single regressor." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "fig = sm.graphics.plot_regress_exog(prestige_model, \"education\", fig=fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot_fit function plots the fitted values versus a chosen independent variable. It includes prediction confidence intervals and optionally plots the true dependent variable." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "fig = sm.graphics.plot_fit(prestige_model, \"education\", ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statewide Crime 2009 Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the following to http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg_self_assessment_answers4.htm\n", "\n", "Though the data here is not the same as in that example. You could run that example by uncommenting the necessary cells below." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#dta = pd.read_csv(\"http://www.stat.ufl.edu/~aa/social/csv_files/statewide-crime-2.csv\")\n", "#dta = dta.set_index(\"State\", inplace=True).dropna()\n", "#dta.rename(columns={\"VR\" : \"crime\",\n", "# \"MR\" : \"murder\",\n", "# \"M\" : \"pctmetro\",\n", "# \"W\" : \"pctwhite\",\n", "# \"H\" : \"pcths\",\n", "# \"P\" : \"poverty\",\n", "# \"S\" : \"single\"\n", "# }, inplace=True)\n", "#\n", "#crime_model = ols(\"murder ~ pctmetro + poverty + pcths + single\", data=dta).fit()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/numpy/lib/npyio.py:2279: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n", " output = genfromtxt(fname, **kwargs)\n" ] } ], "source": [ "dta = sm.datasets.statecrime.load_pandas().data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: murder R-squared: 0.813\n", "Model: OLS Adj. R-squared: 0.797\n", "Method: Least Squares F-statistic: 50.08\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 3.42e-16\n", "Time: 07:44:15 Log-Likelihood: -95.050\n", "No. Observations: 51 AIC: 200.1\n", "Df Residuals: 46 BIC: 209.8\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -44.1024 12.086 -3.649 0.001 -68.430 -19.774\n", "urban 0.0109 0.015 0.707 0.483 -0.020 0.042\n", "poverty 0.4121 0.140 2.939 0.005 0.130 0.694\n", "hs_grad 0.3059 0.117 2.611 0.012 0.070 0.542\n", "single 0.6374 0.070 9.065 0.000 0.496 0.779\n", "==============================================================================\n", "Omnibus: 1.618 Durbin-Watson: 2.507\n", "Prob(Omnibus): 0.445 Jarque-Bera (JB): 0.831\n", "Skew: -0.220 Prob(JB): 0.660\n", "Kurtosis: 3.445 Cond. No. 5.80e+03\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 5.8e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "crime_model = ols(\"murder ~ urban + poverty + hs_grad + single\", data=dta).fit()\n", "print(crime_model.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Partial Regression Plots" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress_grid(crime_model, fig=fig)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12,8))\n", "fig = sm.graphics.plot_partregress(\"murder\", \"hs_grad\", [\"urban\", \"poverty\", \"single\"], ax=ax, data=dta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Leverage-Resid<sup>2</sup> Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Closely related to the influence_plot is the leverage-resid<sup>2</sup> plot." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "fig = sm.graphics.plot_leverage_resid2(crime_model, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Influence Plot" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "fig = sm.graphics.influence_plot(crime_model, ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using robust regression to correct for outliers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Part of the problem here in recreating the Stata results is that M-estimators are not robust to leverage points. MM-estimators should do better with this examples." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.formula.api import rlm" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Robust linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: murder No. Observations: 51\n", "Model: RLM Df Residuals: 46\n", "Method: IRLS Df Model: 4\n", "Norm: TukeyBiweight \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Fri, 12 Jun 2020 \n", "Time: 07:44:35 \n", "No. Iterations: 50 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -4.2986 9.494 -0.453 0.651 -22.907 14.310\n", "urban 0.0029 0.012 0.241 0.809 -0.021 0.027\n", "poverty 0.2753 0.110 2.499 0.012 0.059 0.491\n", "hs_grad -0.0302 0.092 -0.328 0.743 -0.211 0.150\n", "single 0.2902 0.055 5.253 0.000 0.182 0.398\n", "==============================================================================\n", "\n", "If the model instance has been used for another fit with different fit\n", "parameters, then the fit options might not be the correct ones anymore .\n" ] } ], "source": [ "rob_crime_model = rlm(\"murder ~ urban + poverty + hs_grad + single\", data=dta, \n", " M=sm.robust.norms.TukeyBiweight(3)).fit(conv=\"weights\")\n", "print(rob_crime_model.summary())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#rob_crime_model = rlm(\"murder ~ pctmetro + poverty + pcths + single\", data=dta, M=sm.robust.norms.TukeyBiweight()).fit(conv=\"weights\")\n", "#print(rob_crime_model.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There isn't yet an influence diagnostics method as part of RLM, but we can recreate them. (This depends on the status of [issue #888](https://github.com/statsmodels/statsmodels/issues/808))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "weights = rob_crime_model.weights\n", "idx = weights > 0\n", "X = rob_crime_model.model.exog[idx.values]\n", "ww = weights[idx] / weights[idx].mean()\n", "hat_matrix_diag = ww*(X*np.linalg.pinv(X).T).sum(1)\n", "resid = rob_crime_model.resid\n", "resid2 = resid**2\n", "resid2 /= resid2.sum()\n", "nobs = int(idx.sum())\n", "hm = hat_matrix_diag.mean()\n", "rm = resid2.mean()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iIiJyXavQaxJM01wKLD2v7flz3ncsZbnvgaYVWVt56dU8SKFARERERG4oFTncSERERERErkMKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSBARERERETsKCSIiIiIiYkchQURERERE7CgkiIiIiIiIHYUEERERERGxo5AgIiIiIiJ2FBJERERERMSOQoKIiIiIiNhRSLgCoaGhLF++vLLLKFcvvfQSQ4YMuSrLl3VbIiIiIlKxFBLKWVJSElFRUQA8/PDD/O1vf7PmFRYWUq1atRLb1qxZc8XbnD59Oh06dCh1/iOPPMKgQYMuaN+yZQsuLi5kZ2fz3HPPMXXq1Cuu4XKWv5y+8fHxTJ8+/YrrEhEREZHLp5BQgSIiIkhOTramN2zYQO3atVm1apVdG0CLFi0qrI64uDgWLFjA8ePH7dpnzpxJ9+7dqV69+kWXLyoqqrDaREREROTao5BwhdavX0/jxo3x8fFh8ODBnDx58oI+ERER/PLLL2RlZQGwatUq+vfvz/Hjx+3a2rZti7OzMwBr1qyhXbt2eHt706xZM5KSkqz1TZ8+nbp16+Lh4UGdOnWYPXs2v/zyC4888gg//PAD7u7ueHt7X1BH27ZtCQoK4rPPPrPaTp8+zccff2ydYUhISCA2NhaAPXv2YBgGH3zwAbVr1+aOO+4AYMaMGYSEhODr68vYsWPthl2VtPxHH31E7dq18fPzY/z48da2z+0L0LdvXwICAvDy8iIiIoJt27Zd5l9DRERERMqTQsIVmj17NomJiezatYvU1FTGjRsHQFRUlPXD3mazERISYp05SE5OJjw8nHbt2tm1RUREAJCRkUG3bt0YPXo02dnZTJo0iT59+nDo0CGOHz/OsGHDWLZsGXl5eXz//ffceuutNGrUiHfffZe2bdty7NgxcnJySqx30KBBzJgxw5pevnw5hYWFdO3atdR9XLlyJb/88guJiYls376dRx99lNmzZ7N//35yc3PJyMi46Ge0evVqUlJSWLFiBS+++CK//PJLif3uvvtuduzYwcGDB/nLX/7CgAEDrHnTp08nPj7+otsRERERkfKlkHCFhg4dis1mo3r16owaNYo5c+aU2C8yMpLk5GSKi4tZt24dbdq0ITw83Gr77rvviIyMBGDWrFl07dqVrl274uDgwF133UXLli1ZunQpAA4ODvz888/k5+dTq1YtmjRpcsn1Dhw4kJUrV5Keng6cOSsQExNjncEoSUJCAtWqVcPNzY1PP/2UHj160KFDB6pUqcKLL76IYRgX3eaYMWNwc3OjWbNmNGvWjC1btpTY74EHHsDDwwMXFxcSEhLYsmULubm5l7xvIiIiIlK+FBKukM1ms96HhISQmZlZYr+z1yVs3bqVunXrUrVqVTp06GC15efn07p1awD27t3L/Pnz8fb2tl6rV69m//79VKtWjXnz5vHuu+9Sq1YtunXrxq+//nrJ9dauXZuIiAhmzZrFsWPHWLhwYYkXM5e2j5mZmXbTVatWxdfX96LLBwQE2PU/duzYBX1Onz7NiBEjCAsLw9PTk9DQUABrOJaIiIiIXH0KCVdo37591vu0tDQCAwNL7BcREcGWLVtYsmQJ4eHhADRp0oR9+/axZMkSWrVqhaurK3DmR/nAgQPJycmxXsePH2fEiBEAdO7cma+//pr9+/fTsGFDHnzwQYA/PKJ/VlxcHDNnzuSzzz6jTp06f3ix9LnrrVWrlnUWAiA/P5/Dhw9f0nYv5uOPP2bRokUsX76c3Nxc9uzZA4BpmmVet4iIiIhcGYWEKzRlyhTS09PJzs5m/PjxREdHl9ivXr161KxZkzfeeMMKCYZh0Lp1a9544w3regSA2NhYFi9eTGJiIqdPn+bkyZMkJSWRnp7OgQMHWLRoEcePH8fFxQV3d3ccHM78+WrWrEl6ejoFBQUXrblPnz6kpaUxZswY4uLiLmt/77vvPhYvXsz3339PQUEBCQkJ5fJDPi8vDxcXF3x9fTlx4gTPPfdcmdcpIiIiImWjkHCFYmJi6NSpE3Xr1iUsLIzRo0eX2jciIoJDhw7Rvn17qy08PJyDBw/ahQSbzcaiRYt46aWXqFGjBjabjYkTJ1JcXExxcTH/93//R2BgINWrV2flypW88847ANxxxx00adKEgIAA/Pz8Sq2jWrVq9OnTh/T0dLuLgy9FkyZN+Ne//kX//v2pVasW7u7u+Pv74+LiclnrOd+gQYMICQkhKCiIxo0b06ZNmzKtT0RERETKzrhRhnW0bNnSPPvMAal4x44dw9vbmx07dlCnTp3KLkfkT+PswxrPvT2yiIhcXYZhbDRNs2Vl11GRnCq7gOvZwk0ZTExMITMnn0BvN4Z3bkCv5kGVXVaFWbx4MXfeeSemafLMM8/QtGlT60JjEREREblxVOhwI8MwuhiGkWIYxk7DMEaUMP8pwzC2G4bxk2EYKwzDCDlnXpxhGDt+f13eAPqrYOGmDEYu2EpGTj4mkJGTz8gFW1m46eLPDrieLVq0iMDAQAIDA9mxYwdz58695IumRUREROT6UWEhwTAMR2AKcDfQGLjfMIzG53XbBLQ0TfMW4FPg1d+XrQ6MAVoDtwFjDMPwqahar8TExBTyC0/bteUXnmZiYkolVVTxpk6dSk5ODrm5uaxYsYIGDRpUdkkiIiIiUgEq8kzCbcBO0zR3m6ZZAMwFep7bwTTNb03TPPH75Bog+Pf3nYGvTdPMNk3zCPA10KUCa71smTn5l9UuIiIiInK9qMiQEATsO2c6/fe20vwVWHY5yxqG8ZBhGBsMw9hw6NChMpZ7eQK93S6rXURERETkenFN3ALVMIxYoCUw8XKWM03zfdM0W5qm2bJGjRoVU1wphndugJuzo12bm7MjwztrCI6IiIiIXN8qMiRkALZzpoN/b7NjGEZHYBRwj2mapy5n2crUq3kQL/duSpC3GwYQ5O3Gy72b3tB3NxIRERGRP4eKvAXqeqC+YRh1OPMDvz8Qc24HwzCaA+8BXUzTPHjOrETgpXMuVu4EjKzAWq9Ir+ZBCgUiIiIicsOpsJBgmmaRYRhDOfOD3xH40DTNbYZhvAhsME3zc84ML3IH5v9+K8000zTvMU0z2zCMsZwJGgAvmqaZXVG1ioiIiIjI/1Tow9RM01wKLD2v7flz3ne8yLIfAh9WXHUiIiIiIlKSa+LCZRERERERuXYoJIiIiIiIiB2FBBERERERsaOQICIiIiIidhQSRERERETEjkKCiIiIiIjYUUgQERERERE7CgkiIiIiImJHIUFEREREROwoJIiIiIiIiB2FBBERERERsaOQcIMJDQ1l+fLllV3GFXN3d2f37t2VXYaIiIjIn5pCwp9EUlISUVFRADz88MP87W9/s+YVFhZSrVq1EtvWrFlzVes8duwYdevWLfN6pk+fTnx8fNkLEhEREfkTUkj4E4qIiCA5Odma3rBhA7Vr12bVqlV2bQAtWrS46vWJiIiISOVSSLgBrV+/nsaNG+Pj48PgwYM5efKk3fyIiAh++eUXsrKyAFi1ahX9+/fn+PHjdm1t27bF2dmZbt268a9//ctuHbfccgv/+c9/APj+++9p1aoVXl5etGrViu+//97qFxUVxejRo2nXrh3u7u706NGDw4cPM2DAADw9PWnVqhV79uyx+huGwc6dOwGIj4/nscceo1u3bnh4eNC6dWt27dpl9f3qq69o0KABXl5ePProo0RGRjJ16tTy+yBFRERE/qQUEm5As2fPJjExkV27dpGamsq4ceOIiooiKSkJAJvNRkhIiHXmIDk5mfDwcNq1a2fXFhERAUBcXByzZs2y1r9lyxYyMjLo1q0b2dnZdOvWjWHDhnH48GGeeuopunXrxuHDh63+c+fOZebMmWRkZLBr1y7atm3L4MGDyc7OplGjRrzwwgul7svcuXMZM2YMR44coV69eowaNQqArKws7rvvPl5++WUOHz5MgwYN7MJJfHw806dPL5fPU0REROTPRiHhBjR06FBsNhvVq1dn1KhRzJkz54I+kZGRJCcnU1xczLp162jTpg3h4eFW23fffUdkZCQA99xzD6mpqezYsQOAmTNnEh0dTZUqVViyZAn169dn4MCBODk5cf/999OwYUMWL15sbWvw4MGEhYXh5eXF3XffTVhYGB07dsTJyYm+ffuyadOmUvfl3nvv5bbbbsPJyYkBAwawefNmAJYuXUqTJk3o3bs3Tk5ODBs2jICAgPL8GEVERET+tBQSbkA2m816HxISQmZm5gV9zl6XsHXrVurWrUvVqlXp0KGD1Zafn0/r1q0BcHV1JTo6mlmzZlFcXMycOXMYOHAgAJmZmYSEhNitOyQkhIyMDGu6Zs2a1ns3N7cLpo8dO1bqvpz7w79q1apW38zMTLv9NAyD4ODgi38wIiIiInJJFBJuQPv27bPep6WlERgYeEGfiIgItmzZwpIlSwgPDwegSZMm7Nu3jyVLltCqVStcXV2t/nFxccyePZsVK1ZQtWpV2rZtC0BgYCB79+61W3daWhpBQUEVsWuWWrVqkZ6ebk2bpmk3LSIiIiJXTiHhBjRlyhTS09PJzs5m/PjxREdHX9CnXr161KxZkzfeeMMKCYZh0Lp1a9544w3reoSz2rZti4ODA08//bR1FgGga9eupKam8vHHH1NUVMS8efPYvn073bt3r9B97NatG1u3bmXhwoUUFRUxZcoUfvvttwrdpoiIiMifhULCDSgmJoZOnTpRt25dwsLCGD16dIn9IiIiOHToEO3bt7fawsPDOXjw4AUhAWDQoEFs3bqV2NhYq83X15cvvviC1157DV9fX1599VW++OIL/Pz8yn/HzuHn58f8+fN59tln8fX1Zfv27bRs2RIXF5cK3a6IiIjIn4FhmmZl11AuWrZsaZ69t79UjBkzZvD++++zevXqyi7lAsXFxQQHBzN79mxuv/32yi5HpMKcfSji2buViYjI1WcYxkbTNFtWdh0VyamyC5CKsXBTBhMTU8jMySfQ243hnRvQq/mVXydw4sQJ3n77bR599NFyrLJsEhMTad26NW5ubkycOBHTNGnTpk1llyUiIiJy3dNwoxvQwk0ZjFywlYycfEwgIyefkQu2snBTxh8uW5LExERq1KhBzZo1iYmJKd9iy+CHH34gLCwMPz8/Fi9ezMKFC3Fzc6vsskRERESuexpudANqP+EbMnLyL2gP8nbjuxF3VEJFIlJeNNxIRKTy/RmGG+lMwg0os4SAcLF2EREREZFzKSTcgAK9Sx5yU1q7iIiIiMi5FBJuQMM7N8DN2dGuzc3ZkeGdG1RSRSIiIiJyPdHdjW5AZ+9i1P+OFnh3fpywW9uW+e5GIiIiIvLnoTMJN6hezYMI8HJl1pDWfDfiDruAkJSURFRUFGlpabi7u1svwzCoVq2aNb1q1aqrXveXX35Jly5drvp2RUREROR/dCbhT6x27docO3bMmjYMgy1btlCvXr1KrEpEREREKpvOJNzg1q9fT+PGjfHx8WHw4MGcPHnyspbPz8/n73//OzabjYCAAB5//HFOnToFwAsvvEBERATFxcUAvP7669x6660UFBRQVFREnz59qFmzJt7e3tx+++2kpKRY6120aBENGzZFjSUlAAAgAElEQVTEw8MDm83Gm2++WX47LSIiIiJlopBwg5s9ezaJiYns2rWL1NRUxo0bR1RU1CXfY/2pp54iPT2drVu3kpKSQmpqKhMmTABg1KhRnDp1ildffZVt27bx4osvMnv2bKpUqQJAz5492bVrF7/99hsNGzYkLi7OWu8DDzzAjBkzyMvLY/PmzYSHhwPQpUsXvvzyy/L9EERERETksigk3OCGDh2KzWajevXqjBo1ijlz5lzyskVFRXzwwQe88cYbeHt74+XlxYgRI5g7dy4ATk5OzJo1i1deeYXevXvz/PPP06RJE2veoEGDcHd3x9XVlTFjxrBu3TrrTIajoyPbtm0jLy8PX19fmjdvXv47LyIiIiJXRCHhBmez2az3ISEhZGZmXvKymZmZFBYW0qRJE7y9vfH29qZXr14cPHjQ6lO/fn3atWvH/v37efjhh632oqIinn76aerWrYunpycNGzbENE0OHz4MnBlu9Nlnn1G7dm3uuOMO1q9fXw57KyIiIiLlQSHhBrdv3z7rfVpaGoGBgZe8bK1atXBycsLT05NPP/2UnJwccnNzrR/6AAsWLGDr1q20bduWkSNHWu3Tpk3j66+/5ttvvyU3N5dff/0VANM0AWjbti1ffPEFBw4coFOnTsTExJR1V0VERESknCgk3OCmTJlCeno62dnZjB8/nujo6Ete1tnZmQceeIDs7Gxyc3MxTZN9+/bx9ddfA2cCQv/+/Zk2bRq7d+9m2rRprFixAoC8vDxcXV2ZN28eLVq0YPTo0dZ6jx8/zty5czl69CjOzs54eHjg4FC5/xSjoqKYOnWqXZthGJVUjYiIiEjlUki4wcXExNCpUyfq1q1LWFiY3Y/1SzF58mQcHR159NFH8fLyokuXLuzcuROAV155BX9/f+68804eeOABateuzeDBg8nJyeGvf/0rNWrUYOTIkezdu5cOHTrYrffDDz8kJCQELy8vZsyYwYwZM+zmFxUVlW3HRUREROSKKSSUQWhoKMuXL6/sMkq1Z88eRo4cyfbt28nJyeGjjz6iatWqpfY3TfOCZyS4ubnh4+PD3//+d4KDg8nMzLQuQH7llVes/gMHDuTXX39l1apV1kXOEydOxNHRkZSUFOLj48nJyWHMmDHUq1ePbdu28dhjj3HkyBHWrl1LXFwc9erV48knn8TX15eEhASmT59O+/btGTJkCIZhULduXb7//numT5+OzWbD39+fjz76yKo1NzeXQYMGUaNGDUJCQhg3bpx1e9bp06fToUMHnnnmGXx8fKhTpw7Lli0DztyladWqVQwdOhR3d3eGDh1a3n8KuUrK+zt599132/0bu1yPPPIIY8eOveLlX3rpJYYMGXLFy5+rSZMml3xXMxEREUzTvCFeLVq0MK+2kJAQ8+uvvy5x3rfffmtGRkZa04B58803m6dPn7baRo0aZcbFxVVojYD59sJVZruXV5ih//jCbPfyCjP6oSfNAQMGXPI6QkJCzCZNmphpaWnm4cOHzXbt2pmjRo26oF/Hjh3NsWPHWtMjRowwe/bsaU336tXLfOihh8xjx46ZBw4cMFu1amW+++67pmmaZoMGDUwHBwfzzTffNAsLC80TJ06Y06ZNMx0dHc0PP/zQLCoqMkeNGmXabDbz0UcfNU+ePGkmJiaa7u7uZl5enmmapjlw4EDznnvuMY8ePWr+97//NevXr29OnTrVnDZtmtm+fXvTycnJfP/9982ioiLz7bffNmvVqmUWFxebpmmakZGR5r///e8r+ozl2lHad7Kk72ONGjXMwsJCq62goMCsUaOGeeZ/i9euyMhIu325EmPGjDHHjBlTLvWIiPwZARvMa+D3b0W+dCbhKsrMzLRuH3o1TfoqlYycfEwgIyef5b8cZF/2ictax6XcSjUuLo6ZM2cCUFxczOzZs61nIxw4cIClS5cyefJkqlWrhr+/P08++aTd5+Ht7c3jjz+Ok5MTbm5uANSpU4fBgwfj6OhIdHQ0+/bt4/nnn8fFxYVOnTpRpUoVUlNTOX36NHPnzuXll1/Gw8OD0NBQnn76aaseOHN3pwcffBBHR0fi4uLYv38/Bw4cuNyPU24QPj4+1tkkgGXLluHj41OJFYmIiFw7FBLK6HKeaPzss88yZsyYUsfbr1mzhnbt2uHt7U2zZs2soQHffvstTZs2tfrdddddtGrVypoODw9n4cKFpW73ZNFpu+mi4mK27z9qTT/xxBPYbDY8PT1p0aIFq1atsuYlJCRw6NAh5s2bh4eHB02bNsU0TdLS0vD398dms/HVV18B0Lt3b3bt2sWgQYNo3Lgx6enpfPjhh2RnZ7N3714KCwvx8vLCwcEBwzCIjY21uyVr1apVad++PR4eHnTq1Im8vDxq1qzJnj17MAwDZ2dnAKKjoxk1ahTt27cnOzub1NRUdu/eTWFhIbfffjtBQUGMHj2a4OBgMjIyrPUHBATYbQvg2LFjpX5ucn261O/kwIED7a6FmTFjBoMGDbLrc+4F7Tt37iQyMhIvLy/8/PysmwCYpsmTTz6Jv78/np6eNG3alJ9//hmA+Ph46zqgrKwsunfvjre3N9WrVyc8PNwaDvfKK68QFBSEh4cHDRo0sG4AkJCQQGxsLID1PcjMzOSHH36gVq1aTJo0yao1ISGB++67j+joaDw8PPjLX/7Cli1brPnX+vBIERG5tigkXKHQ0FBOnjxZ4hONgRKfaty7d288PT2ZPn36BevLyMigW7dujB49muzsbCZNmkSfPn04dOgQbdq0YceOHWRlZVFYWMhPP/1EZmYmeXl55Ofns2HDBuuJxZcqv+B/waFVq1Zs3ryZ7OxsYmJi6Nu3r90PqxMnTtC0aVOOHDlC8+bNeeCBB/Dw8CAjI4Pnn3/eej5C1apVqVGjBp999hkNGzbkwQcfpEqVKgwbNgybzYaTkxN33XUXx44do6ioiHXr1tk9H+HQoUNMmzaNgwcPUlBQcNEnL8+cOZP333+foKAgAgICGD58OA4ODixZsoRNmzbx1VdfMWfOHIKCgoiPj//Dcd26k9GN41KfMt6rVy+Sk5PJycnhyJEjrFq1ip49e5a63n/+85906tSJI0eOkJ6ezuOPPw7AV199RXJyMqmpqeTm5vLJJ5/g6+t7wfKvvfYawcHBHDp0iAMHDvDSSy9hGAYpKSm89dZbrF+/nry8PBITEwkNDS21jpycHG677Ta++uorXnnlFbsf/osWLaJv377Wd7lXr14UFhZesI6EhAQSEhIu/kGKiMifmkJCGZU0DCcpKYmoqCirz9kfoKdPn2bs2LGMHTuW48eP8/rrr1sXRc6aNYuuXbvStWtXHBwcuOuuu2jZsiVLly7Fzc2NVq1akZyczMaNG2nWrBnt27fnu+++Y82aNdSvX58WLVqUepRw//QnSJscbb1y13yKWxVHa35sbCy+vr44OTnx9NNPc+rUKVJSUqz5rq6ufPvtt/z222906dKFrKwsHnroIZydnenfvz979uzh7bffBqBmzZoUFxfzzTffMGTIEMaOHcsnn3yCv78/TZo04ccff2Tz5s0YhoG3tzebNm2ytlOzZk1uuukm3Nzc6NevH2lpaaV+7vHx8dbTnY8ePcqyZcvo168f48ePx83NjdjYWBYsWGAdhf0jNWvWZPfu3ZfUV65tl/qUcVdXV3r06MG8efOYN28e99xzD66urqWu19nZmb1795KZmYmrq6t1xy5nZ2fy8vL49ddfMU2TRo0aUatWrRKX379/P3v37sXZ2Znw8HAMw8DR0ZFTp06xfft2CgsLCQ0NJSwsrNQ6QkNDcXR0pGnTpgwePNhu/1q0aMF9992Hs7MzTz31FCdPnmTNmjWX+tGJiIhYFBLK6HKeaLxy5Uq6du1KcHAwTz75pN0Pkr179zJ//nzrycbe3t6sXr2a/fv3AxAZGUlSUhLJyclERkYSFRXFypUrWblyJZGRkRetsc6Qt6j993nWy7ddPxrX8rTmT5o0iUaNGuHl5YW3tze5ublkZWVZ8x0dHa1bqT700EO4ubnx/PPPA1jXDpw98+Dl5UW1atUIDg6mVatWhISEUFhYSFZWFl9//TXBwcFERkbi5OREu3bt7B72VqVKFet91apVLzp069zP/bfffqOwsJClS5eyZMkSvLy8+Pvf/467uzsPPPDART+bs5544gk+/fRTfHx8GDZs2CUtI9emy/lODho0yLoF7/lDjc736quvYpomt912G02aNOHDDz8E4I477mDo0KE89thj+Pv789BDD3H06NELlh8+fDj16tWzbkk8YcIEAOrVq8fkyZNJSEjA39+f/v37X7RmFxeXUvfv3H13cHCw7kgmIiJyuRQSyuDUqVMMGTLEGvu8c+fOiz7R+Ox1A+PHj2fmzJk0bNjQmmez2ejTpw8RERE4ODjg5+fH5MmTGTFiBHAmRMyYMYM333yT8ePH89prr7FkyRJWrlzJli1bSEtLo0ePHri7u/Pqq68C0LdvXwAypw/jyLyRFB7aS5C3Gx0b+ZOesoXHHnuMtm3b8uyzz+Lk5MSGDRvIycnBy8uLt956C5vNxssvv0yVKlXo0KED27dvZ+HChfj5+ZV6K1XDMBgyZAjbt28Hzjzl2dnZGT8/P/z8/Fi/fj2FhYXs3r0bX19fTp06BZy5XuAf//iH3bpq1qzJ6tWrrel69epZT2w+e3YmPT2dHj164OLiwuHDhykoKKC4uJji4mIOHjxoPaQtPj7ebl1gf8vXtm3bkpqaypEjR3jzzTdL/RvKte9ynjIeHh5uXcB+/rM8zhcQEMC///1vMjMzee+993j00UetZ4YMGzaMjRs3sn37dlJTU5k4ceIFy3t4ePDaa6+xe/duPv/8c/7v//7PuvYgJiaG1atXs3fvXgzDuOC7cK6z35mS9u/cfS8uLiY9Pf2ynrIuIiJylkJCGRw/fhwvLy+Sk5PZtm0bQ4cOJTo6usTxzwDr1q0jJyeHZs2acfr0abZt22bNi42NZf78+RQXF7Nv3z5mz57NM888w7x584AzISI3N5djx45x+PBh+vXrx6+//sratWv59NNPqV27NosXL+bYsWM8++yzwJl7vANsXL+OwT3voMbG9/luxB00/P0swty5c+nXrx8BAQHUr1+f5557jhdffJGjR4/SoEEDNm/ezD/+8Q/q1KlzwXUK5+vXr5/1ftasWWzfvp0TJ07w/PPPc9999+Ho6Mi3337L1q1bOX36NJ6enjg7O5fLk5Zr1apFp06dePrppzl69CjFxcXs2rWLlStXlnndcv25nKeMG4bB4sWL+fzzz//wupT58+eTnp4OnLkzkmEYODg4sH79etauXUthYSHVqlXD1dW1xH/XX3zxBTt37sQ0Tby8vHB0dMTBwYGUlBS++eYbTp06haurK25ubhf9Xuzdu9f6/8e0adPs9m/jxo0sWLCAoqIiJk+ejIuLC23atPmjj0xEROQCCgll4OHhQVxcHNHR0dYDyy72ROM777zTGv989913k5ubaze/qKiInJwcbDYb3bt3p0aNGsyfPx84MxTH09OT5s2b4+bmxsCBAzl9+jQhISH4+/uXuL2zQ22qVKlCQkICW7Zssdvmvffey7Bhw+jSpQtffvkl//nPf3B1dcVms9GxY0d8fX1xcHCgUaNGnDp1incWreKJuZtIP5JP+wnfsHBTRonbHThwIPHx8QQEBHDy5EnryPxvv/3Gfffdh6enJ40aNSIyMpKBAwde+gd+ETNmzKCgoMC6q819991nDdWSP5fLfcp4kyZNrOtbLmb9+vW0bt0ad3d37rnnHt544w3q1q3L0aNHefDBB/Hx8SEkJARfX1+GDx9+wfI7duygY8eOuLu707ZtWx599FFuv/12Tp06xYgRI/Dz8yMgIICDBw/y8ssvl1qHl5cX69at48477+SZZ56hU6dO1ryePXsyb948fHx8mDlzJgsWLLDuCiYiInI5jLPDN653LVu2NDds2HDVthcaGsqUKVPo1q0bANu2baNly5bk5+df0NcwDHbs2EFmZiYjR47ENE1eeeUVatWqRf369TFNk7Vr19K9e3cOHTpkLffuu+/y2Wef8fXXX5OQkMDOnTuZNWsWcOZ2iHXq1KGwsBAnJydCQ0OZOnUqHTt2BM5cJD1q1Cjmz5/PoUOHcHBwIDc3l507dxIWFkZ8fDzBwcHW3ZiSkpKIjY21jpROmjSJDz74gMzMTAzD4OjRowTHjMch+BarPjdnR17u3ZRezYOstqioKGJjY8vtKbEicsbZ73xERASGYVxwtvL8/0eIiEjFMQxjo2maLSu7joqkMwllcHb878JNGfSd9DlFrt4XPcJ+sfHPgYGBZGdnk5eXZ7WlpaURFBR0/mpKdP5QiY8//phFixaxfPlycnNz2bNnDwCXEgpXrVrFq6++yieffMKRI0fIycnBwaUap4qK7frlF55mYmJKKWsRqTwLN2XQfsI31Bmx5KLfSRERESmZU2UXcD2bMmUKTqEteHX5HtK+nkXVhuFk5OQzcsFWALsj7PC/8c9n35/LZrPRrl07Ro4cyaRJk0hNTeWDDz5g9uzZl1TL+bfwzMvLw8XFBV9fX06cOMFzzz13yfuVl5eHk5MTNWrUoKioiAkTJnD6VMlPaM7MufDMiUhlWrgpg5ELtpJfeOZZIBf7ToqIiEjJdCahDGJiYhgWdx873xqMk08AXu3OXEB4sSPsFxv/PGfOHPbs2UNgYCD33nsvL7zwgjV86I+MHDmScePG4e3tzaRJkxg0aBAhISEEBQXRuHHjy7p4sXPnznTp0oWbbrqJkJAQXF1dcfGqUWLfQG83u+mkpCQNNZJKNTExxQoIZ90IZ71CQ0MxTbPUC6wTEhI01EhERMqNrkkoozojllDSJ2gA/53Q7WqXU2HOPzoLJV+TIFLZbvTv5NkHNZZ0BzUREbk6dE2C/KHzj6T/Ufv1qlfzIF7u3ZQgbzcMIMjbTQFBrkl/lu+kiIhIRdI1CWV0e8MazF6TZnfk0s3ZkeGdG1RaTRWlV/MghQK55g3v3KDEs1434ndSRESkoigklMHCTRl8tjHDLiAYQJ8W+jEtUlnOfvcmJqaQmZNPoLcbwzs30HdSRETkMigklEFJF0iawLe/Hip5ARG5KnTWS0REpGx0TUIZlHb7T90WVERERESuZwoJZaALJEVERETkRqSQUAbDOzfAzdnRrk0XSIqIiIjI9U7XJJSBLpAUERERkRuRQkIZ6QJJEREREbnRaLiRiIiIiIjYUUgQERERERE7CgkiIiIiImJHIUGuqtDQUJYvX15u67v77rv56KOPyrTdVatW0aCB7kglIiIicpZCglwTkpKSiIqKsqYNw8Df35+ioiKrrbCwEH9/fwzDsNqWLVtGXFxcmbYdHh5OSkpKmdZxKfbs2UNoaGiFb0dERESkrCo0JBiG0cUwjBTDMHYahjGihPkRhmH8aBhGkWEY950377RhGJt/f31ekXWWh/I+Qi7g4+PDsmXLrOlly5bh4+NTiRWJiIiI/DlUWEgwDMMRmALcDTQG7jcMo/F53dKAeODjElaRb5rmrb+/7qmoOq+Gko6SN23alOLiYqtt9OjRxMfHV2gd10qQWb9+PY0bN8bT0xN3d3dOnjxZYr+BAwcyY8YMa3rGjBkMGjTIrk9UVBRTp061pv/973/TqFEjPDw8aNy4MT/++KM1b/Pmzdxyyy14eXkRHR1tbTcpKYng4GCr348//kjz5s3x8PCgb9++REdHM3r0aACOHDlC9+7dqVGjBj4+PnTv3p309HS7ev75z3/Svn17PDw86NSpE1lZWWX4tERERESuvoo8k3AbsNM0zd2maRYAc4Ge53YwTXOPaZo/AcUlreBGlpmZydy5cyu7jEty7pCf8jB79mwSExNJTk6mqKiIcePGERUVRVJSkl2/Xr16kZycTE5ODkeOHGHVqlX07Nmz5JUC8+fPJyEhgRkzZnD06FE+//xzfH19rfmffPIJX375Jf/973/56aefmD59+gXrKCgo4N577yU+Pp7s7Gzuv/9+/vOf/1jzi4uLGTx4MHv37iUtLQ03NzeGDh1qt46PP/6YadOmcfDgQQoKCpg0aRJwJqTt2bPn8j8wERERkausIkNCELDvnOn039sulathGBsMw1hjGEavkjoYhvHQ7302HDp0qCy1louzR8h9fHwYPHhwqUfIAZ599lnGjBlT6g/wNWvW0K5dO7y9vWnWrJn1A/rbb7+ladOmVr+77rqLVq1aWdPh4eEsXLjwkur98MMPadSoET4+PnTu3Jm9e/da8wzDYMqUKdSvX5/69etjmiZPPvkk/v7+eHp60rRpU37++WcATp06xTPPPEPt2rWpWbMmjzzyCPn5+QDcfPPNLF682FqvaZrs2bOHrKwsvL298fLyYs6cOSXW5+rqSo8ePZg3bx7z5s3jnnvuwdXVtdT9mTp1Ks8++yytWrXCMAzq1atHSEiINX/YsGEEBgZSvXp1evTowebNmy9Yx5o1aygqKmLYsGE4OzvTu3dvbrvtNmu+r68vffr0oWrVqnh4eDBq1ChWrlxpt47Bgwdz00034ebmRr9+/UrcjoiIiMi17Fq+cDnENM2WQAww2TCMsPM7mKb5vmmaLU3TbFmjRo2rX+F5zh4h37VrF6mpqYwbNw6gxKPkvXv3xtPTs8Sj2RkZGXTr1o3Ro0eTnZ3NpEmT6NOnD4cOHaJNmzbs2LGDrKwsCgsL+emnn8jMzCQvL4/8/Hw2bNhAeHj4H9a6aNEiXnrpJRYsWMChQ4cIDw/n/vvvt+uzcOFC1q5dy/bt2/nqq69ITk4mNTWV3NxcPvnkE+so/YgRI0hNTWXz5s3s3LmTjIwMXnzxRQAGDRrErFmzrHXm5+fj5+dH8+bNCQ0N5ZtvviEzM7PUOgcNGsSMGTNKHGp0vn379hEWdsE/E0tAQID1vmrVqhw7duyCPpmZmQQFBdldHG2z2az3J06c4OGHHyYkJARPT08iIiLIycnh9OnTl7UdERERkWtZRYaEDMB2znTw722XxDTNjN//uxtIApqXZ3EVYejQodhsNqpXr86oUaNKPUIOZ47Ujx07lrFjx1JQUGA3b9asWXTt2pWuXbvi4ODAXXfdRcuWLVm6dClubm60atWK5ORkNm7cSLNmzWjfvj3fffcda9asoX79+nZDbErz7rvvMnLkSBo1aoSTkxPPPfccmzdvtjubMHLkSKpXr46bmxvOzs7k5eXx66+/YpomjRo1olatWpimyfvvv8/rr79O9erV8fDw4LnnnrOGUsXGxrJ06VKOHj0KwPHjx/nLX/5ibSMtLY3AwMBS6wwPD2f//v0cOHCADh06XHSfbDYbu3bt+sN9v5hatWqRkZGBaZpW2759/zsh9tprr5GSksLatWs5evQoycnJAHb9RURERK53FRkS1gP1DcOoYxhGFaA/cEl3KTIMw8cwDJff3/sB7YHtFVZpOTn3iHNISMhFj5ADdO3aleDgYN577z279r179zJ//ny8vb2t1+rVq9m/fz8AkZGRJCUlkZycTGRkJFFRUaxcuZKVK1cSGRl5SbXu3buXJ554wlp/9erVMU2TjIz/5bhz9+eOO+5g6NChPPbYY/j7+/PQQw9x9OhRDh06xIkTJ2jRooW1ri5dunB2+FdgYCDt27fns88+Iycnh/z8fLZv3056ejrZ2dmMHz+e6OjoUus0DIPFixfz+eef2x3dL8mQIUOYNGkSGzduxDRNdu7caRd6LkXbtm1xdHTkrbfeoqioiEWLFrFu3Tprfl5eHm5ubnh7e5Odnc0LL7xwWesXERERuR5UWEgwTbMIGAokAr8An5imuc0wjBcNw7gHwDCMVoZhpAN9gfcMw9j2++KNgA2GYWwBvgUmmKZ5zYeEc484/9ER8rPGjx/PSy+9xIkTJ6w2m83GwIEDycnJsV7Hjx9nxIgzd5E9PyRERkZedkiw2Wy89957dtvIz8+nXbt2Vp/zf5QPGzaMjRs3sn37dlJTU5k4cSJ+fn64ubmxbds2az25ubl2Q2zi4uKYNWsW8+fPx8XFhbi4ODp16kTdunUJCwuz7hxUmiZNmtCkSZM/3Ke+ffsyatQoYmJi8PDwoFevXmRnZ1/S53FWlSpVWLBgAR988AHe3t7MmjWL7t274+LiAsDf/5+9+46K4nobOP6dZeksLE2R4gKiBNDYjYoFjb3FaOyaaBJT1JgYY9Rgiy3GEn01xaiJvRs7dg2i5mdJggU7GqTZAEFAOvf9gzBxBQ0aiSX3c86ew8zcmXl2Vs+ZO3Pv83z0kTpkqm7durRq1eqhji9JkiRJkvRMEEI8F5+aNWuKJ8lgMIjKlSuLmJgYkZiYKAIDA8XIkSOLbQuIixcvqsvNmjUTDg4O4o033hBCCBEdHS3Kli0rduzYIXJzc0VGRob4+eefRUxMjBBCiLS0NGFmZiacnZ1FVlaWEEIIV1dXYWlpKa5fv/7AGHfv3i2EEGL9+vUiICBARERECCGESE5OFmvWrLlvjEePHhWHDx8W2dnZIi0tTbRs2VKMGTNGCCHE4MGDRZcuXdRzx8bGih07dqj73rlzR+j1ehEQECAWL15csgv6FKlTp4748ccfn3QYkiSEEKJx48aicePGTzoMSZKk/zTgV/EU3P+W5udpnrj8zOnZs+dDPSEvNHHiRKMn3h4eHurEYmdnZzw8PJg2bZpaV8Ha2poaNWoQEBCAmZkZUDBMxmAwUKZMmQeeq/DtwKuvvsrw4cPp3r07tra2VK5c2ahw2b1u375N//79sbe3x2Aw4OjoyLBhwwD48ssv8fHxoW7dutja2tKsWTOjCsaWlpZ07tyZP/74g06dOgGwMTyOwCn78BoRQuCUfWwML/F0lVK3f/9+rl27Rm5uLosXL+bkyZPyjYEkSZIkSf8pinhOJlzWqlVL/Prrr086jPoAJkIAACAASURBVKeag4MD+/bto1q1av/6ucePH8+FCxdYtmwZG8PjGLn+FBk5f2UEsjQ14YtOVehY/WGy5JaOefPmMXr0aNLT0/H29uaLL76gbdu2TzosSQJQCzPemzFNkiRJ+vcoivKbKMjC+dzSPukAnjcbw+OYtvM88ckZuOotGdbS96m48d21axd5eXlUrFjxXz93UlISP/zwA0uXLgVg2s7zRh0EgIycPKbtPP9UXKt33nmHd95550mHIUmSJEmS9MSUeLiRoiiWiqL4lmYwz7rCJ+RxyRkIIC45g5HrTz3xoTTdu3fn3XffZf78+VhbW/+r554/fz4eHh60bt2aRo0aARCfnFFs2/utlyRJkiRJkv5dJXqToChKe2A6YAZ4KYpSDRgvhOhQmsE9a57WJ+SFNQuehP79+9O/f3+jda56S+KK6RC46i3/rbAkSZIkSZKkByjpm4RxQB0gGUAIcRzwKqWYnlnyCXnJDGvpi6WpidE6S1MThrWUL6okSZIkSZKeBiXtJOQIIVLuWfd8zHh+jO5+Eh773ZtkRB0vsv5JiYqKQlEUcnNz/7Vzenp6smfPniLrO1Z344tOVXDTW6IAbnrLp2bSsiRJkiRJklTyTsJpRVF6AiaKolRUFGUO8EspxvVMKukT8tDQUDVDCRSkJa1SpYqa4hRg1KhR9O3b95Fjud8Nekn17dsXMzMzdDodOp2OypUrM3LkSFJS7u0rPpqO1d04NKIpf0xpy6ERTY06CH379i1x+liARYsW/aNrJUmSJEmSJBkraSfhAyAAyAJWAreBj0orqGfV3U/IAZxszEr8hDw+Pv6xzB14nG8KPv30U1JTU7l58yYLFy7k8OHDBAYGkp6e/tjOIUmSJEmSJD19StRJEELcEUIECyFqCyFq/fl3ZmkH9ywqfELubm9J7wq5fNarOfb29vTr14/MzPtfsk8//ZSxY8fe9yZ/8+bNBAQEoNfrCQoK4uzZs+o2T09PvvzyS1588UWsra3p0aMH0dHRtG/fHhsbG6ZOnaq2Xb58OeXLl8fJyYlJkyaV6DtZWFhQu3ZtNm/eTGJiIgsXLgTg0qVLNG3aFEdHR5ycnOjVqxfJycnFHuPs2bN4eXmxcuVKdTkoKAi9Xk9AQACbN28GCmoULF++nKlTp2JjY0P79u0BmDJlChUqVECn0+Hv78+GDRtKFLskSZIkSZL08ErUSVAUZYuiKJvv+SxVFOVDRVEsSjvIZ9Xy5cvZuXMnly5d4sKFC0ycOBEoKIZ0byGkTp06YWtry6JFi4oc58KFC/To0YNZs2Zx8+ZN2rRpQ/v27cnOzlbbrFy5kpCQEJKTk1m5ciXly5dny5YtpKWl8emnn6rtDh48yPnz59m7dy/jx4836mz8HZ1OR/PmzTlw4AAAQghGjhxJfHw8Z8+eJSYmhnHjxhXZ7/fff6dly5bMmTOHHj16kJOTQ/v27WnRogU3btxgzpw59OrVi/Pnz/POO+/Qq1cvPv30U9LS0tiyZQsAFSpU4MCBA6SkpDB27Fh69+7N1atXgYLhScVdN0mSJEmSJOnRlHS40WUgDZj/5+c2cB2o9Oey9Ke75wIMGjQIDw8PHBwcCA4OVp+iF0dRFCZMmMCECROMbv4BVq9eTdu2bWnevDmmpqZ88sknJCYmUrNmTbXN4MGD8fDwwNLywZOkx44di6WlJVWrVqVq1aqcOHHiob6fq6srSUlJAPj4+NC8eXPMzc1xdnbm448/Zv/+/UbtDxw4QIcOHViyZAnt2rUD4PDhw6SlpTFixAjMzMxo2rQp7dq1e+D16dKlC66urmg0Grp160bFihU5evToQ8UuSZIkSZIklUxJOwnVhRA9hRBb/vz0BuoIIQYCNUoxvmeah4eH+vf169eJiopSlxVFwdraGhsbGwD69OlDamoq7u7ufP/990bHiY+Px2AwqMsajQYHBweysrKKPdeDuLi4qH9bWVmRlpb2UN8pLi4OBwcH9Tt1794dNzc3bG1t6d27NwkJCWrba9euMWfOHOrXr280UTs+Ph4PDw80mr/++RkMBuLi7l90bsmSJVSrVg29Xo9eryciIsLoXJIkSZIkSdLjU9JOgrOiKOULF/782+nPxezid5FiYmLUv2/cuIGZmZnR9hMnTqg36Z07d2bQoEFUrFiRyZMnc+fOHbWdq6srV65cUZeFECQlJWFubq6uUxTF6Nj3Lj8OaWlp7Nmzh4YNG5Kbm8tnn32GoiicOnWK27dvs2zZMoQwzow7ZMgQoqOjGTJkiNH3iYmJMcrmFB0djZubW7GxX7lyhf79+/P111+TmJhIcnIylStXLnIuSZIkSZIk6fEoaSdhKHBQUZSfFUUJBQ4AwxRFsQYWl1Zwz6pjx44RHx/PwIED6dq1K/Hx8SxbtgxnZ+f77tOxY0e+++47Vq1aha+vL4sXLyY7O5u33nqL2bNns27dOnr16kVmZiYzZszA1NQUnU6n7v/NN9/g4eGBra0tNWvWxMrKisuXL5OZmYmlpaU6RGjy5MlotVpu374NwMaNG/noo4JEVSEhIVSvXh1bW1vWrl1LWFgYAFlZWWzZsgWdToeiKEyZMoWmTZuSmprKnTt3aNu2Lba2tvTq1cvo7QYUvK3YsWMHYWFhjBgxAoCXXnoJKysrpk6dSk5ODqGhoWzZsoXu3bsDULZsWS5fvqweIz09HUVR1Ou3cOFCIiIi/tFvJEmSJEmSJN1fSbMbbQMqUpD29EPAVwgRIoRIF0LMKs0An0XLly+nbNmyfPbZZ2zduhVvb29q1KjBuXPnHrjfK6+8Qm5uLq+88gpJSUkcPHgQrVZLVFQUP/74I+vXr8fe3p4tW7bw4YcfGg3X8fX15fjx4yQlJdGzZ0/i4uKYMGECLi4ulCtXjiNHjgAQFhaGwWDg0KFDQMGk6MaNGwNgbW3NkiVLSE5OplmzZhw4cABLS0scHR0ZOnQoAI0aNeL8+fPs3LmT9957j5CQEI4fP463tze9e/cmMTGRmzdvAgVDm6pWrYper2f37t1s376d0aNHY2ZmxpYtW9i+fTtOTk4MGDCAJUuW8MILLwDw1ltvcebMGfR6PR07dsTf35+hQ4dSr149ypYty6lTpwgMDHy8P5okSZIkSZKkUko6ZENRlMqAP6BmMxJCLCmluB5arVq1xK+//vqkw8DT05MRI0bw3nvvAbBt2zY++OADLl26ZNROURQuXryIj4+P0XoXFxdmzJhBs2bNKF++PMnJyepk5JUrVzJv3jx+/vlnFi1axIIFCzh48GCxcdjb2xMaGkrVqlUZPXo0KSkpfPXVV7i7uzNy5EiuXr3KuHHjsLe3JzY2FkdHxyLH+Oijj1AUhZkzZxIVFYWXlxeXLl3C29sbgC+//JKIiAiWLl2q7tOyZUt69uzJG2+88egXUZKk+yqc33NvhjRJkiTp36Moym9CiFpPOo7SVNIUqGOBOX9+mgBTgQ6lGNcz7e5JxJF3LIiKjsVrRAiBU/axMfz+k3NzcnK4efMmDg4OXLlyhZycHMqVK6dO1n333Xe5ceNGsftOnz4dPz8/7Ozs0Ov1pKSkqBN7GzduTGhoKL///jtVqlShefPm7N+/n8OHD+Pj46N2EI4cOUKTJk1wdnbGzs6OuXPnFpkcfPd3u3LlCmvXrlXj0+v1HDx4UE1NKkmSJEmSJD2btCVs9xpQFQgXQvRTFKUssKD0wnq2FU5Y3hgex7SfDqGxcUAAcckZjFx/6r77bdq0Ca1WS506dcjOzsbc3JyEhAS02gf/TAcOHGDq1Kns3buXgIAANBoN9vb26sTe+vXrc/78eTZs2EDjxo3x9/cnOjqabdu2qUONAHr27MmgQYPYvn07FhYWfPTRR0U6CXdPKvbw8KBPnz7Mny+z4EqSJEmSJD1PSjpxOUMIkQ/kKopiC9wAvEsvrGfbN998Q2xsLJM3HOPmgVVYvdBQ3ZaRk8e0neeN2iclJbF8+XIGDhzI8OHDcXR0pFy5crRo0YKhQ4dy+/Zt8vPzuXTpUpE6BACpqalotVqcnZ3Jzc1l/Pjx6sRkKJg8XLNmTb755hu1U1C/fn3mzp1r1ElITU3FwcEBCwsLjh49yooVKx74PXv37s2WLVvYuXMneXl5ZGZmEhoaSmxs7CNdN0mSJEmSJOnpUNJOwq+KougpKJz2G/A7ICtZ3UfPnj1p0aIFv07tjdbeBbv63Yy2xydnAFC1alVsbGzw8fFhwYIFzJw5k/Hjx6vtlixZQnZ2Nv7+/tjb2/Paa68VO5SnZcuWtGrVikqVKmEwGLCwsChSN6Fx48bk5ORQp04ddTk1NZVGjRqpbb799lvGjBmDTqdj/PjxdO3a9YHf08PDg02bNjF58mScnZ3x8PBg2rRpRqlNJUmSJEmSpGfP305cVgrGl7gLIWL+XPYEbIUQJ0s9uofwpCYue3p6smDBApo1a1ZkW+CUfcT92SG4m5vekkMjmpb4HH83Sflxu9+kakmSnjw5cVmSJOnJkxOXAVHQi9h413LU09ZBeBpNnL+WE3P/KiAmhOD2r5u5+uNAfvu8He7u7nTp0oVTp+4/R+Fpt2jRIvr27fukw5AkSZIkSZIes5IONzqsKErtUo3kObIxPI55YX+Qe9ewm1t755H++xaCJ0wlJfkWFy5coGPHjoSEhJRaHEIIOfRHkiRJkiRJemgl7SQ0oaCjcElRlJOKopxSFEW+TfjTsWPH1HkD/fr148utp8jOy1O35yTFkfp7CL49RjHm3a6Ym5tjZWVFr1691CrEKSkpvP766zg7O2MwGJg4ceJ9b/B/+eUXateujZ2dHbVr1+aXX35RtwUFBREcHExgYKBadXnhwoX4+fmh0+nw9vbm+++/NzretGnTKFeuHK6urvz4449G2x4mLkmSJEmSJOn5UNIUqK1LNYpn3PLly9m5cyfW1ta0b9+es+cXoW/UB5eeLwKQeeUEJjpHUnWG+x7jgw8+ICUlhcuXL5OYmEiLFi0oV64cb731llG7pKQk2rZty+zZs+nRowdr166lbdu2REZGqvUOli5dyvbt2/H19UUIwfnz59XKz2FhYbRu3ZratWtTo0YNduzYwfTp09m7dy9eXl7079+/xHH17dtXDjeSJEmSJEl6DpXoTYIQ4grgATT98+87Jd33v2DQoEF4eHjg4OBAcHAwmefDjLbnZ6RiYu2Aq96y2P3z8vJYtWoVX3zxBTqdDk9PT4YOHWpUybhQSEgIFStWpE+fPmi1Wnr06MELL7zAli1b1DZ9+/YlICAArVaLqakpbdu2pUKFCiiKQuPGjWnRogUHDhwAYM2aNfTr14/KlStjbW3NuHHjHikuSZIkSZIk6fnxMBWXhwMj/1xlCiwrraCeNXenGzUYDIj0JCxNTdR1Gksd+em3GNbSt9j9ExISyMnJwWD4602DwWAgLq5odeb4+HijdsW1vTf96fbt26lbty4ODg7o9Xq2bdumFkmLj48vEv+jxCVJkiRJkiQ9P0r6NuBVoAOQDiCEiAd0pRXUs6awwjJAdHQ07m5ufNGpCm56SxTAUOUl8tMScM8rWuMAwMnJCVNTU65cuWJ0HDc3tyJtXV1djdoV1/buqshZWVl07tyZTz75hOvXr5OcnEybNm3UaszlypUrEv+jxCVJkiRJkiQ9P0raScj+MxWqAFAUxbr0Qnr2FFZYTkpKYtKkSXTr1o2O1d04NKIpf0xpy+/TX2fAgAH06NGD0NBQsrOzyczMZNWqVUyZMgUTExO6du1KcHAwqampXLlyha+++orevXsXOVebNm24cOECK1asIDc3l9WrV3PmzBnatWtXbGzZ2dlkZWXh7OyMVqtl+/bt7Nq1S93etWtXFi1axJkzZ7hz5w6ff/65uu1h4pIkSZIkSZKeHyXtJKxRFOV7QK8oSn9gDwXVlyX+qrDs7e1NhQoVGDVqVJE2s2fPZtCgQQwcOBC9Xk+FChXYsGED7du3B2DOnDlYW1vj7e1NgwYN6NmzJ2+++WaR4zg6OrJ161ZmzJiBo6MjU6dOZevWrTg5ORUbm06nY/bs2XTt2hV7e3tWrFhBhw4d1O2tW7fmo48+omnTpvj4+NC0qXGRt5LGJUmSJEmSJD0//rbistpQUZoDLQAF2CmE2F2agT2sJ1VxWZIk6d8kKy5LkiQ9ebLi8p8URfkYOCOEGCaE+ORp6yA8KZ6enuzZs4eN4XEETtmH14gQAqfsY2P4P5/YGxQUxIIFCx46lqfJ38VUkphDQ0Nxd3cv0fkWLVpEgwYNHipGSZIkSZIkqaiSDjfSAbsURTmgKMogRVHKlmZQzwJPT08yMzM5FJnAyPWniEvOQABxyRkM/mo5lWvVM2ovhMDb2xt/f/+/PXbr1q25du3aY4kzNDRUffIIBZOaq1SpYlQQbdSoUY9c72DRokUPvW9xMUVGRj5wn7snY5eGcePGGaV/lSRJkiRJ+i8raZ2Ez4UQAcBAoBywX1GUp+ux9ROy5tcYMnL+qq6cGX2Sazu/IyYpQ10nhMDV1ZWoqCguX77MsWPH7ns8IQQhISG4uLiUWszx8fGsWrXqvtuDgoLkUAZJkiRJkqT/sIctiHYDuAYkAmUefzjPnviLEcQveJ+YWd1ICJmFyMsBICv3r45DWFgYCQkJKIpCvXr1WLx4sdExgoKCCA4OJjAwECsrKy5fvmy0/erVq7z44otMmzYNgIULF+Ln54dOp8Pb25vvv/8egGPHjuHp6YlGo6F27do4OzvTqVOnIm8lTExM6N27N66urkyfPh0oyILUrl07nJ2dOXjwIO3bt8fOzo6qVasSGhrKokWLKFOmDCYmJnh5ebF8+XIAdu7cyd69e4GCQm/Vq1fH1tYWDw8PkpOTOXbsGP7+/tjb29OoUSPKly/PK6+8UiSN64kTJ6hXrx56vR4rKyvMzc3x8/Mr0qGaMmUKFSpUQKfT4e/vz4YNG4r8Jp988gn29vZ4eXmxffv2v36r+Hg6dOiAg4MDPj4+zJ8v595LkiRJkiQVSwjxtx9gABAKnAbGAf4l2e/f/NSsWVP8mwwGg9Dr9UIxMRWKmaWweqGxMHP1FdYBQcLco7Ko/8VetW2fPn2EVqsV9erVE61atRKOjo4iKytLCCFE48aNRfny5YWZmZkwMzMTZ8+eFQ0bNhSVKlUS8+fPFy+88IJQFEWYmZkJa2trAYjJkyeLyMhIsXHjRmEwGAQgzMzMhI+Pj1i9erXQaDTCxMRENGnSRN3esWNHkZGRIQAxb948YWtrK6ysrISNjY3w8fERHTp0EOvWrRMXL14UWq1WVK5cWXTo0EHs2rVLODg4CGtraxEeHi5sbGzEwYMHRUREhBBCiFq1aomVK1cKIYT4+eefxcmTJ0VeXp44ceKE0Gg0wsPDQ0RHR4tDhw4JjUYj+vTpIzIzM8WQIUOEiYmJ2L17twDEhg0bxP/+9z8xbNgwUatWLVGxYkUxZswYERAQINzc3NRruWbNGhEXFyfy8vLEqlWrhJWVlYiPjxdCCLFw4UKh1WrFvHnzRG5urvj2229FuXLlRH5+vhBCiIYNG4r3339fZGRkiPDwcOHk5CT27v3rd5KkZ0Hjxo1F48aNn3QYkiRJ/2nAr+IpuP8tzU9J3yR4AB8JIQKEEOOEEGceb1fl2ZSenk7P9z7G54PF5KXewMS2DFlx5/B6Y5paXfnOnTusXbsWKysrPvroI44ePUpOTg4hISHqca5fv86bb75Jeno6FSpUQKMp+FnOnDlDRkYGy5cvJysri6+++gpfX18GDhxIXl4ePXv2ZP78+bRv3x4TExOSk5Oxt7fHzMwMBwcHfvnlF3755RecnJwIDw9n0aJFAFy+fJn+/fvj6OhIVFQUWq2W8PBwOnfuzE8//UT37t1ZunQpYWFhNG/enOrVq5Ofn8+lS5do164de/fuJSAggIsXL3Lu3Dk1pWpQUBBVqlRBo9Hw4osvYm1tTcWKFfHw8GDPnj00aNCAQ4cOYW5uzoQJE9TvCVC5cmXq1q3LunXrmDBhAgMGDODkyZMMHjzY6Jp36dIFV1dXNBoN3bp1o2LFihw9elTdbjAY6N+/PyYmJrzxxhtcvXqV69evExMTw6FDh/jyyy+xsLCgWrVqvP322yxZsqRU/m1IkiRJkiQ9y0o6J2EkYKMoSj8ARVGcFUXxKtXInnKxsbFYWFjQo3VDpvaqj3ezPmTHnSE/LYkvOlWhY/WCqsTr169Xb+g7dOhAbm4utWrVMhpy5OLiQo0aNdBqtZiamqrrly9fjpubG6+99hoHDx5k1KhRbN68mUOHDvHyyy+Tk5NDly5d2LFjB2ZmZuTn53P69GkcHR3RarXk5+fj6uqKtbU1derU4fjx4wAcPXqUH374gZSUFCZNmkSDBg24du0a7777LhMnTmTZsmVUr16d5ORk9Ho9YWFhuLu7M3fuXDZv3szUqVM5d+4cK1asoGPHjlhZWQFw5MgRmjRpgrOzM3Z2dqSmpqqVnePj4/H29iY+Ph4Aa2trHB0d1e/6xx9/0K5dO/744w9ee+01PvvsMxISEjAYDEbXfcmSJVSrVg29Xo9eryciIoKEhASja1moMK60tDTi4+NxcHBAp/urULjBYCAu7p9nopIkSZIkSXrelDQF6lhgODDyz1WmwLLSCupZYWJiQkxMDB2ru7F2WAc0mbcp6+zIrCG91DZz584lJyeHefPmYWlpye3bt/n5558JCQlRb27Nzc2Lzd4zbtw4nJyc6NixI126dGHx4sUYDAY6d+6Mn58fgwYNIjk5mTZt2gBga2vLzZs3AcjLyzMqsGZhYUFaWhoAs2bN4rXXXiMvL4+ZM2eyePFicnJyOHfuHK+88gpWVlaEh4cDkJCQQHZ2NhcuXGD37t1cvXqVvLw8evTowcqVK+nZs6d6jsKOUExMDCkpKeh0OtLT0wEoV64cixYtwtnZGSh4w5KQkMDQoUMBGDt2LC+88ALly5dn3bp1TJ48GSEE0dHR6vGvXLlC//79+frrr0lMTCQ5OZnKlSurHZEHcXV1JSkpidTUVHVddHQ0bm5uf7uvJEmSJEnSf01Jhxu9CnQA0gGEEPEUpEX9T/L09EQIQV5eHt988w2xsbGcPn0ajUZDkyZN1HaxsbEcOnQIABsbGxwdHbGyskJRFGxsbFi5ciXnzp0zehJ+N1NTU/bu3UtYWBguLi60bNmS7OxssrKycHd3JyYmhu3bt7Nr1y71fKampuTn55OSkkLjxo2NjpeXVzCZ+ttvv+XixYuMHz8eCwsL9Ym7paUlrVq1IjMzkwEDBgCQmZnJ+vXr+eGHH0hPT8fGxoaAgACioqJISkqiefPm6vFTU1NxcHDAwsKCo0ePkp6ezvnz54mNjVXb1ahRg+zsbMaMGWOUhjU9PR1bW1u6devG6NGj+eabb8jOzmbOnDlGbRRFUTsaCxcuJCIiokS/mYeHB/Xr12fkyJFkZmZy8uRJfvjhB3r37l2i/SVJkiRJkv5LStpJyP5zkoYAUBTFuvRCenakp6dz9epVDAYD3bt3x8XFhVOnTqnbv/rqK6DgCX92djbm5ua8+eablC9fnkaNGrF48WKEEKSlpTFkyBAMBgMTJ05Un4wfOnQIS0tL3NzciIiIwM7OjrCwMGbPns3mzZtZt24dAwcOxMrKipSUFPLy8liwYAHXrl1Dq9VSs2ZN6tevT3R0NFu3buX69esA/Pjjjxw4cIBJkyZhZmZGSkoKUDAsp0+fPuTn53P48GGgYEjOJ598wvDhw3F1dcXBwYGUlBSSk5NJSUnBy8tLnevg7OzMxx9/jE6nY/z48ZiampKbm0uLFi1o1KgRAJs3b8bc3JzY2Fj1Zh9g+PDhrFixgm+//Zbo6GhiYmKIiIigT58+aht/f3+GDh1KvXr1KFu2LKdOnSIwMLDEv9fKlSuJiorC1dWVV199lc8//5xmzZo97M8uSZIkSZL0/CvJ7GbgE+B74DLQH/gf8MGTnnV99+ffzG5kMBiERqMRZcqUET4+PkKn0wlnZ2fRvXt3o6wjlpaWQqPRiF9++UXk5OSIiIgIUbt2beHv7y8K47WwsBBlypQRt2/fFn/88YeoWLGimtlo4cKFAhCmpqbCyspKmJmZCUVRxP79+4UQQgQEBAhTU1NhbW0tfHx8hKOjoxg+fLgQQgg3Nzeh0+lESEiIyMvLE7179xZmZmbixo0bIi4uTjg4OAgTExPh7e0tvvnmGwGInJwc8fPPPxtlExJCiLFjx4pevXoJIYSIiooSNjY2YsWKFSI7O1skJCSI8PBwIURB1pX58+er+y1cuFAEBgaqy4C4ePHi4/9BJOk/RGY3kiRJevKQ2Y3UjsR0YB3wE+ALjBFCzHnwXs83W1tbNBoNV6/fxKJSfWjwDj+F7CIhLQuAixcvkpmZiZ+fH/Xq1UOr1RIQEMBPP/1EZGQkU6dOJS8vj6ysLJo2bUr37t2pUqUKOTk56PV63n77bfVcZ86cIT09naioKIQQvPrqqxgMBtLT0xk1ahRpaWkEBwfj4+OjTk7+4IMPeOWVV2jTpg0ajYalS5cSFBTEtm3bUBSFW7ducfPmTS5dusSAAQMQQqDVav/2e69YsYJmzZrRo0cPTE1NcXR0pFq1avdtn5ieTeCUfXiNKMjmtPv046kkLUmSJEmSJJWev78r/JMQYjewuxRjeaakp6fT56Mx7Eh1J3rVWExzFXIz0shrPZaN4XFcO7IXe3t7o4myUDA2vm7duuzevZuAgACEEOzatYvt27dTo0YNmjdvzq+//lrsOYcPHw7A3r170ev1+Pv7c+nSJQD69u0LwJf/9y2BU/Zxak0Yaad2s37jZkxNCiZF5+Tk0KRJE2JiYnBwcMDe3v6hv3dMTAwVKlQoUdvfr9ziSkI6Gcl/VZ+evusC5cp7qdmfJEmSJEmSpKfPA98kKIqSqijK7WI+qYqi3P63gnwa6XQ6lJHwYAAAIABJREFUQo5Hk621wq5eV+5EHsHExoGMnDym7TxPQkICHh4exMbGFrnpL1euHAkJCTg5OaEoCk2aNKFOnTpotVoqV66sTjC+W15eHqtWrQIKJkF7enri7u6uzh2Av27K45IzMNE5Ye3fhPIfrWbRz6dJTk4mPT2dESNG4OHhQVJSEsnJyUXOU1yWpbt5eHioHZN7WVtbc+fOHXV50//OkH9P4qHM3ILrI0mSJEmSJD29HthJEELohBC2xXx0QgjbfyvIp5GJiQkxBzeQezsBxdya/PRkrF5oCEBccgZOTk7cunWLAQMG0KNHD0JDQ8nOziYzM5MTJ05w8eJFTExM8PLy4tSpU6SmpnLlyhU2bdqEubl5kfMlJCSQk5NjtM7CwsLoRn97xFUyrl4kI+o41gFNuHPpKEnnjzJ1+xkyMzMJDQ0lNjaWcuXK0bp1awYMGMCtW7fIyckhLCwMgLJly5KYmKhOZr5Xr1692LNnD2vWrCE0NNRoiFO1atVYv349d+7cITIykvijIUb7aqz15CZfI/6uNwuSJEmSJEnS06ek2Y2ke+Tl5WETEMSNNaO5vno0iqk5dvW7AWCiKDRt2pSYmBh69+7NoEGDGDhwIHq9Hk9PTy5cuKCm3nzppZcwNTXF29ubBg0a8PLLL2NtXTR5lJOTk1GhNShIT6rX69XlW3f+6kRobZ0p02k0KYfXcmRCJ8qWLUvnzp3VtKPbtm1jw4YN+Pr6UqZMGWbNmkVOTg6NGjUiMzMTb29v9Hq9WvysUPny5dm2bRszZsygQ4cOZGVlceLECQCGDBmCmZkZZcuW5Y033sClRnOjffWBPUkMmUnM/3VjzZo1Rts8PT2Jiop6mJ9AkiRJkiRJKiUlnpMgGXN1deWWfxOsq7bi5k8TMPcIQGNqAUCeEFSqVIn33nuP3r17s3TpUgYNGsS5c+fo168fNWvWVCcmm5mZ0bFjRyZOnAhAaGgou3cXTP0YN26cej4TExO6du1Keno6ZcuW5cqVK2g0Gj755BO1jW/D9hw5uEJdNnf1xaXnFNz0lkyqq2HcuHGUL19e3V6+fHmmT59O+/btgYL0pPb29ty8eZPExES13d1xADRs2JAjR44UuSZOTk5qzQaAjeFxjFx/ioycguFTuuptKFOnPVVv7GLz5s107dq15BdckiRJkiRJ+tfINwmPqGfPniT9NIa4799Ga++ivkUAcNNbAvD111/z9ttv07t3b2xsbGjVqhVBQUH89NNPj3TOOXPmYG1tjYeHB76+vvTs2ZM333wTRVEoU6YMH79cAUWB7KsXiJv/Hle+bMeVL9vxYZCh2OP16dOHJUuWqMtLlizh9ddfN2qzcOFC/Pz80Ol0eHt78/3336vbQkNDcXd3V5c9PT2ZPn06L774InZ2dqyc8jGft62Im94SBbC8epzMNUPZuPhbdu7cycmTJx/pOjyKcePGFSmcFhQUxIIFC0q0f9++fdV6EJIkSZIkSc87+SbhERQOi/Fr9TrD1p4g567ZuaYahWEtfQHQaDQMHz5czUpUnHtvPIOCgoiNjVWXd+/ejY+PDwD29vYsW7aM0NBQxo0bx5gxY9R29vb2mF07ib2VGWnnw7Cr04n0o+vITIrn5NaFTJw4kdDQUKNzdezYkTlz5pCcnIwQggMHDjBu3DhGjRqltilTpgxbt27F29ubsLAwWrduTe3atalRo0ax32fNmjXs2LEDCwsLAgMDaXJ8N4dGvEd4eDgtW/Zjy5YthISEsGfPHjp06MD58+cxNzeXQ40kSZIkSZKeIvJNwj91bzKgBycHemjHjh3D398fe3t7+vXrR2ZmZrHtCt8KWJmZ8NW44QRZxzHq4wFAQaVhMH4rALBhwwbat2/P6tWr+eGHHzA3N6d+/fpAwZCi/Px82rZty7p163B3d6ddu3ZoNBp++OEHAM6ePcuNGzfQ6/WUK1eOpKQkBgwYoFZmrlevHhMnTsTBwYHAwEACAgJ46aWX0Gg0uLu7k5SUhL29PQEBAUYZoBRFITIyUl3u27ev2nFJSEigXbt26PV6HBwc1DgB4uPj6dy5M87Oznh5eTF79mwAduzYweTJk1m9ejU2NjZUrVqV4OBgDhw4wKBBg7CxsWHQoEEIIRgyZAhlypTB1taWKlWqEBER8ci/naenJ3v27Hnk/Z82ixYtokGDBk86DEmSJEmS/gWyk/APjNt8mpw84xyfOXnisab4XL58OTt37uTSpUtcuHCBiRMnEhQUVOxbgbCwMPLz87G3t+fAgQO88sorAOrk48K3ArdvF2SvnTx5MnXr1mXJkiXMnDmT6tWrq3MNJk+ejKIozJ8/nzFjxpCeno6JiQlZWVnqOTUaDXq9noSEBP73v/+RkZHB0aNHAUhNTWXt2rWULVuW+Ph4GjRowP/+9z/0ej1Tpkxh7dq1ZGVlMX/+fDp06MCgQYNKdD1mzJiBu7s7N2/e5Pr162qc+fn5tG/fnqpVqxIXF8fevXuZNWsWO3fupFWrVnz22Wd069aNtLQ0Tpw4waRJk2jYsCFff/01aWlpfP311+zatYuwsDAuXLhASkoKa9aswdHRESi4QS6sRfE4hIaGEhQUpC7f2zGaPn065cqV4/Tp0//4PHcPCXsaPO5rKUmSJEnS4yc7CY9oY3gcyRl/ZROK/e5NMqIKUoE+SorP+93MDRo0CA8PDxwcHAgODlbfCtxr0qRJuLu7k56ezqZNm+jQoQMWFgUTqV1dXVEUBV9fXypUqKDWQmjQoAFpaWlcvXpVrW9Q2KFo2LAh2dnZfPDBB1hYWLBq1Spu3rxJ27Zt1SJsvr6+mJmZodVq8fT0RKfTqfMMtm7dio2NDX5+flhYWODl5cXo0aNJTk5mxIgRvPzyy2RlZdGrVy/69OmjZkj6O6amply9epUrV65gampKw4YNURSFY8eOcfPmTcaMGYOZmRne3t70799frS1R0mOnpqZy7tw5hBD4+flRrly5Eu//uEycOJFZs2axf/9+AgIC/vXzS5IkSZIkyU7CI3rQ2wLXPycuQ/FPjK2trbGxscHGxsYohWlxPDw81L8NBkORlKSFJkyYwMyZM0lPT2fjxo20atVKraHQrVvBpOr9+/dTt25dHBwc1OXExES2bNnCzp078fHxoU+fPgBMmTKF7OxscnJyGDx4sDpsaOvWrWoV6ZiYGBISEnBxccHW1pbk5GS1vkJMTIxRRef+/fszd+5cjhw5ghACJycnQkJCSE1NxcrKiszMTHJzcx94LQCGDRuGj48PLVq0wNvbmylTpgBw5coV4uPj0ev16mfy5Mlcv379b49ZqGnTpmq62jJlyvDOO++ob10eVUmHixUaNWoUCxYsICwsjEqVKqnrt27dSrVq1dDr9dSvX99o0ve9E8a7detGZmYm6enptG7dmvj4ePXfW3x8vNHwLSjaQY2JiaFTp044Ozvj6Oh437c8w4YNo0GDBqSkpODg4MCpU6fUbTdu3MDKyoqbN28+9DWTJEmSJOnJk52ER/SgtwWFE5fv58SJE6SlpZGWllZs1eO7xcTEqH9HR0fj6up637YNGzYkLy8PS0tLRo0aRZMmTQDUG8JBgwbxySefqDfOjRs3RghBQEAAderUYcaMGaxYUZBC9auvvuLo0aPMnj2befPmERERQZs2bXBzc2Pfvn0AzJw5E1NTUy5evMjt27fR6/UIUTD8ysPDg1u3bqmx1apVi/nz5zNo0CC+/PJLNm3adN9sQVZWVkaVm69du6b+rdPpmDFjBpcvX2bz5s189dVX7N27Fw8PD7y8vEhOTlY/qampbNu2DSi+knRx6wYPHsxvv/3GmTNnuHDhAtOmTbvv9S6J4oaLAcUOGRsxYgSrV68mLCwMb29vdX14eDhvvvkm33//PYmJibz77rtqjYpChRPG//jjD06ePMmiRYuwtrZm+/btuLq6qv/eHvTvBwrqf7Rr1w6DwUBUVBRxcXF0797dqE1+fj79+/fn5MmT7Nq1Czs7O7p3786yZcvUNitXruTll1/G2dm5yDlkpihJkiRJevrJTsIjuvttQaHsqxe49uMA+jWtXKKnxg9y9uxZrl27xsCBA6lUqRLLly9n0qRJdOvWjb59+/L+++/Tpk0btfDap59+yujRozl+/DhhYWG8+eab6nCjwiE32dnZODs7s3PnTry9vdmzZw+zZ89m3LhxbN26lcjISAwGA25ubpiYmKDRaGjWrBkrV67k+vXrLF26lJdfflnNbKTVahk2bBg2NjacO3cOZ2dn9c1Iu3btgILOQVZWFqmpqdjb23Ps2DFGjBhB586dWbt2rTqJ+m7VqlVjxYoV5OXlsWPHDvbv369uK4xTCIGdnZ0aZ506ddDpdHz55ZdkZGSQl5dHREQEx44dAwoqSUdFRamTnAvXXb58WV0+duwYR44cIScnB2traywsLNBo/tl/kZIOFwPYtWsXrVq1MqplATBv3jzeffddXnrpJUxMTHjjjTcwNzfn8OHDapvBgwerE8bbt2+vVsF+WEePHiU+Pp5p06ap1+Duyco5OTn06NGDpKQktmzZgpWVFQBvvPEGK1euVDuJS5cuVd9KSZIkSZL07JGdhEc0rKUvlqYmRuvunN3P14vX/e0k47+Tk5ND+/btsbS05PPPPycrK4vevXvj5OSkvhVYsWIFwcHB6tCfQgEBAcTExDBpylSc2g4BYPjs5QCMHj2arl270r17d/z9/dW/v/vuO9avX0+zZs2oXLkyAAMGDKBJkyZkZWUxYsQInJyccHFx4caNG3zxxRdAweTaFStWoNPp6N+/vzqsCQqe+O/evZstW7bg4uJCxYoV+fnnn0v0/f/v//6PLVu2oNfrWb58OR07dlS3Xbx4kWbNmmFjY0O9evXUOE1MTNi6dSvHjx/Hy8sLJycn3n77bXX4U5cuXQBwdHRUOzkffvgh69atw97ensGDB3P79m369++Pvb09BoMBR0dHhg0b9lC/3b1KOlwMCjpz69atY+zYsUbrr1y5wowZM4yGUsXExBgdy8XFRf3bysqKtLS0R4o3JiYGg8GAVlt8duTIyEg2bdrE2LFjMTMzU9e/9NJLWFlZERoayrlz54iMjKRDhw6PFIMkSZIkSU+erJPwiDpWdwMK5ibEJ2eg1Sj07f8eb7euDUBwcDAffPCBOrzkblWrVsXU1BSA119/XU3VWejw4cOkpaWRmJiIRqNh1KhR9OjRA19fX/XJraWlJWfPniUwMBAhhFG2mGnfLcLkhSZkuFTFMHwrMV8XFEgrW70Z16+PKxKPlZUViqIUW6vgxRdfVDMW3atRo0acO3fOaN348ePVvytXrszevXuL7HdvBWdPT0/1CTQUvH24X1afIUOGMGTIkGK3ubq63vdJvaOjIwcPHjRaV69ePS5cuGC07nEXeHuY4WKVKlViz549BAUFYWlpyYgRI4CCjkZwcDDBwcEPff7ihlRZW1vfdziXh4cH0dHR5ObmFttR8PPzY+DAgbRu3Zp9+/bh6/vX0Lo33niDZcuW4eLiwmuvvaa+yZIkSZIk6dmj3H1z9izT6XSiZs2aT+z8hw8fpmLFimrKzPT0dH7//XcaNmxo1G7//v34+voaPfkFiI2N5fLlyzRq1IgbN24QGxtL9erVOfxrOMJCh2XFeoj0RCpV8iX20EaSk5MxGAxq9p1z585hbm6Ol5cXR6NuYVmpProaBUN+Yr7tR37qTTz6zsD7j83cvn2by5cvc+fOHfLz88nPz8fZ2Rk/P79SvUaFnRBPT89SPc/T4vDhw2i1WqpUqYJGoyEiIgI7Ozuj+QaF9u/fT506dbC0tFTTtBoMBtzd3UlNTeX06dP4+/uj0+nIz88nOTkZOzs7tFothw8fxtfXV50oHhUVRUZGBn5+fty5c4fffvuNevXqqTf9V69eJSYmhurVqyOEICIigqysLOrVq4cQgt9++w17e3s8PT1RFIXU1FTs7Oy4du0aV69epXr16ly7do2oqCiqVq2KpWXB0LvMzEx+++03TExMeOGFF/52Ur70aAqHklWrVu0JRyJJkvTftX///t+EELWedBylSb5JeIzunkiamZlpNBzjYZiZmZGZmcnVfFtycnIQWTcxuX4Zc7cXSPRuRe6vPwP3n/Cs0TmTezvhrxX5eQDkmdoABfMd3NzccHV1RaPREBkZSU5OTnGHkv6hMmXKcPLkSbKysnBycsJgMPztPjY2Nrz44oucPHkSjUaDq6srlSpVIjIykjt37mBiYoKtrS12dnZ/eywrKyvKlCmjZpWqXbs2ZcuW5datWxw5cgQLCwtcXFzUNx6KolC5cmUiIyPVOQ9ly5Ytci4XFxeEEJw4cYJq1aphYWGBhYUFNjY2ZGRklCg2SZIkSZKeYkKI5+JTs2ZN8SQZDAZRuXJlERMTIxITE0VgYKAYOXJkkXaAGDp0qPDz8xN6vV707dtXZGRkiJkzZwozMzMhhBBZWVnCy8tLWLtVEpZ+DYWZu79AYyJc3/5OGIZvFc41Wojy5cuL+fPnCyGEiIyMFC4uLsLCwkI4OjoKW6+qQmNlJ8q99a3w+HidUEzNBSDMHcoJvV4vLCws1H13794tzMzMhLm5udDr9aJt27YiJiZGjbdx48YiODhY1KtXT1hbW4t27dqJhIQE0bNnT6HT6UStWrXEH3/8obYfPHiwcHd3FzqdTtSoUUOEhYWp28aOHSvGjh1bCldfelr069dPBAcHP+kwnmuNGzcWjRs3ftJhSJIk/acBv4qn4P63ND9y4vJj1LNnTzV/f4UKFYxy0d9t06ZNRdJiVqtWTU0XaWZmxpo1a0iPjyTz4lHyUq6jaM3Q6guGKN3JzjM6nhCCKlWq8OGHH3L27Fnc9RaYO7hyfdVnxM97B0wK3miMmfo1ly5donz58gwdOhSdTsfUqVNp2rQpHTt2JDo6GktLyyJ58VetWsXSpUuJi4vj0qVL1KtXj379+pGUlISfnx+ff/652rZ27docP36cpKQkevbsSZcuXdQsT+PGjSsyH+G/YGN4HIFT9uE1IoTAKfvYGB73pEMqFVFRUaxfv5633nrrSYciSZIkSdI/JDsJj0lUVBQjR47kzJkzJCcns3jxYnWS8d0MBgNDhw4tkhYzKCiI2NhYtd25c+fQWunwGLIGt3fmAwoZlwrSeVbt9RleXl5qWx8fH3bt2sWUKVNwdnZm0pgRuFhrqDvqJzwGLsHMwop3P/uCz97qhIODAzNnzsTJyYnU1FR27drF9u3bWbVqFTqdjuDgYKOUowD9+vWjQoUK2NnZ0bp1aypUqECzZs3QarV06dKF8PBwtW3v3r1xdHREq9UydOhQsrKyOH/+/oXnnncbw+MYuf4UcckZCCAuOYOR6089dx2F0aNHU7lyZYYNG2b0b1OSJEmSpGeT7CQ8ZiV5alyStJiLFy+mWZuOWJmboWjNsPKtT1rEPixNTYoUa7t+/Trdu3fHzc0NW1tbevfuTU56CodGNOWPKW1xsbOgff0qxZ7zzp07vPvuuxgMBmxtbWnUqBHJycnk5f31tqJs2bLq35aWlkWW7063OX36dPz8/LCzs0Ov15OSkkJCwl3zI/5jpu08T0aO8ZufjJy8B1bsfhZNmDCBtLS0R8rAJEmSJEnS00dOXH6MCp8aF94UFj41hr9SpsLfp8WMjY1l37592NjYoNm3jfSsXHKzMyEvh5FN3OhY3Y1Zd7X/7LPPUBSFU6dO4eDgwMaNG4sMGbrfOWfMmMH58+c5cuQILi4uHD9+XM1687AOHDjA1KlT2bt3LwEBAWg0Guzt7R/pWM+L+1XmflDFbkmSJEmSpCdNvkl4jEr61Pibb74hNjaWpKQktYry3ZYuXUqlSpU4f/48ZyNOEn3xDPFXLuNZ3oOU06FFzpuamoqNjQ12dnbExcUxbdq0Im3ud87U1FQsLS3R6/UkJSUZzS94WKmpqWi1WpydncnNzWX8+PHcvn37kY/3PCiuMveD1kuSJEmSJD0NZCfhMSrpU+O/m+C8ePFiBgwYgIuLi9HnvffeY/HixUWOP3bsWH7//Xfs7Oxo27YtnTp1KtLmfuf86KOPyMjIwMnJibp169KqVatH/fq0bNmSVq1aUalSJQwGAxYWFkZDq/6LiqvMXdyQMUmSJEmSpKdJqRZTUxSlFfB/gAmwQAgx5Z7tjYBZwItAdyHEuru2vQEU3j1PFEIUvTu+S61atcSvv/76OMN/aIFT9hFXTEfBTW/JoRFNn0BE0tNgY3icWpnbVW/JsJa+RsPPJOlhBAUFARAaGvpE45AkSfovUxTluS+mVmpvEhRFMQG+AVoD/kAPRVH872kWDfQFVtyzrwMwFngJqAOMVRTFvrRi/Sc8PT3Zs2cP8HBPjUsjLWZoaCju7u733f7ee+8xYcKEf3weyfh3/zsdq7upk8gPjWhapIMQFBTEggULHktcffv2vW/q3cchKioKRVHIzc197MdetGgRDRo0eOzHlSRJkiTp4ZXmcKM6QKQQ4rIQIhtYBbxydwMhRJQQ4iSQf8++LYHdQogkIcQtYDfw6ONg/iUdq7vxRacquOktyYw+ya01wXzRqQrb547n/fffV9utOxpFp5cqcHL1dDUt5oi14VhYWqlVbkvD3LlzGT169GM5lqIoREZGPpZjLVq0iL59+z6WYz0NQkND1ae9UHCtrK2tsbGxwc3NjY8//tgoe9TT4nH+po+DoihPOgRJkiRJ+s8qzU6CGxBz13Lsn+se276KoryjKMqviqL8evPmzUcO9HEqfGq86p16VC+vp2N1Nxo1akRYWJjaZvzCLZjonMmKPU3sd2+SEXWclJhz5OQJatas+QSj/8vDPCl/HA4ePFiqT8CftBMnTpCWlsb+/ftZvXo1P/7445MOSZIkSZIk6b6e6YnLQoh5QohaQohahdWKn4Rjx47h7++Pvb09/fr1UysMF2rUqBFnz55V6wXEnvsda79G5OdkIfILXqJkxp7GzNUXU1NTALp06YKDgwNarZZGjRpx+vRpNm3aRLVq1bCyskKr1aLVanFxcWH69OlG55sxYwZlypShXLlyLFy4UF1/91CUwqFJM2bMwNLSEhsbG6O2eXl5jB49GltbW2rXrs2oUaPUoSCNGjUCoGrVqtjY2PD+++9jYmKCubk5Go0GExMTypcvz4EDB9TjKYrC3LlzqVixInq9noEDBz6W1KgPO0QlKioKT0/Pf3xe+PvfvTg+Pj4EBgZy/Phxo/VXrlwhMDAQnU5HixYtjGpLbN68mYCAAPR6PUFBQZw9e1bdFh4eTo0aNdDpdHTr1q1IDFu3bqVatWro9Xrq16/PyZMnS/Tdjh49Sq1atbC1taVs2bJ8/PHHxbZbuHAhfn5+6HQ6vL29+f7779Vtd/8bK+7fY2JiIh06dMDW1pY6depw6dKlEsUmSZIkSVLpK81OQhxwd2ob9z/Xlfa+/7rly5ezc+dOLl26xIULF5g4cSJBQUHqxEIPDw8MBsNfN81Xz2LuEYC52wuQlwNAVsxpnCtWU4/ZunVrli1bRv369alRowavvfYar7/+OjNmzMDOzo6QkBBWr17N7t27adr0r0nR165dIyUlhbi4OH744QcGDhzIrVu3io27sG2XLl1o166dUdukpCQsLCy4du0aixcvNsqqVPhWpPDp+EsvvaTeKB45coRTp05hZ2dHUFAQERER6n5bt27l2LFjnDx5kjVr1rBz506goPPyLI5FL+53B4x++3udO3eOAwcO4OPjY7R+xYoVLFy4kBs3bpCdna12/C5cuECPHj2YNWsWN2/epE2bNrRv357s7Gyys7Pp2LEjffr0ISkpiS5duvDTTz+pxwwPD+fN/2fvzsNruvYGjn93BklkOpnIIDkRMUaqhiiNOqSmUuo1xCxUXW2vUnOIEopqqQ63Sg2NhpjubU0ltFREW2OJKkoIEkkaIk7mOev9Q+06kpCooKzP85znOWfvtfdae+/wrLXX8Hv1Vb744guuX7/OqFGj6NGjB/n5+fe8trFjxzJ27FgyMjK4cOECgYGBZaarUaMG3377LRkZGYSFhTFu3DiOHTum7r/b3+O///1vzM3NSU5O5ssvvyzVu/I0x9eQJEmSpEetKhsJR4C6iqLUVhSlGtAf2FrBY3cBnRRFsftzwnKnP7c9lkaPHo27uzv29vaEhISwbt26Uml0Oh3R0dGUlJRQfPU8Gm0jzGv5IIoLyE86S27cUa799D/1jfSrr75K9erVMTIyIjQ0lN9//x0PDw9efPFFTE1NuXTpEh07dsTX15dmzZqRn5/PZ599RnFxMcuXL2fSpEm8+OKLWFlZsWjRolKV8Pbt22NiYoKLiwvr1q3jm2++ITc3l+7du1NcXExOTg5+fn60atWK1q1bY21tTUnJnVNH/nLt2jVeffVV/Pz8aNSoEQcOHEAIwaRJk9Q02dnZNGjQAF/fm9GfbzUS7pSZmUn79u0ZM2YMQgjS09MZOnQoTk5OaLVa5syZQ0lJCWfOnOH111/nwIEDWFlZodFoANi+fTtNmzbFxsYGd3d3QkNDK/tIK6Qiz/2WZs2aYWlpScOGDWnXrh1vvvmmwf7hw4dTr149LCwsCAwMVHsaNmzYQLdu3ejYsSOmpqZMnDiR3Nxcfv75Zw4ePEhhYSFvv/02pqam9OnTBz8/P/Wcy5YtY9SoUTz33HMYGxsTFBSEmZlZhea9mJqacv78eVJTU7GysqJVq1ZlpuvWrRt16tRBURR0Oh2dOnUy6EEyNTVlxowZmJqa0rVrV6ysrDh79izFxcV8/fXXzJ49G0tLSxo3bkxQUNA9yyVJkiRJ0sNRZY0EIUQRMJqblfszwEYhxClFUWYritIDQFEUP0VRrgB9gS8URTn157FpwLvcbGgcAWb/ue2xdHssAK1WS1JSUqk0t+YlnDx5kob1vHm/nx/uDZshigrJO7UHExNTLpygE8MDAAAgAElEQVQ/x7lz55g9ezbBwcGMGDGCY8eOqcNjzp07x7hx45g6dSpbt25Fq9Wi0+k4cOAAc+fO5fTp07i4uPDrr79y+PBh5syZQ/Xq1csdBqPRaHjjjTcYNGgQkydPRqvVEhoayq35HXv27GHnzp1cvHiRtLQ0rl69Wu49KCgoQKvVqr+trKywtrY2qJB26tSJ2NhYrl69ipOTE2vXri11nuvXr/Piiy/i7+/Pp59+iqIovPXWW6SnpxMXF8e+ffsIDw9Xh7ksXbqU1q1bk5WVhV6vB8DS0pLw8HD0ej3bt29nyZIlbN68Gbg51+LSpUvlXkdlVOS533Ls2DGysrLYsGEDhw4dIjs722C/s7Oz+r169epkZWUBkJSUZHBfjYyMcHd3JzExkaSkJNzc3Awm+N6e9vLly3z44YdoNBr1k5CQcNdy3rJy5UrOnTtHgwYN8PPz49tvvy0zXWRkJK1atcLe3h6NRsOOHTsMhko5ODhgYvJXYPdb13bt2jWKiopK3UNJkiRJkh4PVTonQQixQwhRTwhRRwgx989tM4QQW//8fkQIUUsIYSmEcBBC+Nx27JdCCO8/P2Hl5fE4SEj4a451fHw8rq6updK0bduWEydOsH37dl544QV6NnXj+EevYkQJndu0oNVzLXF1dSUkJIQVK1awZcsWdu/eTXp6ulqpXbNmDYmJicyYMYM9e/bQvXt3XnrpJQIDA4mIiGDo0KEYGRnh5OTEzJkzWb169X1dz635Hf3798fV1RV7e3tq165dqmJ7u2rVqnH58mX1d3Z2NtnZ2WRmZqrb+vbti7W1NWZmZjz77LNcvXqV9PR0dX9SUhI6nY6+ffuqQ3eKi4tZv3497733HtbW1nh6ejJhwoS7Xlu7du3w9fXFyMiIZ555hgEDBrBv3777uhd3U5HnfjtFUQgMDKR169bMnj27Qnm4uroa3FchBAkJCbi5ueHi4kJiYqLBsJz4+Hj1u7u7OyEhIej1evWTk5PDgAED7plv3bp1WbduHVevXmXKlCn06dOn1PPPz8+nd+/eTJw4kZSUFPR6PV27dq3QMCEnJydMTExK3UNJkiRJkh4P/+iJy4+LxYsXc+XKFdLS0pg7dy79+vUrlcbb25uaNWvyySef8MILLwA3K41mZmZER0erk4G1Wi03btzAzMwMBwcHcnJymDZtGgBNmzZlzZo1fPzxx0RGRvLjjz8SHR2NkZERSUlJ1KxZU83vXm+278bY2Jjq1asTGRlJTk4Ov//+O6dPnzZYtrNmzZrExcWpv52cnAgLCyMmJob8/HymTZtG7dq1cXBwUNMsWLCAOnXqYGNjw//+dzNu3u1vnbdv305ubi6vv/66ui01NZXCwkKDt8xarZbExPKnqBw6dIj27dvj5OSEra0tS5cuNcjnQanIcy9LcHAwy5cv548//rhn2sDAQLZv386ePXsoLCzkww8/xMzMjOeff57WrVtjYmLCp59+SmFhId988w2HDx9Wjx05ciRLly7l0KFDCCHIzs5m+/btBg238qxZs4Zr165hZGSkDuMyMjL876KgoID8/Hy1wh8ZGcl3331XoXtgbGxMr169CA0NJScnh9OnT5cZTVySJEmSpEdDNhIegIEDB9KpUye8vLyoU6dOuUt5tm3blmvXruHv769uMzMzQ6/Xq42E+Ph4atWqhVarxc3NjUaNGpUaD7569Wp69epFYmIiP//8MxEREbi6upKSkqKmuf3Ntrm5OTk5Oeq+OyunZa1Hb29vT3Z2Ns7OzgwZMoTGjRsbVBJDQ0MJCgpCo9Fw+PBhNBoN7777Lr1798bFxYULFy7g4eGhNogAdu/erfaO9OnTBzCcnDpy5Ei6dOlC165d1bfWjo6OmJqaGrxNj4+Px83NrdyyDxw4kB49epCQkEB6ejqvv/56lUyCrehzv5Ovry9t27ZlwYIF90xbv3591qxZw1tvvYWjoyPbtm1j27ZtVKtWjWrVqvHNN9+watUq7O3t2bBhA7169VKPbdGiBcuXL2f06NHY2dnh7e3NqlWrKlTGnTt34uPjg5WVFWPHjmX9+vVYWFgYpLG2tubTTz8lMDAQOzs71q5dS48ePSp0foDPPvuMrKwsnJ2dGTZsGMOHD6/wsZIkSZIkVTEhxBPxad68ufgn0mq1onHjxiIhIUFcv35d+Pv7i6lTp5ZKt3//frFs2TKRkpIihBDizJkzom7dumLOnDlCCCFCQkJE69atxdWrV8W1a9eEv7+/CAkJEUIIcfbsWVGtWjVx/PhxkZubK0aNGiUAERsbK4QQYsqUKWLAgAGlyvX999+rv59//nlRu3btMq8hLCxM+Pv7CyGEKCoqEnFxcWL06NHC0tJS/Prrr0IIIRYvXiyaNGki0tPTRVZWlnjjjTcMyhAUFCRCQkJESUmJGDFihGjfvr3IyckRQggxaNAg0bNnT5GRkSEuXbok6tevL5YvXy6EECIyMlJotVqRn5+vlsfJyUmsWrVKCCHEoUOHhJOTkxg0aFCFn4kkPc50Op3Q6XSPuhiSJElPNeCoeAzqv1X5kT0Jj4GKvJHWaDRs3boVX19frKys6NKlC//3f//H5MmTAZg+fTotWrTgmWeeUVc8unWeevXqMWPGDDp06EDdunVLrXQ0YsQITp8+jUajoWfPngAUFhYSFxeHEILDhw9z/Phxg0mmd7q1wpCNjQ3t2rUjIyODI0eOqCsZDR069K69I7coisKyZcuoVasWr7zyCnl5efznP//B0tISLy8v2rRpw8CBA3n11VcBCAgIwMfHB2dnZxwdHQH4/PPPmTFjBtbW1syePbvc5TsflgcVmO6ll176W0NyKnP8381LkiRJkqR/NkU8IWuRt2jRQhw9evSR5b/5eCILdp0lSZ+Lq8aCSZ3r07NpRQNMP36OHDnCgAED1LkO//rXvwgODi5zeM/TrCLP3dPTkxUrVtChQweD7VFRUYSGhhIVFUWDBg2YPHmy2vi55ZNPPmH16tU8yr/te/H09CQqKuqBBamT7q5du3YA5cbikCRJkqqeoii/CCFaPOpyVCXZk/AAbD6eyNRvTpKoz0UAifpcpn5zks3HKx7/bfPxRPzn/0Dt4O34z/+hUsdWBT8/P86fP09OTg4XL15k6tSpsoFwhwfx3G8JCgoiPDy81PbVq1dXKH5AUVFRpfN8GB5UL8rDEB8fj5WVlTpBX/amSJIkSU8z2Uh4ABbsOktuYbHBtthPgwhZXDoOQFkeZGXz75o3bx6vvfbaQ8/3n6is555bWMyCXWdLpT1y5AiNGjXCzs5ODZh3uyFDhvDjjz8aTNA+ffo0v/76q7pkabt27VixYgUAq1atwt/fn3HjxuHg4EBoaCjFxcVMmDABR0dHateuzWeffYaiKGoD4s7j27Rpw8SJE7Gzs6N27dpERkaqed+e9sKFCwQEBODg4ICjoyODBg1SY1Lcr6ioKPWNONwcZnb+/HmDNKGhoQwePPhv5VMZHh4eZGVlYWxsDNyMAXGrgbZq1SqGDRv20MoiSZIkSY+abCQ8AEn63DK3X88qKLXtzsqREILxM9/nwhdvEP9hbxI+G8wfa4NJPfFDmZXNqjZt2jS1cngvw4YNq/BqOU+i8p57WdsjIiLYtWsXFy5c4Ny5c8yZM4d27dqpQ0Zq1apF+/btDeI/rF69mq5du6pzLe506NAhvLy8SElJISQkhOXLlxMZGUlMTAzHjh1TA8iV59ChQ9SvX5/U1FQmT57MiBEjylwFSgjB1KlTSUpK4syZMyQkJBhEsb506ZIcaiRJkiRJTxjZSHgAXDUWZW53sKp2z2PHjBlDwv7/Ydd+BLXGrKXWm1+haTuE3IvHyq2E3q/HdUjKP1V5z72s7aNHj8bd3R17e3tCQkJYt25dqTRBQUFqI6GkpISIiIi7DjVydXXlrbfewsTEBAsLCzZu3MjYsWOpVasWdnZ2BAcH37X8Wq2WkSNHYmxsTFBQEMnJyQbL6N7i7e1Nx44dMTMzw8nJifHjx1cqON29elEqauzYsbi7u2NjY0Pz5s3Zv38/AHl5eVhYWKixMObOnYuJiQkZGRkAvPPOO7z99tvAzVgcTZs2xcbGBnd391KNnfJ6XiRJkiTpaSMbCQ/ApM71sTA1NtimKNDMIu2ulaNz587x+eef02jQO1jUboqRqRmKkTHmtXxw7DZOrWymp6czYsQIXFxccHNzY/r06eq46ZKSEubMmYNWq6VGjRoMHTpUjWJ8q9KzcuVKPDw8CAgIACA8PBytVouDgwPvvvuuwbjxO4d49O3bF2dnZ2xtbWnbti2nTp2qmpv4D1TWc7cwNWZS5/ql0t6+MlR5ge569epFcnIyBw8eJCoqipycHLp161Zu/neuNpWUlGSw7W6rUQE4Ozur36tXrw5AVlZWqXQpKSn0798fNzc3bGxsGDx4cKWC092rF6Wi/Pz8iImJIS0tjYEDB9K3b1/y8vIwNzfHz89Pbbjs27cPrVbLTz/9pP7W6XQAWFpaEh4ejl6vZ/v27SxZsuSePS4ge80kSZKkp49sJDwAPZu68V4vX9w0FiiAm8YCu+rVOL53210rRz/88APu7u7MGtHjrpXNYcOGYWJiwvnz5zl+/DjfffedwdjyVatWsXfvXuLi4sjKymL06NEG59q3bx9nzpxh165dnD59mjfffJOIiAiSk5NJT0+/a/Til156idjYWK5evUqzZs0YNGiQuu9pH6dd1nN/r5dvmataJSQkqN9vD3R3u+rVq9OnTx/Cw8NZvXo1/fv3p1q18nuj7pxI7uLiwpUrV8rM8++YNm0aiqJw8uRJMjIyWLNmTaWC01WkFwWgWbNmaDQa9TN//nyD/YMHD8bBwQETExMmTJhAfn4+Z8/eHJKn0+nYt28fRUVF/Prrr4wZM4Z9+/aRl5fHkSNH1GCF7dq1w9fXFyMjI5555hkGDBhQqV4RSZIkSXpamDzqAjwpejZ1M6gcei41VitHACEhIbz11lvMmTNHTZOamoqzs7N63IJdZzk0NxCK8jAqKaLpa2dJSUlhx44d6PV6LCwssLS0ZNy4cSxbtoxRo0YRERHB+PHj8fLyAuC9996jcePGhIWFqfmEhoZiaWkJwP/+9z+6d++uxkqYPXs2n376abnXdfuSnKGhodjZ2ZGeno6tre3fvWVPhDufe3kWL17Myy+/TPXq1Zk7dy79+vUrM11QUBC9evWisLCQPXv2VKosgYGBfPLJJ3Tr1g1LS0vef//9Sh1fnszMTGxtbbG1tSUxMbFCkaJvV5FeFIBjx47h7e2t/g4NDTWYzLxw4UJWrlxJUlISiqKQkZGh9mjodDrGjx/PsWPH8PX1pWPHjowYMYKDBw/i7e2Ng4MDcHMeRnBwML/99hsFBQXk5+fTt2/fSl2PJEmSJD0NZE9CFbpX5cjBwYHk5GTgZmXzp+AAijJTycvUU1iQjxCCy5cvU1hYiIuLi/qGddSoUVy9ehW4OcREq9Ua5FNUVGQwtvz2ctw5JKV69epqBepOxcXFBAcHU6dOHWxsbNTJqZUZaiLdVJGAeQBt27bF1taWWrVq4efnV6k8Ro4cSadOnXjmmWdo2rQpXbt2xcTERF2t537NnDmTY8eOYWtrS7du3ejVq1eljq9IL8q97N+/nw8++ICNGzdy48YN9Ho9tra2ao/G888/z9mzZ9m0aRM6nY5GjRoRHx/Pjh071KFGcPM59OjRg4SEBNLT03n99dcr1SsiSZIkSU8L2ZNQhe5VOQoICGD06NEcPXqUFi3Kjsfh7u6OmZkZqampmJiUflyurq4Gy2bGx8djYmJCzZo11aEntw9LcXFxUYdoAOTm5nL9+vUy8167di1btmxh9+7deHp6kp6ejp2dnaxUVdKlS5cAmDp16j3TKopCXFxcmftuH8M/bNiwUkO9TExM+Oijj/joo4+Am0t4urq6qs//Xsff/lxvT+vj48Mvv/xikHbChAn3vJZbKtqLcjeZmZmYmJjg5OREUVER8+fPVycmw83GbvPmzVm8eDHbt28HbjYcli5dysqVKw3OY29vj7m5OYcPH2bt2rV06tSp0uWRJEmSpCed7EmoQosXL+bKlSukpaWVWTmqX78+o0aNon///nz//ffk5uZSXFzMzz//rKZxcXGhU6dOTJgwgYyMDEpKSrhw4YI6jnrAgAF89NFHXLx4kaysLKZNm0a/fv3KbFAA9OnTh23btvHzzz9TUFBAaGhouZX+zMxMzMzMcHBwICcnh2nTpj2gO/N0quqAebm5uezYsYOioiISExOZNWsW//d///dA87gfFe1FuZvOnTvTpUsX6tWrh1arxdzcvNTEbJ1OR2FhIS1btlR/Z2ZmqvMRAD7//HNmzJiBtbU1s2fPJjAw8O9dnCRJkiQ9qYQQT8SnefPm4nGi1WrFvHnzRMOGDYWtra0YOnSoyM7OLpWupKREfPLJJ6Jx48bC3NxcODs7i7Zt24oNGzaI4uJiIYQQer1evP7668LNzU3Y2NiIZ599Vqxbt04IIURxcbGYNWuWqFWrlnB0dBSDBg0SaWlpQgghLl68KABRWFhokGdYWJhwd3cX9vb2Yvbs2cLV1VVER0cLIYSYOXOmGDRokBBCiMzMTNGjRw9hZWUlPDw8xFdffSUAERsbW2X37Um16dgV0WB6pNBO+Vb9NJgeKTYdu/LA8sjOzhYtWrQQVlZWwsnJSQwbNkykp6c/sPNLjwedTid0Ot2jLoYkSdJTDTgqHoP6b1V+FPGEDB1p0aKFOHr06KMuxj9OVlYWGo2G2NhYateu/aiL88Tyn/8DiWXEvXDTWPBTcMAjKNHDtfl4Igt2nSVJn4urxoJJnetXaMK3VNqtYIyVXUJWkiRJenAURflFCFH2WPEnhBxu9BTatm0bOTk5ZGdnM3HiRHx9fWXE3CpWmejMT5rNxxOZ+s1JEvW5CCBRn8vUb04+8OFWkiRJkiQ9OLKRUMVuH4dubufMrKXrH9i5X3rpJb766qtKH7dlyxZcXV1xdXUlNjaW9evXl1pzX3qwKhOd+UmzYNdZcguLDbblFhazYNfZco6QJEmSJOlRk42EKnTnG9SiEsHK/RdLvUGNiopShxDAzRVuatSoQVFRkbqtsLCQGjVqGFTmIyMjCQoKqnS5VqxYgV6vJz09nT179lC/fukIwX/XrWjPt1/DvXh6eqorAT1pKhOd+UnzNPeiSJIkSdI/lWwkVKGy3qDmF5dU6A2qnZ0dkZGR6u/IyEjs7OweeBmlh6My0ZmfNE9zL4okSZIk/VPJRkIVKutNaUHyOY4sHIadnR3Dhw8nLy+vzGOHDBlCeHi4+js8PJyhQ4capGnXrh0rVqwAYNWqVbRp04aJEydiZ2dH7dq1DRoZ7dq145133sHf3x9ra2s6depkEBTt4MGDPP/882g0Gpo0aWIwKXLVqlV4eXlhbW1N7dq1iYiIAKCkpIQ5c+ag1WqpUaMGQ4cOJT09HUBddlKj0WBlZcWBAwe4cOECAQEBODg44OjoyKBBg9Dr9ZW5pf9otwLmXZzfjZ+CA56KBgI83b0okiRJkvRPJRsJVaisN6XZp6N45rUPuHDhAufOnWPOnDm0a9eu1EolPXv2JDo6Gr1ez40bN9i/fz+vvPLKXfM7dOgQ9evXJzU1lcmTJzNixAiDGAhr164lLCyMq1evUlBQwMKFCwFITEykW7duTJ8+nbS0NBYuXEjv3r25du0a2dnZjBkzhsjISDIzM/n555959tlngZuNh1WrVrF3717i4uLIyspi9OjRAERHRwOg1+vJysqidevWCCGYOnUqSUlJnDlzhoSEBEJDQ9XyXbp0SU6gfgI9zb0okiRJkvRPJRsJVaisN6j2fj2Y3u8F7O3tCQkJYd26dWUea25uTvfu3dmwYQMbNmygR48emJub3zU/rVbLyJEjMTY2JigoiOTkZFJSUtT9w4cPp169elhYWBAYGEhMTAwAa9asoWvXrnTt2hUjIyM6duxIixYt2LFjBwBGRkb89ttv5Obm4uLigo+PDwARERGMHz8eLy8vrKyseO+991i/fn258xC8vb3p2LEjZmZmODk5MX78eDUonPRke1p7USRJkiTpn0o2EqrQnW9QTYwUXuvip1aQtFotSUlJ5R4/dOhQwsPDyxxqVBZnZ2f1e/Xq1YGbcRDK239r3+XLl/nvf/+LRqNRPz/++CPJyclYWlqyYcMGli5diouLC926deP3338HICkpCa1Wq55Tq9VSVFRk0DC5XUpKCv3798fNzQ0bGxsGDx5sMORJkiRJkiRJejzIRkIVu/0NqrOtObWq5aj74uPjcXV1LffYF154Qe0NaNOmTZWV0d3dnSFDhqDX69VPdnY2wcHBAHTu3Jnvv/+e5ORkGjRowMiRIwFwdXXl8uXLBtdjYmJCzZo1y1xSddq0aSiKwsmTJ8nIyGDNmjU8KcH8JEmSJEmSniSykfCQLV68mCtXrpCWlsbcuXPp169fuWkVRWHbtm1s3bq1SuMYDB48mG3btrFr1y6Ki4vJy8sjKiqKK1eukJKSwpYtW8jOzsbMzAwrKyuMjG7+2QwYMICPPvqIixcvkpWVxbRp0+jXrx8mJiY4OTlhZGREXFycmk9mZiZWVlbY2tqSmJjIggULquyaJEmSJEmSpPsnGwkP2cCBA+nUqRNeXl7UqVOH6dOn3zW9j4+POgegqri7u7NlyxbmzZuHk5MT7u7uLFiwgJKSEkpKSli0aBGurq7Y29uzb98+lixZAsCrr77KkCFDaNu2LbVr18bc3Jz//Oc/wM3hTCEhIfj7+6PRaDh48CAzZ87k2LFj2Nra0q1bN3r16lWl1yVJkiRJkiTdH+VJGe7RokULcfTo0UddDEmSpCp1K/DinSuiSZIkSQ+Poii/CCFaPOpyVCXZk1CFPD092b17d6ntm48n4j//B2oHb8d//g+lIjA/qnI9DqKioqhVq5b628fHR1aGJEmSJEmSHjLZSHjINh9PZOo3J7nw6yGS1waTqM9l6jcnmfrRlzz77LPY2Njg6OhIQEAAFy9e/Nv5DRs27J5Dmu4lOTmZESNG4OLigrW1NQ0aNGDmzJlkZ2f/7fLdy6lTp9Q3p3/HqlWrGDZs2N8+jyRJkiRJ0tNANhIesgW7zpJbWGywLeNqAgumjuHDDz8kPT2dixcv8u9//xtjY+NyzvLwpKWl0bp1a3Jzczlw4ACZmZl8//336PV6Lly4UOnzlRdD4VF6nHtWJEmSJEmSHgXZSKhiR44coVGjRtjZ2TF8+HCupKaXSlOQEoeRbQ1efPFFFEXB2tqa3r174+HhAUB+fj5vv/02rq6uuLq68vbbb5Ofnw/cfEN+5/KoiqJw/vx5li1bRkREBB988AFWVlZ0795dTRMTE8MzzzyDra0t/fr1Iy8vr8zyL1q0CGtra9asWaNGQ3Z3d+eTTz7hmWeeAWDs2LG4u7tjY2ND8+bN2b9/v3p8aGgoffr0YfDgwdjY2LBq1aq7Xs+dbq/Ah4aGEhgYyNChQ7G2tsbHx4fb56HMnz+fOnXqYG1tTaNGjdi0adNdn829REVFGfRiCCFYsGABdevWxcLCAg8PD6ZOnVpu2R+1siJ5S5IkSZIkVYRsJFSxiIgIdu3axYULFzh37hwlx74GwNzjGZwHzgegmrM3RWlXGDduHHv37jUIgAYwd+5cDh48SExMDCdOnODw4cPMmTPnnnn/61//YtCgQUyePJmsrCy2bdum7tu4cSM7d+7k4sWL/Prrr6xatarMc+zevZtevXqpy56Wxc/Pj5iYGNLS0hg4cCB9+/Y1aHRs2bKFPn36oNfrGTRo0H1fD8DWrVvp378/er2eHj16MHr0aHVfnTp12L9/P+np6cycOZPBgweTnJwM3Bx2Vd41VtSYMWNYtmwZ4eHhZGZmEhkZyZ49ewgMDCz3mOLi4nL3SZIkSZIkPa5kI6GKjR49Gnd3d+zt7QkJCaEw9kcsTA2HEdk4ufHeiv+RmJhIYGAgjo6ODBs2TG0sREREMGPGDGrUqIGTkxMzZ85k9erVf6tcY8aMUZc17d69OzExMWWmu379Oi4uLnc91+DBg3FwcMDExIQJEyaQn5/P2bNn1f2tW7emZ8+eGBkZYWFh8beup02bNnTt2hVjY2OGDBnCiRMn1H19+/bF1dUVIyMj+vXrR926dTl8+HCFzntnj8+dPSuxsbF8/vnnRERE0Lp1a0xMTPDx8eHrr79m586d/PDDD8DNxsgbb7xB165dsbS0ZO/eveTn5zNx4kQ8PDyoWbMmr7/+Orm5ueq5P/jgA1xcXHB1dWXFihVqTxBAeno6Q4cOxcnJCa1Wy5w5cygpKQH+6kWaOHEidnZ21K5dm8jIyApdryRJkiRJ0t3IRkIVc3d3V79rtVrSU1N4r5cvbhoLFMBNY8F7vXyZEtSDjRs3cu3aNfbv3090dDRz584FICkpCa1Wa3CepKSkv1UuZ2dn9Xv16tVL9V7c4uDgoL6NL8/ChQtp2LAhtra2aDQa0tPTSU1NVffffg/g713PneXOy8tT5zmEh4fz7LPPotFo0Gg0/PbbbwbluJs7e3zmzJljMFxnz5491KpVi5YtWxoc5+7uTqtWrfj+++/VbWvXriUkJITMzEzatGlDcHAw586dIyYmhvPnz5OYmMjs2bMB2LlzJ4sWLWL37t2cP3++1PCgt956i/T0dOLi4ti3bx/h4eGEhYWp+w8dOkT9+vVJTU1l8uTJjBgxQo1ifedwKUmSJEmSpIqSjYQqlpCQoH6Pj4/H1dWVnk3d+Ck4gIvzu/FTcAA9m7oZHOPn50evXr347bffAHB1deXy5culzgNgaWlJTk6Ouu+PP/4wONffjdTcoUMHNm3apL69vtP+/fv54IMP2LhxIzdu3ECv12Nra8vt8TfuLMPdrud+Xb58mZEjR/LZZ59x/fp19Ho9jRs3ptc5ZPkAACAASURBVKJxQO7s8Vm3bp3B/tTU1HJ7VFxcXAwaI6+88gr+/v4YGRlhZmbGsmXL+Oijj7C3t8fa2ppp06axfv164Oawr+HDh+Pj40P16tUJDQ1Vz1NcXMz69et57733sLa2xtPTkwkTJhj0umi1WkaOHImxsTFBQUEkJyeTkpJS0dsmSZIkSZJUJtlIqGKLFy/mypUrpKWlMXfuXPr161cqzY8//sjy5cu5evUqAL///jtbt26lVatWAAwYMIA5c+Zw7do1UlNTmT17NoMHDwagSZMmnDp1ipiYGPLy8gwqmQA1a9YkLi7uvss/fvx4MjIyCAoKUiv2iYmJjB8/nl9//ZXMzExMTExwcnKiqKiI2bNnk5GRcddz3u167ld2djaKouDk5ARAWFiY2siqiDt7fO7s2XB0dCy3RyU5ORlHR8cyz3Xt2jVycnJo3ry52sPRpUsXrl27BtzsVbk9/e3fU1NTKSwsLNXrkpj4V1yNO3tWgHJ7hSRJkiRJkipKNhKq2MCBA+nUqRNeXl7UqVOnzJgFGo2GrVu34uvri5WVFV26dOH//u//mDx5MgDTp0+nRYsWPPPMM/j6+tKsWTP1PPXq1WPGjBl06NCBunXrllrpaMSIEZw+fRqNRkPPnj0N9lVk6U97e3t+/vlnTE1Nee6557C2tubFF1/E1tYWb29vOnfuTJcuXahXrx5arRZzc/NSw4vudLfruV+NGjViwoQJtG7dmpo1a3Ly5En8/f0rfHxZPT63CwgIICEhodQch4SEBA4ePMiLL76obru958TR0RELCwtOnTqFXq9Hr9eTnp6uVuRdXFy4cuVKmeVwdHTE1NS0VK+Lm5thz5MkSZIkSdIDJ4R4Ij7NmzcXUuVotVrx/fffl9q+d+9eodPp1N+AiI2Nfajlunjx4kPNr3HjxiIhIUFcv35d+Pv7i6lTp5ZK98Ybbwhvb29x4MABUVRUJH777Tfh5+cnunbtqqYJCgoSISEhBseNGTNG9O3bV6SkpAghhLhy5YrYuXOnEEKIHTt2CGdnZ3H69GmRnZ0thg4danC/Bw0aJHr27CkyMjLEpUuXRP369cXy5cuFEEKEhYUJf39/g7we9rOSHj6dTmfw71OSJEl6+ICj4jGo/1blR/YkPASbjyfiP/8Hagdvx3/+D2w+nnjvg6SHqiI9Pp999hmvvfYagwcPVnt82rVrx9dff33Xc7///vt4e3vTqlUrbGxs6NChg7r600svvcSYMWNo3769mgbAzMwMgP/85z9YWlri5eVFmzZtGDhwIK+++uoDvnpJkiRJkiRDiqjgxM7HXYsWLcTtgbUeF5uPJzL1m5MGUZYtTI15r5dvqQnLD5unpyejRo1i9erVJCcn07NnT5YsWcLBgwcJDQ1VV9pRFIXY2Fi8vb3Jz89nypQpbNy4EYDAwEDef/99zMzM0Ol0jBkzht69e/PTTz/Rpk0bvv32W7p168aePXuYMGECMTExXLhwgZEjR3LixAkURaFz584sXrwYjUajlisqKkoN3vY0OXPmDI0bNyY/Px8TE5NHXRzpMXRrxSoZKE+SJOnRURTlFyFEi0ddjqokexKq2IJdZw0aCAC5hcUs2HW2nCMernst/XmnuwVC0+l06nH79u3Dy8uL6Oho9bdOpwNuDnGbOnUqSUlJnDlzhoSEBIMJ15cuXXokDYRH1eOzadMm8vPzuXHjBlOmTKF79+6ygSBJkiRJ0iMlGwlVLEmfW6ntD9u9lv68090Coel0Ovbt2wdAdHQ0U6dOVX/f3kjw9vamY8eOmJmZ4eTkxPjx49V0j8qtHp9EfS4CSNTnMvWbkw+lofDFF19Qo0YN6tSpg7GxMUuWLKnyPCVJkiRJku5Gvq6sYq4aCxLLaBC4aiweQWlKu9fSn3e6WyC01q1bc+7cOVJSUoiJiWHr1q3MnDmT1NRUDh8+TNu2bQFISUlh7Nix7N+/n8zMTEpKSrCzs6uCq6u4u/X4VPWwsJ07d1bp+SVJkiRJkipL9iRUsUmd62NhamywzcLUmEmd6z+iEhm619Kfd7pbILTq1avTvHlzPvnkExo3bky1atV4/vnnWbRoEXXq1FFjCUybNg1FUTh58iQZGRmsWbOmwkHPqsrj3uMjSZIkSZL0MMlGQhXr2dSN93r54qaxQAHcNBaPxaTlWyoS7O129wqEptPp+Oyzz9ShRe3atTP4DZCZmYmVlRW2trYkJiayYMGCqrm4SiivZ+dx6fGRJEmSJEl6mGQj4SHo2dSNn4IDuDi/Gz8FBzw2DQSo2NKft7tXIDSdTkdmZqY6tOjO3wAzZ87k2LFj2Nra0q1bN3r16lU1F1cJj3uPj1R1KhJU8EmyYsUKdYUkSZIkSSqPXAJVkv60+XgiC3adJUmfi6vGgkmd6z9WDTqpanh6erJixQo6dOhQal9UVFSp5YAbN27MiRMnMDK6+Y5l+vTpXLlyhVWrVj3wsgkh0Ol0vPjii8ycORO42Tv3xx9/UFhYyMmTJ6levXqlzrlixQrWrFlT5gpmK1as4ODBg6xYseJBFF+SJOmJ9TQsgSonLkuycvynnk3dnsrrlionKSmJ9evXM3DgwCrPS1EUVqxYQcuWLenTpw8+Pj4UFBQQFxfHrl27Kt1AKCoqqqKSSpIkSU8aOdzoPjys4Qnt2rWr8jd6m48nMmZRBIfmBj70pT8l6XFx5MgRGjVqhJ2dHcOHDycvL6/ctJMnT2bmzJnlVrgPHjzI888/j0ajoUmTJuob+7179+Lr66um69ixI35+furvF154gc2bN5c6X7169QgJCWHEiBGUlJRw/vx5HB0dad++PQB6vZ7Bgwfj5OSEp6cn7733nroQwIoVK2jbti1jxozB3t5ejWlyixCCcePGodPpyMjIqNjNkiRJkp4KspHwAEVFRRmM9VUUBV9fX0pKStRt06dPZ9iwYQ+/cOVYsOssBcX3H+ztboHXJOmfoqygglD233evXr2wsbEpc3hRYmIi3bp1Y/r06aSlpbFw4UJ69+7NtWvXaNWqFbGxsaSmplJYWMivv/5KUlISmZmZ5ObmcvToUV544YUyyzd+/HiEEPTp04eMjAy8vLzUfW+++SY5OTnExcXxww8/sHLlSsLDw9X9P//8Mw0bNuTatWtMmTJF3V5cXMyIESM4e/YsO3fuxMbGhtdee00ONZIkSZIA2UiocreGJlS1+x1GIJf+lKTKBRVUFIV3332Xd999l4KCAoN9a9asoWvXrnTt2hUjIyM6duxIixYt2LFjBxYWFvj5+REdHc0vv/xCkyZN8Pf356effuLgwYPUrVsXBweHMvM0Njbmyy+/ZNOmTXh7e6sRuQsLC9m4cSPz58/H2toaLy8vxo0bpwY4BPDw8OCNN97A2NgYC4ubq3UVFBTQr18/srKy2LJli7pdkiRJkm6RjYT7VNHhCfc7NOGWCxcu0LJlS2xsbHjllVdIS0sD4NKlSyiKwsqVK/Hw8CAgIACAvn374uzsjK2tLW3btuXUqVPquXbs2EGjRo2wtrbGzc2NhQsXlrnEZ8bRraSEvcmVK1e4ceMGL7/8Mk5OTtjZ2fHyyy9z5cqV+7llkvTYqmxQwa5du1KrVi2++OILg+2XL1/mv//9LxqNRv38+OOPJCcnAzdX+4qKiiI6OhqdTke7du3Yt2+fQUTy8vj4+ABgaWmpbrt69SrFxcWlAhwmJv41XPD2a7vl7NmzbN++nZkzZ2JqanrXfCVJkqSnk2wk3Keyhic8yKEJt4SHh/Pll1+SnJyMiYkJY8aMMTjHvn37OHPmDLt27QLgpZdeIjY2lqtXr9KsWTMGDRqkph0xYgRffPEFmZmZ/PbbbwQEBDCpc32qGf+19Kf+p3XknNrDknXbqFWrFiUlJQwfPpzLly8THx+PhYUFo0ePVtPfOcRKkv6JKhtUEGDu3LnMmzePnJwcdZu7uztDhgxBr9ern+zsbIKDg4HSjQSdTlfhRkJZatSogbGxcakAh25uf03AVxSl1HG+vr4sW7aMLl26cP78+UrnK0mSJD0FhBBPxKd58+biYdFqtWLJkiXq7+3btwsvL69S6QARGxsrtm/fLjw8PER+fr4ICQkRQUFBQggh5s+fLwYPHmxwTKdOncSqVauEEELodDoxZcoUdd+pU6eEqampKCoqEhcvXhSAuHDhQrnlvHHjhgCEXq8XQgjh7u4uli5dKtLT0w3Svbtso6hm4yBsWrwirLWNxZp9p8s95/Hjx4VGoyl3vyT902i1WtG4cWORkJAgrl+/Lvz9/cXUqVPLTHvr3/QtHTp0EPb29uq/6fj4eFGzZk2xc+dOUVRUJHJzc8XevXtFQkKCEEKIrKwsUa1aNeHk5CTy8/OFEEK4uroKCwsLkZKScs+yAqJly5ZCp9Op2/r16yd69+4tMjMzRVxcnPD29hZhYWFCCCGWL19ukPbObStWrBAeHh4iLi6uAndKkiRJugU4Kh6D+m9VfmRPwn2qzPCE+x2aUFY+hYWFpKamlrm/uLiY4OBg6tSpg42NDZ6engBq+q+//podO3ag1WrR6XQcOHAAgDZ1nTApyqX4zG7WLZ7PoLYN1XPm5OQwatQotFotNjY2tG3bFr1eT/Edk50l6Z+sskEFb5kzZ446BBBu/nvcsmUL8+bNw8nJCXd3dxYsWKAuXmBpaUmzZs3w8fGhWrVqALRu3RqtVkuNGjXuq+yff/451apVw9PTE51OR1BQEEOHDq3QsSNGjCA4OJiAgADi4+PvK39JkiTpCfWoWykP6vMoexJ27Nhx154EIYTYu3evcHZ2FuPGjVPfOs6bN0+89tpr5eZzZ0/C6dOnS/UkFBYWqvvDw8NFgwYNRFxcnCgpKVF7Em5/8ymEEAUFBWLRokWiVq1aatnc3NzE3r17hZOTk/jxxx/VtLNnzxY6nU4kJycLIW72JNyZryRJD49OpyvVOyBJkiQ9XMieBKk8ixcv5sqVK6SlpTF37lz69et31/Tt2rWjcePGfPXVV+q2wYMHs23bNnbt2kVxcTF5eXlERUUZTAxes2YNp0+fJicnhxkzZtCnTx+Mb5tDcLvMzEzMzMxwcHAgJyeHadOmqfsKCgqIiIggPT0dU1NTbGxs1Iixt5cxIiKCXr16cfjwYfWcFhYWaDQa0tLSmDVrVqXvlST9E2w+noj//B+oHbwd//k/yFghkiRJ0lNNNhLu0/0MT6js0ASAIUOGMGzYMJydncnLy+PTTz8t9/xDhw5Fq9Xi5uZGo0aNaNWqlcH+1atX4+npiY2NDUuXLiUiIqLUOTp27MiXX35J9+7dOXbsGG+//Ta5ubk4OjrSqlUrunTpUpHbI0n/KJuPJzL1m5Mk6nNlUEFJkiRJApSbPSb/fC1atBBHjx591MWQJOkfyH/+DySWERvETWPBT8EBj6BE5bu1mpgMYihJkvToKIryixCixaMuR1WSPQmSJD31ZFBBSZIkSTJk8qgL8E+2+XgiC3adJUmfi6vGgkmd69Ozqdu9D5Qk6bHiqrEosyehrGCDkiRJkvQ0kD0J90mOYX44PD092b1796MuhvSEm9S5PhamhgsCWJgaM6lz/UdUIkmSJEl6tGQj4T54enoSsngtuYWGsQJyC4tZsOvsQy/PpUuXUBSFoqKih553RYSGhjJ48GDgZjRYKyurBxJn4c5oz4qiGESPXbhwIS4uLpw6depv51XVyoqKKz08PZu68V4vX9w0FijcnIvwXi9f2TMoSZIkPbVkI+E+Xc8qKHP7hV8PqRVXT09PFEXB19fXYMWi6dOnM2zYsPvKNz4+HiMjI3bt2nVfx99y+PBhunbtikajwd7enpYtWxIWFnbf53v99dd5991375nOw8ODrKyscpdxrYz58+fzxx9/lLlvzpw5fPzxx+zbtw8fH5+/nZf05OvZ1I2fggO4OL8bPwUHyAaCJEmS9FSTjYT75GBVrcztjlZmpbYlJSWxfv169ffly5fZuXMnAKNGjeKNN95Q9xUWFmJpaVnmth9//BEPDw88PDz+ViX7wIEDBAQEoNPpOH/+PNevX2fJkiVERkZW+lxCCEpKSli6dCnvvPNOhY4ZNmwYq1atqnAeR44coVGjRtjZ2TF8+HDy8vLumn769OmsWLGC6Oho6tWrB8CNGzd4+eWXcXJyws7OjpdfftkgHkW7du1455138Pf3x9ramk6dOqmRqvPy8hg8eDAODg5oNBr8/PxISUkBICwsjIYNG2JtbY2Xl5dBVO3U1FRefvlltSH2wgsvGDQWJUmSJEmSHldV2khQFKWLoihnFUU5ryhKcBn7zRRF2fDn/kOKonj+ud1TUZRcRVFi/vwsrcpy3o9mFmkkr3yDhI/7kbr9Y0RRARamxvT3cy+VdvLkycycObPM4UBt27Zl+/bt+Pj4oNFoaNmyJc7Ozuzfvx+42RsxduxYcnNzCQgIYMCAAcTHx9O9e3esrKz44IMP1HNFRETg4eGBo6Mjc+fOLbfskyZNIigoiClTpuDo6IiiKDRv3pyNGzcCFatQh4SE4O/vT/Xq1YmLiyMpKYkePXpgb2+Pt7c3y5cvLzPvS5cu8dVXX6nDje5WOQe4du0aM2bM4MqVKzRs2JCYmBjmzJkDgLOzM8HBhn9WwcHBbNiwgejoaLy8vNTtJSUlDB8+nMuXLxMfH4+FhQWjR482OHbt2rWEhYVx9epVCgoKWLhwIQBfffUV6enpJCQkcP36dZYuXYqFxc0JrTVq1ODbb78lIyODsLAwxo0bx7FjxwD48MMPqVWrFteuXSMlJYV58+aVO6zoSVmKWJIkSZKkJ0RVhXIGjIELgBdQDTgBNLojzZvA0j+/9wc2/PndE/itMvk1b978XhG0HxitVit8fHzE8h2Hhd87m4SZW0NRq/0gsenYlVLpAFGnTh1hbGwsnn/+eZGbmysGDx4satasKYQQIioqSgDiv//9rygoKBAvvfSSsLOzE1qtVly7dk1otVrh4uIi/P39RU5Ojrh48aIAxM6dO4UQQoSFhQl3d3cBCCsrK/Hll1+KmJgYUa1aNTFr1izRoEEDYWVlJRo2bCh++eUXkZ2dLRRFEU2aNBG2traiUaNGYsuWLWqZg4KCxKuvviqaNWsmrKysRPPmzUWXLl3EK6+8IkpKSsTbb78tTE1NhaIoom7duuL48eOioKBA1KhRQzRr1kzk5uaK5cuXCyMjIzFq1Cjh5OQkrKysRKtWrYQQQi3/ihUrhBBC6HQ64eXlJc6ePStycnKETqcTU6ZMUcvj4OAgPvroI5GXlyfGjh0rateuLby8vMp8LoCwtrYWo0ePvuczPH78uNBoNOpvnU4n3n33XfX34sWLRefOnYUQQqxcuVK0bt1anDhx4p7nfeWVV8THH38shBDinXfeET169BCxsbH3PE6SKkqn0wmdTveoiyFJkvRUA46KKqpDPy6fquxJaAmcF0LECSEKgPXAK3ekeQX46s/v/wNeVP4hMzhHjx7Nay/5cXh2T75ZtpBqlw+UO4Y5LCyMiIgIjh49yqxZs9BqtWrk4ujoaKpXr46xsTGmpqYoioKRkRHe3t5qb4KTkxOdOnVS317fkp2dzZgxY9S5BFFRUbRs2ZImTZrg7u7Oxx9/THh4OBkZGWzduhUHBweuXr2KEIL27dtz9epV/vOf/zBo0CDOnv1rwvXmzZtZsmQJN27coH79+pSUlLBv3z6+++47oqOjadmyJSEhIWzZsoWaNWvyxx9/cPXqVQICAjA3N8fb2xshBAcOHCAxMZEePXpw5MgRbty4oeYRFBSkfh8+fDj16tXDwsKCwMBAYmJi1H1WVlbUrVsXMzMzQkNDuXjxIomJ5a8gtX79ev73v/8xc+ZMg+05OTmMGjUKrVaLjY0Nbdu2Ra/XG0ygdnZ2Vr9Xr16drKws4GbU686dO9O/f39cXV2ZPHkyhYWFAERGRtKqVSvs7e3RaDTs2LFD7QmZNGkS3t7eamTu+fPnl1tuSZIkSZKkx0lVNhLcgITbfl/5c1uZaYQQRUA64PDnvtqKohxXFGWfoigvlJWBoij/UhTlqKIoR69du/ZgS38P7u5/DSvSarUkJSWVm9bFxYV+/fpRp06dUsNwkpKSqF27NtHR0ZSUlHDkyBFq166Nh4cH0dHRCCGIi4tDp9OVeW4jIyPOnTsHQJMmTdRJumlpaXTq1Ak/Pz8URcHb2xutVktsbCwAL7/8MtWqVSMgIICXX36ZdevWqefs3r07K1eupE6dOmzatIndu3ej1+sxMjIiMzOTnJwcatWqRcOGDXFxcSEpKQkzMzPMzP6aj2FiYoKjoyOmpqbUrVsXExMTg4bI7cqrnBcXF3Pjxg2GDRuGjY0Nnp6ewM0hPuWpV68eu3fv5vPPPzeolH/44YecPXuWQ4cOkZGRQXR0NFCxYT6mpqbMnDmT06dP8/PPP/Ptt98SHh5Ofn4+vXv3ZuLEiaSkpKDX6+natat6Tmtraz788EPi4uLYunUrixYtYs+ePffMT5IkSZIk6VF7XCcuJwMeQoimwHhgraIoNncmEkIsE0K0EEK0cHJyeqgFTEj4q/0THx+Pq6vrPY+ZNGkS169fJycnR93m6uqKhYUF0dHRnDx5Ei8vL5KSknjhhReIjo6msLCQ/Px8nnvuuVLns7S0ZMOGDURERADQo0cPfv/9dwDy8/Mp656kpaVhZWXFpk2b1G1ardbg7fzFixfVCvWOHTtwdHQEoH379owePZrY2FgmTpzIv/71LzIyMnB1dSU/P5/8/Hz1HObm5gYNKRMTE7XyX1Fr164lJycHe3t7Tp06xfHjxwHo2rXrXY/z8fFh9+7dLFiwgI8//hiAzMxMLCws0Gg0pKWlMWvWrAqXY+/evZw8eZLi4mJsbGwwNTXFyMiIgoIC9T6bmJgQGRnJd999px737bffcv78eYQQ2NraYmxsjJHRw/knJ+NLSJIkSZL0d1RljSURuH0Wb60/t5WZRlEUE8AWuC6EyBdCXAcQQvzCzbkN9aqwrJW2ePFirly5QlpaGnPnzqVfv373PMbZ2RkLCwu++uordVtgYCC//fYbMTExbN26FXNzc8zMzBg4cCAJCQnk5uZSv359zM3NDc4VFxcHQOfOnVmzZg0A9evXZ+TIkQCYmZlRVu+Kq6sr1apVIywsjAULFnD9+nXi4+MxNjamf//+ABQUFKgV6oyMDDIyMtTjx4wZQ/PmzZk1axbnzp1jwYIFuLu7U6NGDfbu3UteXh4XLlwgOztbjY1wvzIzM1EUhf79+9OxY0caNGgAwL///e97HtukSRN27drFrFmzWLp0KW+//Ta5ubk4OjrSqlUrdbhXRfzxxx/06dMHGxsbGjZsiE6nY8iQIVhbW/Ppp58SGBiInZ0da9eupUePHupxsbGxdOjQASsrK1q3bs2bb75J+/btK38jHqBbsSVuxau49VEUBUtLS/X3raFuT7rff/9d/buSJEmSJOkvJlV47iNAXUVRanOzMdAfGHhHmq1AEHAA6AP8IIQQiqI4AWlCiGJFUbyAukBcFZa10gYOHEinTp1ISkrilVdeYfr06eWmTU5Oxt7enrlz5xIYGGjQSKhfvz5r164lMDCQmTNn0qhRI7Zt24aZmRnPPfccO3bswNfXt9Q5582bx5QpU+jVqxeTJ08Gbo7fv/Wm2sXFhe+++45ffvmFZs2aceHCBUxNTXnuueewtbWlf//+7N69m9DQUHJycvD19WXixIn88MMPtGzZkhMnTuDo6IhGo8Hc3Jy8vDyOHDmCkZERJSUlmJmZYW5urubXtm1bYmJicHV1xdzcHBsbGzp06PC37vHQoUPZtWsXixYtwt7enuXLlxMUFFRqbsYtdw4datGihcE8iKioKIP9o0aNKnffsGHD1FgWAwYMYMCAAWXm+e9//7vcRsu4ceMYN25cmfsetVvxKm5RFIUTJ07g7e39CEslSZIkSdJjoypnRQNdgXPc7AkI+XPbbKDHn9/Ngf8C54HDgNef23sDp4AY4BjQ/V55PczVjSpKq9WKefPmiYYNGwpbW1sxdOhQkZ2dXWba/v37C0VRRGpqqrrt/fffN1jJSIi/VgcqLCwUSUlJom3btsLGxkbY2toKnU4nTp06paZdsmSJqFevnrC0tBQ+Pj7i2LFjQgghfvvtN/W4hg0bim+++UY9JigoSISEhKi/9+7dK9zc3IQQQuzevVv4+voKS0tL4eDgIAYOHCgyMzNLHXf7Mbffi++///6+7qMQ/9/enYdXVd37H38vQoCQhCRAGDIQINRYAmoQkZlAJQwCRlBQZFSpXA2IV1EoOLQNiqJc5WpbhwpFQNDLICCQVixD7Y8ZBNELApJADjInBElIIOv3R07OzckAgZCRz+t5eDxn7bXX/u7TXdjftfday9qlO47amv4NbcMh8bbja2sLzCR1rfKfZ3G35detWzf74YcfliiW0pD32vP397ejRo2y6enp9p///GehM+MABWZhunDhgn366adtSEiIbdiwoY2Li7MZGRnWWmtXr15tw8PD7bRp02z9+vVtUFCQnTdvnmvfIUOG2KefftrGxMRYHx8f27FjR3v48GHX9j179tju3btbf39/e+utt9qlS5e6ti1btsxGRERYHx8fGxISYt955x1rrbXHjh2zvXr1sn5+frZu3bq2e/furn2SkpLsgAEDbL169WyzZs3sn//8Z9e2S5cu2T/84Q+2WbNmruv27Nmz1lprf/jhBxsREVGCX7rsaXYjEZHyx00wu1G5B3Cj/lTEJEFujKU7jtpbp662HnUa2AZD4m3YCyvtrVNXuxKF/De+2dnZ9o033rAtWrSwtWrVsqGhoXbSpEmuG1xrq16SMHLkSDt79mzX99xpepOSkuzp06dtx44dr3hOhSUJY8eOtYMGDbJnz561KSkpNiYmxr7yyivW2pwkoXr16jY+Pt5mZmbaJUuWWB8fmAW3tQAAIABJREFUH1fSOGTIEBsYGGi3b99uMzMz7aBBg+zIkSOttdampqbaRo0a2Xnz5tlLly7ZLVu22ICAANfx69atazdv3myttfbUqVOu5HbChAl2/PjxNisry168eNGuX7/eWpuTBLRq1cpOnz7dXrx40e7bt8+GhobadevWWWutnT59uu3cubNNTk626enpduTIkXbUqFEl/MXLj5IEEZHydzMkCRV14HKlsGxnMp2mf02zSV/SafrXLNtZcGrO4tSRK5uRsI/0rMtuZelZl5mRUPhsSePHj+eDDz5g7ty5pKWlsXr1atauXcvgwYPLItwKIy4ujtDQUOrWrcuUKVPcZrC6mkuXLvHXv/6Vd955B39/f/z8/Jg0aZLbyuG1a9dm8uTJeHp6cv/992OM4cCBA67tgwcPpk2bNnh6ejJ06FDX1LZLly6lVatWPPLII3h4eHDXXXfRv39/Fi9eDICHhwd79+4lLS2NevXqERUVBeTMMuVwOEhKSqJGjRp07doVgH/9619kZGTwwgsvUKNGDW655RZGjx7tivUvf/kL06dPd70K9/LLL7No0aKcXhIREREplJKE67RsZzKTl+whOSUdCySnpDN5yR63JKA4deTqHCnprs+Zx/bj+Chnpevd818jIyPDre6PP/7In/70J+bPn0+HDh2oXr06kZGRLF68mDVr1vD1118XaD8tLY3u3bszfvz4AjeOV1t9GiAxMbHIFaOXL1/uWk07OjqaH374wbWtadOmzJgxg9tuuw1vb28ee+wxjh8/Tp8+ffD19eWee+5xG1Px4IMP0qhRI/z8/OjatSt79+694u92LdP05udwOMjKynLF7u/vT2xsLCdOnHDVCQwMdJutKe/0tVD01LaJiYls2LDB1a6/vz+LFy/m2LFjAHzxxRcsXryYJk2a0KNHD7Zu3QrAlClTCAoKonv37rRo0YKZM2e62jt8+LBbezNnzuTnn3/GWsuRI0fo27eva1tUVBTZ2dmcPn262L+HiIjIzUZJwnUqTu/2tfaAS+GC/P9voPIv36+jweA/EPTER5i0Y8THxxMdHe0aeLx27VpCQkJo166dWxuhoaG0b9+ef/zjH27lp0+f5je/+Q2dOnVi1qxZ5F/LLzs7m9GjR5OYmEhSUhJeXl7ExcW51VmwYAGzZ8/mxIkTZGZm8uabbwKwf/9+Hn74Yd5++21OnjxJ37596d+/P5mZma59Fy9ezD/+8Q/279/PihUr6NOnD6+++ionT54kOzubWbNmuer26dOHH3/8kRMnTtCmTRseeeQR17Y5c+a4Blrnup5penM1btyY6tWrc/DgQVJSUkhJSSE1NfWG3FiHhoYSExPjajclJYXz58+7pqvt0KEDK1eu5Pjx48TExDB0aM58B35+frzzzjskJiayePFi4uPj+eabbwgNDeXWW291ay8tLY2lS5dijCE4OJivv/7abXtGRoZral8REREpSEnCdcrbu11UeXHqyNVN7BWBl6cHAL5t+lG9TiA+dfx5+tnnC7xCc+rUKRo3blxoO40bN3br5Xc4HHTr1o0HH3yQ+Pj4QvepV68egwYNonbt2vj6+jJlyhTWr1/vVqeoFaMXLVrEvffeS8+ePfH09OS5554jPT2df//73659x40bR8OGDQkODqZLly7cfffdREVFUatWLe6//37X2hAAjz76KL6+vq7Vp7/99ltSU1OL/N2uZ5reXJ6enjz66KM8/fTTnDp1ytUjnz/Juh6xsbHs3LmTRYsWkZWVRWZmJps2bWL//v388ssvLFy4kHPnzuHp6Ymvr6/racXy5cs5dOgQ1rqvO9G5c2cA3n77bTIyMrh06RK7d+9mx44dAIwdO5ZJkya5kqYTJ06wYsWKEp+HiIhIVaYk4Trl7d0uqrw4deTqYqOCeW1ga6pXM1SvE0iwvxevDWzNQz3uLPAKTf369V2vreR37Ngxt97jL7/8kvT0dMaOHVvksS9cuMATTzxBWFgYderUoWvXrqSkpHD58v89ISrqtRqHw0FYWJhrW7Vq1QgNDXVbuK5hw4auz15eXgW+5119etKkSYSHh7utPp036ckvd5re5s2bEx4efsVpegvz9ttvExQURNu2bfHz86N3795uYw6uV0BAAAkJCcyePZvGjRsTFBTE1KlTycrKAuDjjz8mLCwMPz8/5s6dy9y5cwH44Ycf6N69O76+vnTt2pXnnnuODh064OnpyapVq/j3v/9NWFgYgYGB/Md//Ifrt3v++ee555576NGjB76+vnTs2NGVQIiIiEjhSnOdhCptYq8IJi/Z4/Y6kZenBxN7RVxTHSme2KhgGvnVYtJvGjF2bA8AVq/eXeAVmh49evDkk0+yZcsWt1eOjhw5wqZNm3jxxRddZWPGjOHs2bP07duXNWvW4O3tXeC4b731lmv16UaNGrFr1y6ioqKKNeg1KCiIPXv2uL7n9sYHBwdf8/kvWLCAL774gq+++oqmTZuSmppKQEBAkXEcPnwYgMmTJxer/cLa8fLy4o033uCNN94osK2whOHnn392fc47wLmw+pGRkaxZs6bQWPKuWp3XCy+8wAsvvFDottDQUD777LNCt3l4eFxxXxERESlITxKuU27vdrC/FwZcvduxUcHXVEeuzdVeobnlllsYO3YsjzzyCJs2beLy5cvs3buXQYMGcc899xRY4O3dd98lIiKC/v37k55e8DWwtLQ01+rTZ86c4fe//32xYx08eDBffvkla9euJSsri7feeouaNWvSsWPHaz7vtLQ0atasSb169bhw4QK/+93vrrkNERERkeJSklACsVHBfDOpBz9Nv5dvJvUo9Oa/OHWk+IrzCs27777L448/zrBhw/Dx8aF3795ER0e7ptjMyxjDBx98QEhICPfdd1+B2ZImTJhAeno69evXp3379vTu3bvYsUZERDBv3jzGjRtH/fr1WbFiBStWrKBGjRrXfN4jRowgLCyM4OBgWrZsSfv27Yu1n6bgFRERkethqspc4W3btrXbtm0r7zBEKozcKXjzv+6mp1mVW3R0NIBrRi8RESl7xpjt1tq25R1HadKYBKl0lu1MZkbCPhwp6QT5ezGxV4RuegtxpSl49XuJiIjIlShJkEolf+947gJ1gG5889EUvCIiInK9NCZBKhUtUFd8moJXRERErpeSBKlU1DtefHkXoculKXhFRESkOJQkSKWi3vHi0xS8IiIicr00JkEqFS1Qd21io4KVFIiIiMg1U5IglUruDa9mNxIREREpPUoSpNJR77iIiIhI6dKYBBERERERcaMkQURERERE3ChJEClnTZs25auvvirvMG6o6OhoPvroo1I9RlJSEj4+Ply+fPnqlUVEROSaKEkQqaDWrVtHdHS067sxBm9vb3x8fAgODuY///M/b+ob5CZNmnD+/Hk8PDyuWjf/bykiIiJXpiRBXMqiR3vUqFFMnToVgI0bNxIRoalLr8W3337L+fPnWbt2LQsWLODDDz8s75BERESkClKSIFdVWI/2gQMHStxuly5d2LdvX4nbuZrK0Iu8detWWrZsSUBAAKNHjyYjI+OK9W+99Va6dOnCd999B4DD4WDQoEEEBgbSrFkzZs2a5ar7yiuvMHjwYEaMGIGvry+RkZFs27bNtb1p06a8+eab3Hbbbfj5+TFkyBDX8U+dOkW/fv3w9/enbt26dOnShezsbGbMmMGgQYPcYho/fjxPP/20W9nFixfx9/d3xQlw8uRJvLy8OHHiBGfPnqVfv34EBgYSEBBAv379OHr0qKtudHQ0L774Ip06dcLX15eYmBhOnToFwOHDhzHGcOnSJQBmz57Nr3/9a3x9fWnevDnvv/9+sX9/ERERcackQaQCmD9/PgkJCRw8eJD9+/cTHx9PdHQ069atK7T+999/z8aNG4mKiiI7O5v+/ftz++23k5yczNq1a3n77bdJSEhw1V++fDkPPfQQKSkpDBgwgLi4OLf2PvvsM9asWcNPP/3E7t27mTNnDgBvvfUWISEhnDx5kuPHj/Pqq69ijGHYsGGsWbOGlJQUAC5dusTChQsZMWKEW7s1a9Zk4MCBfPrpp27H6tatGw0aNCA7O5vRo0eTmJhIUlISXl5eBWJbsGABs2fP5sSJE2RmZvLmm28W+ps0aNCAlStXcu7cOWbPns0zzzzDjh07AK74W4qIiEhBShLEzbX2aF+tl3rnzp20adMGX19ftx5qyOnhDwkJcX2fPn064eHh+Pr60rJlS5YuXeraNmfOHDp37sxzzz1HQEAAzZo1Y/Xq1a7tlb0XOS4ujtDQUOrWrcuUKVPcbqrzatOmDQEBAfTv35/HH3+c0aNHs3XrVk6ePMlLL71EjRo1aN68OWPGjGHhwoWu/Tp37kzfvn3x8PBg+PDhfPvtt27tjh8/nqCgIOrWrUv//v3ZtWsXAJ6enhw7dozExEQ8PT3p0qULxhgaN25M165d+fzzzwFYs2YN9evX58477ywQ89ChQ91iWbBgAUOHDgWgXr16DBo0iNq1a+Pr68uUKVNYv3692/6jR4/mlltuwcvLi8GDB7tiy+/ee+8lPDwcYwzdunUjJiaGjRs3Xu2nFxERkUIoSRA319qjDUX3UmdmZhIbG8vw4cM5c+YMDz74IIsXLy6ynfDwcDZu3Ehqaiovv/wyw4YN49ixY67tmzdvJiIiglOnTvH888/z2GOPYa0FKn8vcmhoqOtzWFgYDoej0Ho7duzg7NmzHDx4kPj4eKpVq0ZiYiIOhwN/f3/Xn1dffZXjx4+79mvUqJHrc+3atcnIyHC9plPY9vPnzwMwceJEWrRoQUxMDM2bN2f69OmueiNHjmTevHkAzJs3j+HDhxcac/fu3blw4QKbN2/m8OHD7Nq1i/vvvx+ACxcu8MQTTxAWFkadOnXo2rUrKSkpbgOyi4otv9WrV9O+fXvq1q2Lv78/q1atcr2aJCIiItdGSYK4KW6Pdl5F9VJv2rSJrKwsJkyYgKenJw888AB33XVXke08+OCDBAUFUa1aNYYMGcKvfvUrtmzZ4toeFhbGmDFj8PDwYOTIkRw7dsx1I1zZe5GPHDni+pyUlERQUFCx9w0NDaVZs2akpKS4/qSlpbFq1aoSx+Xr68tbb73FoUOHWL58OTNnzmTt2rUAxMbGsnv3br777jtWrlzJI488UmgbHh4eDB48mE8//ZRPP/2Ufv364evrC+S8zrRv3z42b97MuXPn2LBhA4Ar+SuuixcvMmjQIJ577jmOHz9OSkoKffv2veZ2REREJIeSBHFT3B7tvIrqpXY4HAQHB2OMcWuzKHPnzuWOO+5w9YZ/9913bj3B+Y8DuHqVK3sv8nvvvcfRo0c5c+YM06ZNY8iQIcXet127dvj6+vL666+Tnp7O5cuX+e6779i6dWuJ41q5ciUHDhzAWoufnx8eHh5Uq5bz10atWrV44IEHGDp0KO3ataNJkyZFtjN06FAWLVrE/PnzXa8aAaSlpeHl5YW/vz9nzpzh97///XXFmZmZycWLFwkMDKR69eqsXr2av//979fVloiIiChJkHxK0qOdX+PGjUlOTnbrzU1KSiq0bmJiImPGjOHdd9/l9OnTpKSk0KpVq2L1BFeFXuShQ4e6XukJDw93TRNbHB4eHqxcuZJdu3bRrFkz6tevz+OPP05qamqJ4/rxxx+555578PHxoUOHDjz55JN0797dtX3kyJHs2bOnyFeNct199914e3vjcDjo06ePq3zChAmkp6dTv3592rdvT+/eva8rTl9fX2bNmsXgwYMJCAhgwYIFDBgw4LraEhEREahe3gFIxfLee+/Rr18/ateufc092vl16NCB6tWrM2vWLJ588klWrFjBli1b3G4yc/3yyy8YYwgMDARyBiLnnTbzSorqRW7VqtV1x16WDh8+DMDkyZOvWO9KSU9QUFCRr4a98sorbt+bNm3q1lbu8Qur/8wzz/DMM88UedwmTZrg5eVVYDrUwsaAFDZtblBQUIG6TzzxRJHtjBo1ilGjRhV6Hk899RRPPfVUkbGKiIhI8elJgrgpSY92fjVq1GDJkiXMmTOHunXrsmjRIgYOHFho3ZYtW/Lss8/SoUMHGjZsyJ49e+jUqVOxjlMVepGX7Uym0/SvaTbpSzpN/5plO5PLO6Srys7OZubMmTz00EPUqVOnvMMRERGRG8hUplcyrqRt27Y279SbIpXFsp3JTF6yh/Ss/5vRx8vTg9cGtiY2KrgcIyvaL7/8QsOGDQkLC2PNmjVuY1mkdOUuDFjRZ+wSEanKjDHbrbVtyzuO0qTXjaSAZTuTmZGwD0dKOkH+XkzsFVFhb1arghkJ+9wSBID0rMvMSNhXYX93b2/vIqciFRERkcpPSYK4yd+rnZySzuQlewAq7A1rZedISb+mchEREZHSpjEJ4uZKvdpSOoL8va6pXArXtGlTvvrqqxvWXp8+ffjb3/52w9orqYoWj4iIVG1KEsSNerXL3sReEXh5eriVeXl6MLFXRDlFVLWsW7fO9R4/gDGGBg0auK04nZWVRYMGDdzW9Fi9ejUjR44sy1CvKG88P//8s2uWJxERkdKgJEHcqFe77MVGBfPawNYE+3thgGB/rwo9aLkqCAgIYPXq1a7vq1evJiAgoBwjKpq1luzs7PIOQ0REbjJKEsSNerXLR2xUMN9M6sFP0+/lm0k9lCBcp61bt9KyZUsCAgIYPXo0GRkZhdYbPnw4c+fOdX2fO3cuI0aMcKsTHR3NRx99BOSs8dCtWzf8/PyoX7++a/0Qay3PPPMMDRo0oE6dOrRu3dq1vkdqaiojRowgMDCQsLAw4uPjXTf7r7zyCsOGDXMd6/DhwxhjXE83oqOjmTJlCp06daJ27docOnTILR4REZHSpiRB3KhXWyqz+fPnk5CQwMGDB9m/fz/x8fFER0cXmC40NjaWDRs2kJKSwtmzZ9m4cSP33Xdfke2++OKLxMTEcPbsWY4ePcq4ceMA+Pvf/86GDRvYv38/qampfPbZZ9SrVw+AcePGkZqayqFDh1i/fj1z585l9uzZxT6XTz75hA8++IC0tDTCwsLctjVq1Ig5c+YUuy0REZFrpdmNpIDYqGAlBVIpxcXFudZsmDJlCuPGjSM+Pr5AvVq1atG/f38WLVqEtZYBAwZQq1atItv19PQkMTERh8NBSEgInTt3dpWnpaXxv//7v7Rr145f//rXAFy+fJmFCxeya9cufH198fX15dlnn+WTTz7hscceK9a5jBo1isjIyGv9CURERG4IPUkQkSoj76JuYWFhOByOIuuOGDGCuXPnFvqqUX5vvPEG1lratWtHZGQkH3/8MQA9evQgLi6Op556igYNGvDb3/6Wc+fOcerUKbKystyeAISFhZGcXPyVtLVAnYiIlCclCSJSZRw5csT1OSkpiaCgoCLrdunShWPHjnH8+HHXk4GiNGrUiA8//BCHw8H777/Pk08+yYEDBwAYP34827dv5/vvv2f//v3MmDGD+vXru54+5I0nODjnCZ23tzcXLlxwbfv5558LHDPvTEsiIiJlTUmCiFQZ7733HkePHuXMmTNMmzbNNcC4MMYYVqxYwfLly696Q/75559z9OhRIGdmJGMM1apVY+vWrWzevJmsrCy8vb2pVasW1apVw8PDg8GDBzNlyhTS0tJITExk5syZrsHKd9xxBxs2bCApKYnU1FRee+21G/cjiIiI3ABKEkSkyhg6dCgxMTE0b96c8PBwpk6desX6kZGRxXrvf+vWrdx99934+PgwYMAA3nnnHZo3b865c+cYM2YMAQEBhIWFUa9ePSZOnAjAf//3f+Pt7U3z5s3p3LkzQ4cO5dFHHwWgZ8+eDBkyhNtuu40777yTfv36lfzkRUREbiBjrS3vGG6Itm3b2m3btpV3GCIipSp3Ybj8MzaJiEjZMcZst9a2Le84SpNmNxKRKmXZzmRmJOzDkZJOkL8XE3tFaLYuERGRa6QkQUSqjGU7k5m8ZA/pWZcBSE5JZ/KSPQBKFERERK6BxiSISJUxI2GfK0HIlZ51mRkJ+8opIhERkcpJSYKIVBmOlPRrKhcREZHCKUkQkSojyN/rmspFRESkcEoSRKTKmNgrAi9PD7cyL08PJvaKKKeIREREKicNXBaRKiN3cLJmNxIRESkZJQkiUqXERgUrKRARESkhvW4kIiIiIpVC06ZN+eqrr8o7jHJljOlijCn1afuUJIiIiIhIpbdu3TrXqvQAxhhat25Ndna2q2zq1KmMGjWqVOMwxlhjzAljTPU8ZZ7OMlvS9q21G621JRpsZ4xpaow5fKU6ShJEREREpEpyOBwsXLiwPA59FuiT53sfZ1mloSRBRERERCqNrVu30rJlSwICAhg9ejQZGRlF1n3++ed5+eWXuXTpUqHbN23aRMeOHfH39+f2229n3bp1APzzn/+kdevWrno9e/bkrrvuyrtrhDEm9gphfgKMyPN9BDA3bwVjjJ8x5q/GmGPGmGRjTLwxxsO57c/GmMV56r5ujFlrckQbY47m2RZqjFlijDlpjDltjHnXWV7NGDPVGJPofIox1xjjd4WY3ShJEBEREZFKY/78+SQkJHDw4EH2799PfHw8ANHR0a6b/FwDBw6kTp06zJkzp0A7ycnJ3HvvvUydOpUzZ87w5ptvMmjQIE6ePEn79u358ccfOXXqFFlZWezevRuHw0FaWhrp6ekA3sDGK4S5DOhqjPE3xgQAXYAv8tWZA1wCWgBRQAzwuHPbs0BrY8woY0wX4DFgpLXW7XUlZ1KxEkgEmgLBQO6jk1HOP92B5oAP8C6AtfawtbbpFeJXkiAiIiIilUdcXByhoaHUrVuXKVOm8OmnnxZZ1xjDH//4R/74xz+SmZnptm3evHn07duXvn37Uq1aNXr27Enbtm1ZtWoVXl5e3HXXXWzYsIHt27dz++2306lTJ7755hs2bdoEkGGtPX2FMDOAFcAQ55/lzrLcuBoCfYEJ1tpfrLUngP8CHgKw1l4AhgMzgXnAOGvtUQpqBwQBE53tZFhr/+Xc9ggw01p7yFp7HpgMPJR3rMSVaApUEREREak0QkNDXZ/DwsJwOBxXrN+3b19CQkJ4//333coTExP5/PPPWbFihassKyuL7t27A9CtWzfWrVtHSEgI3bp1IyAggPXr11OzZk2AtGKEOhd4DTDAC/m2hQGewDFjTG5ZNeBI7hdr7WZjzCGgAfBZEccIBRKttYW9TxVEzhMG1ymTc+/fEEi+WvBKEkRERESk0jhyxHUfTVJSEkFBQVfdZ9q0aTz88MM8/PDDrrLQ0FCGDx/Ohx9+WOg+3bp149lnn6VJkyZMmjSJgIAAxowZk5sknC9GqBuBxoAF/gWE5z0N4CJQv4gbfIwxTwE1AQfwPDkJR35HgCbGmOqFtOMgJxnJ1YSc15uOFyN2vW4kIiIiIpXHe++9x9GjRzlz5gzTpk1jyJAhV90nOjqaVq1a8be//c1VNmzYMFasWEFCQgKXL18mIyODdevWcfRozls9HTt2ZN++fWzZsoV27doRGRlJYmIimzdvhmI8SXCOH+gPDMg/lsBaewz4O/CWMaaOc5BxuDGmG4Ax5hYgHhhGzmtHzxtj7ijkMFuAY8B0Y4y3MaaWMaaTc9unwDPGmGbGGB/gVWBRUUlJfkoSRERERKTSGDp0KDExMTRv3pzw8HCmTp1arP3i4+M5c+aM63toaChffPEFr776KoGBgYSGhjJjxgzXugre3t60adOGyMhIatSoAUCHDh0ICwuDnB75q7LW7rXW7i1i8wigBvA9OdOj/g/Q2DlmYB7wurX2W2vtj8DvgE+MMTXztX+ZnESkBZAEHCVnDATAx+TMsrQB+ImcMRHjihM3gMmX2FRabdu2tdu2bSvvMERESlXuQkH5Z/AQEZGyY4zZbq1tW95xlCaNSRARERGRSmXZzmRmJOzDkZJOkL8XE3tFEBsVXN5hVSlKEkRERESk0li2M5nJS/aQnnUZgOSUdCYv2QOgROEG0pgEEREREak0ZiTscyUIudKzLjMjYV85RVQ1KUkQERERkUrDkZJ+TeVyfUo1STDG9DbG7DPGHDDGTCpke01jzCLn9s3GmKZ5tk12lu8zxvQqzThFREREpHII8ve6pnK5PqWWJBhjPID3gD5AS+BhY0zLfNUeA85aa1uQsxT16859W5KzLHUk0Bv4k7M9EREREbmJTewVgZen+22hl6cHE3tFlFNEVVNpPkloBxyw1h6y1mYCC4H78tW5D8hd1eJ/gN+YnLWp7wMWWmsvWmt/Ag442xMRERGRm1hsVDCvDWxNsL8XBgj29+K1ga01aPkGK83ZjYLJWSo611Hg7qLqWGsvGWNSgXrO8k359i3wv7wx5rfAbwGaNGlywwIXERERkYorNipYSUEpq9QDl621H1hr21pr2wYGBpZ3OCIiIiIiVUJpJgnJQGie7yHOskLrOJeg9gNOF3NfEREREREpBaWZJGwFfmWMaWaMqUHOQOTl+eosB0Y6Pz8AfG2ttc7yh5yzHzUDfgVsKcVYRURERETEqdTGJDjHGMQBCYAH8LG1dq8x5g/ANmvtcuCvwCfGmAPAGXISCZz1PgO+By4BT1lrLxd6IBERERERuaFKc+Ay1tpVwKp8ZS/l+ZwBPFjEvtOAaaUZn4iIiIiIFFSpBy6LiIiIiMiNpyRBRERERETcKEkQERERERE3ShJERERERMSNkgQREREREXGjJEFERERERNwoSRARERERETdKEkRERERExI2SBBERERERcaMkQURERERE3BhrbXnHcEMYY04CieV0+PrAqXI6tlQNuobkRtB1JCWla0hK6ma5hsKstYHlHURpqjJJQnkyxmyz1rYt7zik8tI1JDeCriMpKV1DUlK6hqoOvW4kIiIiIiJulCSIiIiIiIgbJQk3xgflHYBUerqG5EbQdSQlpWtISkrXUBWhMQkqh/iMAAAFeElEQVQiIiIiIuJGTxJERERERMSNkgQREREREXGjJOEKjDG9jTH7jDEHjDGTCtle0xizyLl9szGmaZ5tk53l+4wxvcoybqlYrvc6MsbUM8b80xhz3hjzblnHLRVHCa6hnsaY7caYPc7/9ijr2KXiKMF11M4Ys8v551tjzP1lHbtUDCW5L3Jub+L8N+25sopZrp+ShCIYYzyA94A+QEvgYWNMy3zVHgPOWmtbAP8FvO7ctyXwEBAJ9Ab+5GxPbjIluY6ADOBFQH+Z3sRKeA2dAvpba1sDI4FPyiZqqWhKeB19B7S11t5Bzr9p7xtjqpdN5FJRlPAayjUTWF3ascqNoSShaO2AA9baQ9baTGAhcF++OvcBf3N+/h/gN8YY4yxfaK29aK39CTjgbE9uPtd9HVlrf7HW/oucZEFuXiW5hnZaax3O8r2AlzGmZplELRVNSa6jC9baS87yWoBmPLk5leS+CGNMLPATOX8XSSWgJKFowcCRPN+POssKreP8CzQVqFfMfeXmUJLrSARu3DU0CNhhrb1YSnFKxVai68gYc7cxZi+wBxibJ2mQm8d1X0PGGB/gBeD3ZRCn3CBKEkREqjhjTCQ5j/2fKO9YpHKy1m621kYCdwGTjTG1yjsmqVReAf7LWnu+vAOR4lOSULRkIDTP9xBnWaF1nO9n+gGni7mv3BxKch2JQAmvIWNMCLAUGGGtPVjq0UpFdUP+LrLW/gCcB1qVWqRSUZXkGrobeMMYcxiYAPzOGBNX2gFLyShJKNpW4FfGmGbGmBrkDERenq/OcnIGAwI8AHxtc1anWw485Bzl3wz4FbCljOKWiqUk15EIlOAaMsb4A18Ck6y135RZxFIRleQ6apY7UNkYEwbcChwum7ClArnua8ha28Va29Ra2xR4G3jVWqtZ+yo4zU5QBGvtJWeWmwB4AB9ba/caY/4AbLPWLgf+CnxijDkAnCHn/zA4630GfA9cAp6y1l4ulxORclWS6wjA2etSB6jhHPQVY639vqzPQ8pPCa+hOKAF8JIx5iVnWYy19kTZnoWUtxJeR52BScaYLCAbeNJae6rsz0LKU0n/PZPKx6jDUkRERERE8tLrRiIiIiIi4kZJgoiIiIiIuFGSICIiIiIibpQkiIiIiIiIGyUJIiIiIiLiRkmCiMhNzhgz1hgzopDypsaY75yfexpjthtj9jj/26PsIxURkbKidRJERKoYY4whZ4rr7OLUt9b+pRjVTgH9rbUOY0wrcuZKDy5BmCIiUoHpSYKISBXg7PX/wRjzJ2AHMNwY8/+MMTuMMZ8bY3yc9aYbY743xuw2xrzpLHvFGPOc8/OdxphvjTH/D3gqt31r7U5rrcP5dS/gZYypWaYnKSIiZUZJgohI1REBzAV6Ao8B91hr2wDbgP80xtQD7gcirbW3AfGFtDEbGGet7XCF4wwCdlhrL97Q6EVEpMJQkiAiUnUkWms3Ae2BlsA3xphdwEggDEgFMoC/GmMGAhfy7myM8Qf8rbUbnEWf5D+AMSYSeB14otTOQkREyp3GJIiIVB2/OP9rgH9Yax/OX8EY0w74DfAQEAcUewCyMSYEWAqMsNYeLHm4IiJSUelJgohI1bMJ6GSMaQFgjPE2xtziHJfgZ61dBUwA7si7k7U2BUgxxnR2Fj2Su835lOFLYJK19puyOAkRESk/epIgIlLFWGtPGmNGAZ/mGVw8FUgDvjDG1CLnacMzhew+GvjYGHOBnBmMcsUBLYCXjDEvOctirLUnSuMcRESkfBlrbXnHICIiIiIiFYheNxIRERERETdKEkRERERExI2SBBERERERcaMkQURERERE3ChJEBERERERN0oSRERERETEjZIEERERERFx8/8BUL2fGW9xoSkAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics import utils\n", "fig, ax = plt.subplots(figsize=(12,8))\n", "ax.plot(resid2[idx], hat_matrix_diag, 'o')\n", "ax = utils.annotate_axes(range(nobs), labels=rob_crime_model.model.data.row_labels[idx], \n", " points=lzip(resid2[idx], hat_matrix_diag), offset_points=[(-5,5)]*nobs,\n", " size=\"large\", ax=ax)\n", "ax.set_xlabel(\"resid2\")\n", "ax.set_ylabel(\"leverage\")\n", "ylim = ax.get_ylim()\n", "ax.vlines(rm, *ylim)\n", "xlim = ax.get_xlim()\n", "ax.hlines(hm, *xlim)\n", "ax.margins(0,0)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmpscge7xpu/555ef5ea-02e2-4640-87b3-ffcea5c96356 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Robust Linear Models" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation\n", "\n", "Load data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/datasets/utils.py:100: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n", " exog = np.column_stack(data[field] for field in exog_name)\n" ] } ], "source": [ "data = sm.datasets.stackloss.load()\n", "data.exog = sm.add_constant(data.exog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Huber's T norm with the (default) median absolute deviation scaling" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-41.02649835 0.82938433 0.92606597 -0.12784672]\n", "[9.79189854 0.11100521 0.30293016 0.12864961]\n", " Robust linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 21\n", "Model: RLM Df Residuals: 17\n", "Method: IRLS Df Model: 3\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Fri, 12 Jun 2020 \n", "Time: 07:42:20 \n", "No. Iterations: 19 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "var_0 -41.0265 9.792 -4.190 0.000 -60.218 -21.835\n", "var_1 0.8294 0.111 7.472 0.000 0.612 1.047\n", "var_2 0.9261 0.303 3.057 0.002 0.332 1.520\n", "var_3 -0.1278 0.129 -0.994 0.320 -0.380 0.124\n", "==============================================================================\n", "\n", "If the model instance has been used for another fit with different fit\n", "parameters, then the fit options might not be the correct ones anymore .\n" ] } ], "source": [ "huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())\n", "hub_results = huber_t.fit()\n", "print(hub_results.params)\n", "print(hub_results.bse)\n", "print(hub_results.summary(yname='y',\n", " xname=['var_%d' % i for i in range(len(hub_results.params))]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Huber's T norm with 'H2' covariance matrix" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-41.02649835 0.82938433 0.92606597 -0.12784672]\n", "[9.08950419 0.11945975 0.32235497 0.11796313]\n" ] } ], "source": [ "hub_results2 = huber_t.fit(cov=\"H2\")\n", "print(hub_results2.params)\n", "print(hub_results2.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters: [-40.8817957 0.79276138 1.04857556 -0.13360865]\n" ] } ], "source": [ "andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())\n", "andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov=\"H3\")\n", "print('Parameters: ', andrew_results.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for scale options\n", "\n", "## Comparing OLS and RLM\n", "\n", "Artificial data with outliers:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "x1 = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x1, (x1-5)**2))\n", "X = sm.add_constant(X)\n", "sig = 0.3 # smaller error variance makes OLS<->RLM contrast bigger\n", "beta = [5, 0.5, -0.0]\n", "y_true2 = np.dot(X, beta)\n", "y2 = y_true2 + sig*1. * np.random.normal(size=nsample)\n", "y2[[39,41,43,45,48]] -= 5 # add some outliers (10% of nsample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 1: quadratic function with linear truth\n", "\n", "Note that the quadratic term in OLS regression will capture outlier effects. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.02736511 0.52480664 -0.01306303]\n", "[0.45271415 0.06989295 0.00618445]\n", "[ 4.70078941 4.96613842 5.2271349 5.48377885 5.73607027 5.98400917\n", " 6.22759553 6.46682936 6.70171067 6.93223945 7.15841569 7.38023941\n", " 7.5977106 7.81082926 8.01959539 8.22400899 8.42407006 8.61977861\n", " 8.81113462 8.99813811 9.18078906 9.35908749 9.53303338 9.70262675\n", " 9.86786759 10.0287559 10.18529168 10.33747493 10.48530566 10.62878385\n", " 10.76790951 10.90268265 11.03310326 11.15917133 11.28088688 11.3982499\n", " 11.51126039 11.61991835 11.72422378 11.82417668 11.91977705 12.0110249\n", " 12.09792021 12.180463 12.25865325 12.33249098 12.40197618 12.46710885\n", " 12.52788899 12.5843166 ]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "res = sm.OLS(y2, X).fit()\n", "print(res.params)\n", "print(res.bse)\n", "print(res.predict())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate RLM:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4.94549540e+00 5.10716177e-01 -2.51687056e-03]\n", "[0.12984253 0.02004593 0.00177376]\n" ] } ], "source": [ "resrlm = sm.RLM(y2, X).fit()\n", "print(resrlm.params)\n", "print(resrlm.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare OLS estimates to the robust estimates:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0xac8c8db0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(x1, y2, 'o',label=\"data\")\n", "ax.plot(x1, y_true2, 'b-', label=\"True\")\n", "prstd, iv_l, iv_u = wls_prediction_std(res)\n", "ax.plot(x1, res.fittedvalues, 'r-', label=\"OLS\")\n", "ax.plot(x1, iv_u, 'r--')\n", "ax.plot(x1, iv_l, 'r--')\n", "ax.plot(x1, resrlm.fittedvalues, 'g.-', label=\"RLM\")\n", "ax.legend(loc=\"best\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2: linear function with linear truth\n", "\n", "Fit a new OLS model using only the linear term and the constant:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5.55388512 0.39417636]\n", "[0.39129675 0.03371571]\n" ] } ], "source": [ "X2 = X[:,[0,1]] \n", "res2 = sm.OLS(y2, X2).fit()\n", "print(res2.params)\n", "print(res2.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimate RLM:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5.01031303 0.49145726]\n", "[0.1025012 0.00883192]\n" ] } ], "source": [ "resrlm2 = sm.RLM(y2, X2).fit()\n", "print(resrlm2.params)\n", "print(resrlm2.bse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare OLS estimates to the robust estimates:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(res2)\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "ax.plot(x1, y2, 'o', label=\"data\")\n", "ax.plot(x1, y_true2, 'b-', label=\"True\")\n", "ax.plot(x1, res2.fittedvalues, 'r-', label=\"OLS\")\n", "ax.plot(x1, iv_u, 'r--')\n", "ax.plot(x1, iv_l, 'r--')\n", "ax.plot(x1, resrlm2.fittedvalues, 'g.-', label=\"RLM\")\n", "legend = ax.legend(loc=\"best\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp2yi9nicf/628c5ee6-8319-4dc2-aae4-6c8e9618b7f3 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# M-Estimators for Robust Linear Modeling" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "from statsmodels.compat import lmap\n", "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* An M-estimator minimizes the function \n", "\n", "$$Q(e_i, \\rho) = \\sum_i~\\rho \\left (\\frac{e_i}{s}\\right )$$\n", "\n", "where $\\rho$ is a symmetric function of the residuals \n", "\n", "* The effect of $\\rho$ is to reduce the influence of outliers\n", "* $s$ is an estimate of scale. \n", "* The robust estimates $\\hat{\\beta}$ are computed by the iteratively re-weighted least squares algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* We have several choices available for the weighting functions to be used" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "norms = sm.robust.norms" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_weights(support, weights_func, xlabels, xticks):\n", " fig = plt.figure(figsize=(12,8))\n", " ax = fig.add_subplot(111)\n", " ax.plot(support, weights_func(support))\n", " ax.set_xticks(xticks)\n", " ax.set_xticklabels(xlabels, fontsize=16)\n", " ax.set_ylim(-.1, 1.1)\n", " return ax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Andrew's Wave" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " Andrew's wave weighting function for the IRLS algorithm\n", " \n", " The psi function scaled by z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " weights(z) = sin(z/a)/(z/a) for \\|z\\| <= a*pi\n", " \n", " weights(z) = 0 for \\|z\\| > a*pi\n", "\n" ] } ], "source": [ "help(norms.AndrewWave.weights)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = 1.339\n", "support = np.linspace(-np.pi*a, np.pi*a, 100)\n", "andrew = norms.AndrewWave(a=a)\n", "plot_weights(support, andrew.weights, ['$-\\pi*a$', '0', '$\\pi*a$'], [-np.pi*a, 0, np.pi*a]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hampel's 17A" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " Hampel weighting function for the IRLS algorithm\n", " \n", " The psi function scaled by z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " weights(z) = 1 for \\|z\\| <= a\n", " \n", " weights(z) = a/\\|z\\| for a < \\|z\\| <= b\n", " \n", " weights(z) = a*(c - \\|z\\|)/(\\|z\\|*(c-b)) for b < \\|z\\| <= c\n", " \n", " weights(z) = 0 for \\|z\\| > c\n", "\n" ] } ], "source": [ "help(norms.Hampel.weights)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fUQAgIyjGAHAL3cPTkqTOhnLPSXJXe12pFpfjGpjkyDYAwUAxBoBb6BqaUTRinGF8Bx0c2QYgYCjGAHALXcMz2lFTosIYb5O3kxozucIGPAABwTs+ANxC19CMdtaz8e5OtlUlvnHo5sg2AAFBMQaAm8TjTpeHZ5gvXkM0YtpZV6auoWnfUQAgIyjGAHCTgcl5zS2tsGKchl2NZbo0xIoxgGCgGAPATVKjAZ0NFOO1dNaX6+rorBaX476jAMCmUYwB4Cap0YDOekYp1rKrsUwrcaero6waA8h/FGMAuEnX8IxKC6NqqizyHSXn7UrOYV8cpBgDyH8UYwC4SepECjPzHSXnpTYoXmIDHoAAoBgDwE26OZEibeVFMTVVFqmLDXgAAoBiDACrzC+tqHdslhMp1mFXQzkrxgACgWIMAKtcGppW3El7m1gxTleqGDvnfEcBgE2hGAPAKhcHEyufe5sqPCfJH7sayjQ1v6yh6QXfUQBgUyjGALDKhevTikZMHXWMUqRrV2NyAx4nUwDIcxRjAFjl/PUpddSVqjDG22O6djemjmyb8pwEADaHd34AWOXi4LT2NDJGsR7NlcWqKI7p3HWKMYD8RjEGgKT5pRVdHplh4906mZn2NVfo3ADFGEB+oxgDQFL38IziTtrNxrt129uUKMacTAEgn1GMASDpQvJEij2NrBiv113NFZqcX9b1SU6mAJC/KMYAkHTh+pQiJnU2cCLFeqWOtzs7MOk5CQBsHMUYAJIuXJ9WR12ZimJR31Hyzl3JYnyeDXgA8hjFGACSzl+funH0GNanpqxQjRVFOjfA1dAA8hfFGAAkzS4uq3tkRndvq/QdJW/d1VzBijGAvEYxBgBJZwem5Jy0fzvFeKPuakoU45U4J1MAyE8UYwCQdKY/sWlsPyvGG7a3uUILy3FdHZ31HQUANoRiDABKFOOKophaa0p8R8lbqQ14XPQBIF9RjAFA0pn+Ke3bViEz8x0lb+1pKpcZxRhA/qIYAwi9eNzpbP8kG+82qbQwprbaUp27zlnGAPITxRhA6PWMzWpmcYVinAH7mit0tp8VYwD5iWIMIPRSG+8oxpt3cHuVuoZnNDW/5DsKAKwbxRhA6J3uT1wFndo8ho072FIlKTGzDQD5Jq1ibGaPm9k5M7toZp+/zTOfNLPTZnbKzP48szEBYOuc6Z9UR32ZSgq5CnqzDiTPgT7ZN+E5CQCsX2ytB8wsKunLkj4iqVfSMTM76pw7veqZPZL+jaTHnHNjZta4VYEBINPO9E/q0I5q3zECobGyWA0VRTp5jWIMIP+ks2L8sKSLzrku59yipK9JeuqmZz4r6cvOuTFJcs4NZjYmAGyNyfkl9Y7NcbFHBh3cXqlTfZxMASD/pFOMWyT1rPq4N/naansl7TWzl8zsFTN7/Fa/kZk9Y2bHzez40NDQxhIDQAaducaNd5l2sKVKFwanNLe44jsKAKxLpjbfxSTtkfR+SU9L+iMze8fPJZ1zzzrnDjvnDjc0NGToSwPAxr3Vm/iR/z2tVZ6TBMeB7VWKO+nsAKvGAPJLOsW4T9KOVR+3Jl9brVfSUefcknOuW9J5JYoyAOS0E73jaqkuUX15ke8ogXGwJbH6fuoaxRhAfkmnGB+TtMfMdppZoaRPSTp60zN/qcRqscysXonRiq4M5gSALfHjvgnd08JqcSa1VJeourRAp9iAByDPrFmMnXPLkj4n6XlJZyR93Tl3ysy+ZGZPJh97XtKImZ2W9KKk/8E5N7JVoQEgE8ZnF3VlZFb37qAYZ5KZ6eD2Kp1kAx6APLPmcW2S5Jx7TtJzN732hVW/dpJ+NfkXAOSF1HzxoVaOasu0Ay2V+tMfXtbiclyFMe6SApAfeLcCEFpv9Y5Levu2NmTOwe1VWlyJ6/x1bsADkD8oxgBC60TvhDrry1RVUuA7SuDcl7ww5c2ecc9JACB9FGMAofXj3gndyzFtW6K1pkT15YV64yrFGED+oBgDCKXByXkNTM7rHuaLt4SZ6b4dNXqjZ8x3FABIG8UYQCiduLHxjhXjrXJ/W7W6hmY0PrvoOwoApIViDCCU3uodVzRiOrCdYrxV7mfOGECeoRgDCKU3e8a1p7FcJYVR31EC694d1TKjGAPIHxRjAKGzEnd64+q4DnfU+I4SaOVFMd3VVMEGPAB5g2IMIHTODkxqemFZh9trfUcJvPvbqvVmz7jicec7CgCsiWIMIHReu5I4KeHBdlaMt9r9O2o0Mbek7pEZ31EAYE0UYwCh89qVMTVVFqm1psR3lMC7vy2xAY9xCgD5gGIMIHSOXx7T4fZamZnvKIG3q6FcFUUxvX6V84wB5D6KMYBQGZiYV9/4nB5gjCIrIhHTA+01On551HcUAFgTxRhAqBy/kihohynGWfPwzlqdvz6t0Rku+gCQ2yjGAELl+OUxlRREtX97pe8oofHIzsTpH8dYNQaQ4yjGAELltStjOrSjSgVR3v6y5Z7WKhXFInq1i2IMILfxJwOA0JhZWNbp/knOL86yolhU97dV60eXR3xHAYA7ohgDCI3jV8a0End6aCfFONse3lmn09cmNTm/5DsKANwWxRhAaLx8aUQFUdNDXAWddUd21iru3r5cBQByEcUYQGi83DWiQ63VKi2M+Y4SOve31SgWMf2omzljALmLYgwgFKbml3Syb0Lv2lXnO0oolRRGdW9rFcUYQE6jGAMIhWOXR7USdzpCMfbm4Z11eqt3XHOLK76jAMAtUYwBhMI/XBxRYSyiB9qYL/blkc5aLa045owB5CyKMYBQeLlrRA+0Vau4IOo7Smg91FGraMT0ctew7ygAcEsUYwCBNz67qNP9k3rXrnrfUUKtvCime1ur9AoXfQDIURRjAIH3aveonJMeZb7YuyOddTrRM66ZhWXfUQDgHSjGAALv5UsjKi6I6FBrte8oofdoZ52W407HmTMGkIMoxgAC79XuUT3YXqPCGG95vh3uqFFB1PRKF9dDA8g9/CkBINAmZpd0dmBSj+xkjCIXlBbGdKi1Wi9fohgDyD0UYwCB9qPLifniR3bW+o6CpCOddfpx34SmmTMGkGMoxgAC7dWuxPnFh3YwX5wrHt1Vp5W40zFuwQOQYyjGAALtjZ5x3dtSxfnFOeTB9hrFIsZFHwByDsUYQKBdHp7RnqZy3zGwSnFBVG21peoanvYdBQB+AsUYQGBNzi9pZGZR7XVlvqPgJh31ZeoenvUdAwB+AsUYQGBdSRavDopxztlZX6bLwzOKx53vKABwA8UYQGB1j8xIkjrqSz0nwc121pdpbmlF16fmfUcBgBsoxgACq3toRmasGOeinfWJ/ybdQzOekwDA2yjGAAKra3ha26tKOJEiB6WKcdcwxRhA7qAYAwisrqEZdTawWpyLmiuLVVwQ0WWKMYAcQjEGEEjOOXUPz6iznmKciyIRU0ddmbopxgByCMUYQCANTS1oemFZnQ2cYZyrdtZTjAHkFooxgEC6lNzUtZMV45y1s75MV0dntbQS9x0FACRRjAEEVGolkhnj3LWzvkzLcafesTnfUQBAEsUYQEB1DU2rKBbR9qoS31FwG6lvWtiAByBXUIwBBFLX8Ix21pcpEjHfUXAbqfOlObINQK6gGAMIpO5hjmrLdbVlhaosjql7eNp3FACQRDEGEECLy3FdHZ1VZz0nUuQyM9POhnJOpgCQMyjGAALn6uisVuKOEynyQGd9GddCA8gZFGMAgcOJFPmjs75M1ybmNbOw7DsKAFCMAQRP11BiZpVRity3uzHx36iLVWMAOYBiDCBwuoZmVFdWqKrSAt9RsIY9TYlifGFwynMSAKAYAwigruFp5ovzRHtdmWIR08VBTqYA4B/FGECgOOd0cXD6xo/okdsKohG115VSjAHkBIoxgEAZmVnU2OyS9jRV+I6CNO1prKAYA8gJFGMAgXLheqJg7WHFOG/sbizXldFZLS7HfUcBEHIUYwCBktrEtZcV47yxp6lcK3GnyyOcTAHAL4oxgEC5cH1aFUUxNVUW+Y6CNO1qSJ5McZ1xCgB+UYwBBMqFwSntbiqXmfmOgjTtaiiXmZgzBuAdxRhAoFwcnNbeRsYo8klJYVStNSW6OEQxBuAXxRhAYIzOLGp4evHGpRHIH3saK3ThOpd8APCLYgwgMFLFijOM88/uxnJ1Dc9oJe58RwEQYhRjAIFxITmjyokU+Wd3Q7kWl+PqHZv1HQVAiFGMAQTGhetTKi+KaVtVse8oWKfdTZxMAcC/tIqxmT1uZufM7KKZff4Oz/20mTkzO5y5iACQngvJq6A5kSL/pC5kOcecMQCP1izGZhaV9GVJT0jaL+lpM9t/i+cqJP2KpFczHRIA0nFhcJob7/JURXGBWqpLdG6AYgzAn3RWjB+WdNE51+WcW5T0NUlP3eK5fy/p1yXNZzAfAKRlfHZRQ1MLnEiRx/Y1V1CMAXiVTjFukdSz6uPe5Gs3mNkDknY45/4mg9kAIG3nk7OpezjDOG/d1VyhS0PTWlyO+44CIKQ2vfnOzCKSfkvSv0rj2WfM7LiZHR8aGtrslwaAG84NTEqS9m2jGOeru5ortBx36hpmAx4AP9Ipxn2Sdqz6uDX5WkqFpIOSvmdmlyUdkXT0VhvwnHPPOucOO+cONzQ0bDw1ANzkzMCUqkoK1FzJiRT5al9zpSTpbD/jFAD8SKcYH5O0x8x2mlmhpE9JOpr6pHNuwjlX75zrcM51SHpF0pPOueNbkhgAbuFs/6T2NVdwIkUe62woU0HUdJY5YwCerFmMnXPLkj4n6XlJZyR93Tl3ysy+ZGZPbnVAAFhLPO50dmBKd2+r9B0Fm1AQjWhXQ/mNsRgAyLZYOg85556T9NxNr33hNs++f/OxACB9PWOzml1c0d3MF+e9fc0V+lH3qO8YAEKKm+8A5L0zyZnU1Iwq8tddzZW6NjGvibkl31EAhBDFGEDeOzswKTNpbxMrxvluX3PivyHnGQPwgWIMIO+d7Z/SzroylRRGfUfBJt11oxgzZwwg+yjGAPLemYFJzi8OiG1VxaoojnEyBQAvKMYA8trMwrKujMwyXxwQZqZ9zRUUYwBeUIwB5LVz11Mb71gxDor92yp1tn9S8bjzHQVAyFCMAeS11C1pnGEcHAe2V2lmcUWXR2Z8RwEQMhRjAHnt7MCkyotiaqku8R0FGbJ/e+KbnFPX2IAHILsoxgDy2qlriaugIxGugg6KvU0VKogaxRhA1lGMAeStlbjT6WuTOthS5TsKMqgwFtGexgqdujbhOwqAkKEYA8hb3cPTmltaoRgH0IHtlTp9bVLOsQEPQPZQjAHkrZN9iR+1H2xh413QHGyp0sjMoq5PLviOAiBEKMYA8tbJvgkVxSLa3VDuOwoy7MCNDXiMUwDIHooxgLx18tqE7t5WqViUt7KguXtbpcze/qkAAGQDf5oAyEvxuNOpvknGKAKqrCimnXVlrBgDyCqKMYC81DM2q6mFZR3czsa7oNq/vZIj2wBkFcUYQF56e+MdxTioDrZUqW98TuOzi76jAAgJijGAvPTjvgkVRE17mth4F1QHuAEPQJZRjAHkpVPXJrS3qUJFsajvKNgi9yR/GnCid9xzEgBhQTEGkHecczrZN3GjOCGYqksL1V5Xqrd62IAHIDsoxgDyTt/4nMZml278qB3Bdai1Wm+xYgwgSyjGAPLOieQK4qEd1Z6TYKvd21qlaxPzGpya9x0FQAhQjAHknRO94yqMRbSvmRXjoEt988M4BYBsoBgDyDtv9ozrwPZKFcZ4Cwu6A9srFTExTgEgK/hTBUBeWV6J68e9EzrUyhhFGJQWxrS3qUInelkxBrD1KMYA8sqFwWnNLa3oPuaLQ+Pe1iqd6B2Xc853FAABRzEGkFdO9CR+pM7Gu/A4tKNa47NL6hmd8x0FQMBRjAHklRO946oqKVBHXanvKMiS1NgMF30A2GoUYwB55Y2r4zq0o1pm5jsKsuSu5goVxiI3floAAFuFYgwgb8wuLuv89Snd18qNd2FSEI3owPZKvUkxBrDFKMYA8sbJvknFHfPFYfRgW43e6pvQ4nLcdxQAAUYxBpA33uwZk0QxDqMH22u0uBzXyWsc2wZg61CMAeSN16+Ma0dtierLi3xHQZY90F7ESVPJAAAU3ElEQVQjSXr9ypjnJACCjGIMIC845/Ta1TEdbq/1HQUeNFUWq7WmRK9RjAFsIYoxgLzQMzqnoamFGyuHCJ8H22t0/MoYF30A2DIUYwB54fiVUUnSYYpxaD3YXqOhqQX1jnHRB4CtQTEGkBdeuzKmiqKY9jZV+I4CTx5oS84ZX2WcAsDWoBgDyAuvXRnTfW3Vika42COs9jVXqKwwypwxgC1DMQaQ8ybmlnTu+hQb70IuFo3ovrZqijGALUMxBpDz3uwZl3OJGVOE24NtNTrTP6mZhWXfUQAEEMUYQM577fKoIibd18bFHmH3QHuN4k46wfXQALYAxRhAzjt+ZUx3b6tUeVHMdxR4dn9yAx7jFAC2AsUYQE5bWonrzZ5xxiggSaoqKdDepnIdoxgD2AIUYwA57WTfhGYXV/TwTjbeIeGhjlq9dnlUyytx31EABAzFGEBOe6UrcbHHIzvrPCdBrjjSWaeZxRWdvDbpOwqAgKEYA8hpr3aPaHdjuRoqinxHQY54pDPx04NXu0Y8JwEQNBRjADlreSWuY92jeoQxCqzSWFGszoYyvUIxBpBhFGMAOevktUnNLK7oSCdjFPhJRzrrdOzyGHPGADKKYgwgZ6V+VJ760TmQcqSzTtMLyzrdz5wxgMyhGAPIWa90jaizoUyNFcW+oyDHHEmO1zBOASCTKMYActLySlzHLo8xRoFbaqwsVmd92Y1TSwAgEyjGAHLS6f5JTS8sU4xxW4901ulY96hW4s53FAABQTEGkJNSPyI/wokUuI0jnbWaWljWac4zBpAhFGMAOemVrlF11pepsZL5Ytxa6tIX5owBZArFGEDOWYm7xPnFjFHgDpqrirWznvOMAWQOxRhAznmrd1xTC8t6dBfFGHf26K46vdo9qiXOMwaQARRjADnnBxeGZSa9e3e97yjIce/ZXa/phWW92TPuOwqAAKAYA8g5P7wwrIPbq1RbVug7CnLcu3bVK2KJb6YAYLMoxgByytT8kl6/OqZ372G1GGurKi3Qva3V+sGFId9RAAQAxRhATnmla1TLcaf3UIyRpvfuqdeJnnFNzC75jgIgz1GMAeSUH14YUklBVA+21/iOgjzx7j0Nijvp5S7GKQBsDsUYQE75wYVhPdJZq6JY1HcU5In726pVVhjV95kzBrBJFGMAOaN3bFZdwzOcRoF1KYhG9OiuOv2QYgxgkyjGAHJGqti8d2+D5yTIN+/Z06Cro7O6MjLjOwqAPJZWMTazx83snJldNLPP3+Lzv2pmp83sLTP7rpm1Zz4qgKD7wcVhNVUWaU9jue8oyDOpU0w4tg3AZqxZjM0sKunLkp6QtF/S02a2/6bH3pB02Dl3r6RvSPqNTAcFEGwrcaeXLg7r3bsbZGa+4yDPdNaXqaW6RN8/z7FtADYunRXjhyVddM51OecWJX1N0lOrH3DOveicm01++Iqk1szGBBB0J3rHNT67pPfuZb4Y62dmeu/eev3DpREtLnM9NICNSacYt0jqWfVxb/K12/lFSd++1SfM7BkzO25mx4eG+K4ewNtePDuoiEnvY74YG/SBuxo1vbCs45dHfUcBkKcyuvnOzH5W0mFJv3mrzzvnnnXOHXbOHW5o4A8/AG/77plBHW6vVXUp10BjYx7bXa/CWETfPTvoOwqAPJVOMe6TtGPVx63J136CmX1Y0v8s6Unn3EJm4gEIg/6JOZ3un9QH9jX6joI8VlYU05HOOr1AMQawQekU42OS9pjZTjMrlPQpSUdXP2Bm90v6QyVKMe9IANblxbOJ0aoP3U0xxuZ8aF+juodn1DU07TsKgDy0ZjF2zi1L+pyk5yWdkfR159wpM/uSmT2ZfOw3JZVL+v/M7E0zO3qb3w4A3uGFs9fVWlPCMW3YtA8mf+rAqjGAjYil85Bz7jlJz9302hdW/frDGc4FICTml1b00sURfeJwK8e0YdN21JZqT2O5Xjw3qH/2nk7fcQDkGW6+A+DVy10jmltaubHSB2zWB+9u1Ktdo5qaX/IdBUCeoRgD8OrFs4MqKYjqSGed7ygIiA/ta9Jy3N24YhwA0kUxBuCNc07fPTOox3bXq7gg6jsOAuKBtmpVlRRwbBuAdaMYA/Dm3PUp9Y3PcRoFMioWjeh9exv04tlBrcSd7zgA8gjFGIA33zk5IDPpw3c3+Y6CgPnYgWaNzCxyCx6AdaEYA/DmOycH9FB7rRoqinxHQcC8/64GFcYi+s6pAd9RAOQRijEALy4Pz+jswJQ+drDZdxQEUFlRTO/d06DnTw7IOcYpAKSHYgzAi+eTK3kfO8AYBbbG4webdW1iXj/um/AdBUCeoBgD8OLbJwd0T0uVWmtKfUdBQH347kbFIqZvn2ScAkB6KMYAsq5/Yk5v9ozrccYosIWqSwv16K46fYdxCgBpohgDyLq/PXVdkijG2HIfO9Cs7uEZXRic9h0FQB6gGAPIuu+cHNCexnLtaij3HQUB99H9TTJL/H8OANZCMQaQVaMzi3q1e4TVYmRFY2WxHmyrYc4YQFooxgCy6tsn+xV3jFEge564Z5vO9E/q0hDjFADujGIMIKuOvnlNuxrKtH9bpe8oCIl/dO82mUl/deKa7ygAchzFGEDWDEzM60eXR/XkoRaZme84CImmymI9srNWR09c43QKAHdEMQaQNX/91jU5J/2Xh7b5joKQefJQi7qGZnS6f9J3FAA5jGIMIGv+6sQ1HWypVCenUSDLnjjYrFjEdJRxCgB3QDEGkBWXh2d0ondCTx7a7jsKQqimrFDv2VOvvz7Rr3iccQoAt0YxBpAVf/1WYqXuH91LMYYfT963XX3jc3qjZ8x3FAA5imIMICuOnrimhzpqtL26xHcUhNRH9jerKBbR0TcZpwBwaxRjAFvu7MCkzl+fZowCXpUXxfShuxv1Nz/u1/JK3HccADmIYgxgy/3H13pVEDV9/B5Oo4BfP3Vfi4anF/X9C0O+owDIQRRjAFtqeSWub71xTR/c16i68iLfcRByH9jXqLqyQn3jtV7fUQDkIIoxgC31/QtDGp5e0E8/0Oo7CqCCaERP3rddf3d6UOOzi77jAMgxFGMAW+obr/WqrqxQH9jX6DsKIEn6mQdbtbgS54poAO9AMQawZcZnF/V3pwf15H3bVRDl7Qa54cD2Kt29rZJxCgDvwJ9UALbMX524psWVuH7mQcYokFt++oEWneid0IXrU76jAMghFGMAW+Ybr/Xq7m2VOrC9yncU4Cf81P0tikVM33idVWMAb6MYA9gS569P6UTvhH76gRbfUYB3qC8v0vvvatC3Xu/jTGMAN1CMAWyJr/7oqgqipp+6n2KM3PSJwzs0OLWgF84O+o4CIEdQjAFk3PzSir75ep8+dqBZ9ZxdjBz1oX2Naqwo0ld/dNV3FAA5gmIMIOOe+3G/JuaW9E8ebvMdBbitWDSif/zQDn3v/JB6x2Z9xwGQAyjGADLuz1+9qp31ZXp0V53vKMAd/eOHdkiS/uJYj+ckAHIBxRhARp2/PqXjV8b09MM7ZGa+4wB31FpTqvfvbdBfHOthEx4AijGAzPrzV6+qMBrhCmjkjacfbtPg1IK+yyY8IPQoxgAyJrHprlcfO9isOjbdIU98cF+jmiuL9eevsgkPCDuKMYCMOXrimibnl9l0h7wSi0b0yYd26PsXhnR1hE14QJhRjAFkhHNOX3npsvY2letIZ63vOMC6/JOH2xQ10//18mXfUQB4RDEGkBHHLo/pdP+kPvOunWy6Q95prirWE/ds09eP9WhmYdl3HACeUIwBZMRX/qFbVSUF+q+46Q556jPv6tDUwrK++Xqv7ygAPKEYA9i0vvE5PX/quj718A6VFEZ9xwE25IG2ah1qrdJX/uGy4nHnOw4ADyjGADbt/3nlipxz+vSRdt9RgA0zM33msQ5dGprRDy8O+44DwAOKMYBNmVtc0Vd/dFUf3d+s1ppS33GATfn4PdtUX16kP32p23cUAB5QjAFsyrfe6NP47JJ+/l0dvqMAm1YUi+qfPtKmF88N6eLgtO84ALKMYgxgw1biTn/0gy7d01LFEW0IjE8/2q6iWET/5w+6fEcBkGUUYwAb9renBtQ9PKNfet8ujmhDYNSXF+kTh1v1zdf7NDg57zsOgCyiGAPYEOec/uDvL6m9rlSPH2z2HQfIqM++p1PL8bj+5KXLvqMAyCKKMYANeaVrVCd6J/TZ93QqGmG1GMHSXlemJ+7Zpv/3lSuanF/yHQdAllCMAWzIH37/kurLC/UzD7b6jgJsiX/xvl2aWljWV1+96jsKgCyhGANYtzP9k/reuSH9wmM7VVzAhR4IpoMtVXr37nr98Q+7tbC84jsOgCygGANYtz/8+0sqK4zqZx/hQg8E2z9/X6cGpxb0l2/0+Y4CIAsoxgDW5erIrP7qrX49/XCbqkoLfMcBttS7d9frYEul/uDvu7S8EvcdB8AWoxgDWJfffeGCYhHTM+/t9B0F2HJmpn/5wT3qHp7R0RPXfMcBsMUoxgDSdmVkRt98o0//9JF2NVYW+44DZMVH9zdp/7ZK/e4LF1k1BgKOYgwgbb/3wkXFIqZfeh+rxQgPM9OvfJhVYyAMKMYA0sJqMcKMVWMgHCjGANLyu6wWI8RWrxr/pzdZNQaCimIMYE2Xh2f0LVaLEXJvrxpfYNUYCCiKMYA1/U7yJApWixFmqVXjyyOz+hbnGgOBRDEGcEdnByb1rTf69HOPsloMfHR/k+5trdJv/+fzml/iNjwgaCjGAO7oN75zTuVFMf3yB3b7jgJ4Z2b6/OP7dG1iXn/28hXfcQBkGMUYwG290jWiF84O6r95/25Vlxb6jgPkhHftrtd79zbo9168qIm5Jd9xAGRQWsXYzB43s3NmdtHMPn+LzxeZ2V8kP/+qmXVkOiiA7HLO6de+fVbNlcX6hcc6fMcBcsq/fvwuTc4v6fe/d8l3FAAZtGYxNrOopC9LekLSfklPm9n+mx77RUljzrndkn5b0q9nOiiA7PrOyQG92TOuX/3IXhUXRH3HAXLKge1V+qn7WvSnL3Wrf2LOdxwAGRJL45mHJV10znVJkpl9TdJTkk6veuYpSV9M/vobkn7PzMw55zKYddNmFpbVPTzjOwaQF37z+XPa01iu//qBFt9RgJz0qx/Zq795q1+/9u2z+ux7OLEFSMfBlirfEe4onWLcIqln1ce9kh653TPOuWUzm5BUJ2k4EyEz5dS1SX3yD1/2HQPIG3/0c4cVi7IVAbiVHbWl+vSj7frjH3Zz6QeQhohJXf/hv/Ad447SKcYZY2bPSHpGktra2rL5pSVJexrL9eynH8z61wXyUWNlse7bUe07BpDT/qeP36337m3QAke3AYGQTjHuk7Rj1cetyddu9UyvmcUkVUkaufk3cs49K+lZSTp8+HDWxyxqygr10QPN2f6yAICAikZM79vb4DsGgAxJ52ekxyTtMbOdZlYo6VOSjt70zFFJP5/89c9IeiHX5osBAACAO1lzxTg5M/w5Sc9Likr6E+fcKTP7kqTjzrmjkv5Y0p+Z2UVJo0qUZwAAACBvpDVj7Jx7TtJzN732hVW/npf0icxGAwAAALKH7eYAAACAKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJIoxAAAAIEky55yfL2w2JOmKly8u1Usa9vS1AWAzeP8CkK98vn+1O+ca1nrIWzH2ycyOO+cO+84BAOvF+xeAfJUP71+MUgAAAACiGAMAAACSwluMn/UdAAA2iPcvAPkq59+/QjljDAAAANwsrCvGAAAAwE+gGAMAAAAKQDE2s4+Z2QtmNmBmC2bWa2ZfN7P9t3j2i2b2GQ8xAWDDzGyHmX3DzCbMbNLMvmlmbb5zAQi3IHawvC/GkmolvSbpc5I+KunfSDog6RUzazezx8zsk6v/ATOLmtm/MLO7sh8XANJnZqWSXpC0T9LPS/q0pD2SXjSzMp/ZAIRe4DpYIDffJf9ln5X030v6uqQvSOqQNCJpXNK9kr4v6dedc2OeYgLAmszsVyT9lqS7nHMXk6/tlHRB0v/onPstn/kAYLV872Ax3wG2yEjy78vOuR5JnzWzj0o6Kmle0gecc2+s/gfM7H2S/q2kh5X493JR0u845/44e7EB4B2elPRKqhRLknOu28xekvSUEqUZAHJFXnewwBRjM4tKikpql/RrkgYkfdXMtivx3couSX+pxHcrXzazv5f0G865MTN7StJ/lPSSpH+uxD3eB5K/FwD4dEDSf7rF66ckfSLLWQDgHYLUwQJTjCW9KunB5K8vSvqgc27QzN4t6UXn3C+Z2RclXZb0y5KekdRoZuOS/ndJbyrxXUw8+Xv8XTbDA8Bt1Eq61Y8bRyXVZDkLANxKYDpYkIrxpyVVSupUYq7lP5vZu51zP7z5QefciqTflyQz26fkdzir/oMAAAAgPYHpYIEpxs65M8lfvmpm31biu5LPS/qlVc988Rb/aF3y771bmQ8ANmhMt14Zvt1KMgBkVZA6WBCOa3sH59y4Ekv5u9N4fDj595atSwQAG3ZKiXm7m+2XdDrLWQDgjvK9gwWyGJtZkxJnfl5K4/HzSnxn88/MzLYyFwBswFFJR8ysM/WCmXVIeiz5OQDIGfnewfL+HGMz+5ak1yW9JWlS0l5J/52kZkkPO+fOp/F7PCXpm0qcq/cHkoYk3S2p0Tn377YoOgCsKXmJxwlJc0ocZ+Qk/XtJFZLudc5Ne4wHIMSC2MGCUIz/taRPKnEUSKGkHknfk/QfnHOX1/H7fFDS/yLpoeRLlyT9b865P81kXgBYr+T1z78t6SOSTNJ3Jf2363mPA4BMC2IHy/tiDAAAAGRCIGeMAQAAgPWiGAMAAACiGAMAAACSKMYAAACAJIoxAAAAIIliDAAAAEiiGAMAAACSKMYAAACAJOn/B7JXEVzuUhqqAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c = 8\n", "support = np.linspace(-3*c, 3*c, 1000)\n", "hampel = norms.Hampel(a=2., b=4., c=c)\n", "plot_weights(support, hampel.weights, ['3*c', '0', '3*c'], [-3*c, 0, 3*c]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Huber's t" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " Huber's t weighting function for the IRLS algorithm\n", " \n", " The psi function scaled by z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " weights(z) = 1 for \\|z\\| <= t\n", " \n", " weights(z) = t/\\|z\\| for \\|z\\| > t\n", "\n" ] } ], "source": [ "help(norms.HuberT.weights)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = 1.345\n", "support = np.linspace(-3*t, 3*t, 1000)\n", "huber = norms.HuberT(t=t)\n", "plot_weights(support, huber.weights, ['-3*t', '0', '3*t'], [-3*t, 0, 3*t]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Least Squares" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " The least squares estimator weighting function for the IRLS algorithm.\n", " \n", " The psi function scaled by the input z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " weights(z) = np.ones(z.shape)\n", "\n" ] } ], "source": [ "help(norms.LeastSquares.weights)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "support = np.linspace(-3, 3, 1000)\n", "lst_sq = norms.LeastSquares()\n", "plot_weights(support, lst_sq.weights, ['-3', '0', '3'], [-3, 0, 3]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ramsay's Ea" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " Ramsay's Ea weighting function for the IRLS algorithm\n", " \n", " The psi function scaled by z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " weights(z) = exp(-a*\\|z\\|)\n", "\n" ] } ], "source": [ "help(norms.RamsayE.weights)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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N1SUs8OY/mqIMY2mc9PQPcPD98+w+nn3DOXruEgBlRbNz33BKebi2jGJnhSXdJt5vu8Lu+uz4x72N7XT1DjCzILhv1RK2rcneydpwh0dAauowjKUxklLiRNuVfAjvb+rgal/2TeX+yuL8N5X1dxR5tzlJt72e/gHePHme3cfb2H28lcMfXgRyNw3KzSZvrS2ldMHsCR6p9NkZxtIoutTdx97G9nwMnzl/Fcg2tTyyJvvGsamqhPm+DSlpkmu51M1rx9vYXd/Ka7lNwgB3LVuYvd7VlnHvqiXMLJgxwSOVRs4wlj6HwcHEoQ8usru+lR8ea+WtU+fpH0zMn1XAlppStq0p45HaMlaWzJvooUrSmBkcTLz3QWd+UuCtUxcYGEwsmF3I5uqS7GjJNWWsKPa1ULc3w1j6lFov9fBabs3da/VttOdmSTbeuTA/K3zvyiXMKnSWRNL0dLG7j70N7flJg7MXsnfPVpfOz71OlrKpqoR5s3z3TLcXw1i6hd7+wWxdXe4Ffmhd3dBRatvWlLK1poyyItfVSdK1Uko05fZb7D7emr/ByKyCGdRVLslPKKxb6n4LTTzDWLqOk+0fbZrb19jOld4BCmd8tBP7kTXuxJakz6K7L3dCT26y4VhzdkJPedHs3GRDGQ/XlHpuuyaEYSyRve23r7GdPfXZRpKT7V0ArCiem99Esrm6hKI5Myd4pJI0tZzr7M4fCfdafRudV7Mz3e9etoiHa7OTLlyepvFiGGta6hsY5J3TF3itvo09DW28fTrbKDJvVgGbqkryb+1VlszzrT1JGicDg4l3z1zIjoSrb/3Ea/PWmlK2rSmlumyBr80aE4axpoWhM4X3NLTxWn0b+xvbudTTz4yALyxfzLbaUrbWlHKPsxKSdNsY/m7enoa2/J34li6cw9baUh7OvXaXeHayRolhrCnr/JVeXm9sY099FsNDu6JXFM9la00Z22pL2VJdyqJ5Lo+QpMngdEcXexra8qHcebUPgA13LOThNaU8XFNGXeUS5swsmOCRarIyjDVl9PQP8NbJC7xW38qehjZ+craTlKBoTiFbqkvYWpvF8KqS+RM9VEnS5zQwmHjvbCd7GrI78b116jx9A4nZhTN4YHVxbjbZu4vq0zGMNWmllKhvucxr9W28Vt/KgdwtlwtmBPeuXMzWmmzTxheXL6LQOy9J0pR2paefH53oYHd9K3vq26hvuQxA6YLZbK3JJkceri2lYuGcCR6pbmeGsSaV1ks9vJ5bJ7ynoZXmiz0AVJXOz2YHasvYVFXs6RGSNM2d6+zOv4P4ekMbbZezmzGtqVjA1poyHl5TyoOri73JiD7GMNZtrbtvgB+d6MhvmjuSu7nG4nkzeaimlIdrStlaW8ryJd5mVJJ0fYODiSPnLubXJh840UFvf3aTkXtXLebh3GzyxjsXUeD59NOaYazbyuBg4vCHF/ObK370/kcvXvetWpLfheyLlyTps+ruG+CN9ztyZ9dfM+lSnX2feaimlBXFTrpMN4axJlRKiVO5XcZ7G9rZ29jG+a5sl/HaiiK21mYzwr7dJUkaK62Xetjb2Mbu4x9fpreyeB4P1ZTyUE0JW6pLKfZufFOeYaxxN/QCtLehnT0NHx2jdseiOWypzl6AHqpxg4QkafyllGhouczrDW3saWjnQFN27j1kx8JtrS1lS3UJDzhhMyUZxhpzl3v6+dGJdvbUZzPCR89dAmDhnEI2V2d3MtpSU0pV6XyP1JEk3Vb6BwZ592wnexuy9clvnbxA78AgMwuCe1YuYWtuRvnu5YuZ6QlIk55hrFHX2z/I26cv5JZHZLdb7h/Mzpa8v7KYLTVZDLtOWJI02VztzdYnv96YnXZx6IOLpAQLZhfy4OpittRkd+NbU+FtqyejkYax7xXohoZ2+w4tjXjj/Q66egfyt1t+ZlsVW2tKuXeVdyOSJE1uc2cVsG1NGdvWlAHZXVb3NbXzeu5YuJ1HW4Ds/OSHakp4qLqUh2pLWbZ47kQOW6PMGWN9zKn2rux2yw1t7Gtsp+NKdj5kddn8/NKITVUlLJrrecKSpOnjzPku9ja052aU22m7nG3kqywZ2shXyuaqEpa4ke+25FIKjUj75R72NuZ+Im5s43RHtmFu6cI5bBn6ibimlKWL3DAnSRJkG/mON1/OzyYfONHB5Z5+ImDjnQvz3zvvryxm7izfUb0dGMa6rqFba76e22wwtGGuaE4hm6tKcrtyS6kuc8OcJEkj0TcwyLtnLvB6bunhj0+dp28g5W808lB19o7rF5cvotCNfBNiVMM4Ip4GfgcoAP5jSulfXPP5lcAfAotz13wjpfSDm31Nw3h8dPcN8Nap8+xvbGdvY3t+w9yswhncX7kkd4xaKV9Y5oY5SZJGQ1dvNgm1t7GdPfVtHM7daGTB7EIeWF3M5qoSNleXsOGOhczwe++4GLUwjogC4DjwBHAGeAP4akrp8LBrngV+nFL63YjYAPwgpVR5s69rGI+N3v7sp9Z9uRB+89R5evsHKZgRfGHZovwxave5YU6SpHHRcaWXvY3Z3p19Te00tV4BYNHcmWyqykJ5S00pteWeeDFWRvNUigeAhpRSU+4LPwd8GTg87JoELMz9ehHwwacbrj6rgcHEoQ862ZsL4YO5kyMisgPL//qmVWypKeH+ymKK5rhhTpKk8VY8fxY/c/ed/MzddwJwrrObfU1t+UmsFw41A1C6YBabqrK78W2uLqGyZJ6hPM5GEsbLgNPDPj4DPHjNNd8CXoyIvwvMBx4fldHpEwYHE8eaL7G3sZ19je0cONHOpe7szj1rKhbw8/ctZ3N1KZuqilk8z52xkiTdbpYumsPP3rOcn71nOQCnO7pykdzGvqZ2/ue7HwLZnWM3V5fkZ5Q9Gm7sjdY5xl8FvpdS+u2I2Az854i4K6U0OPyiiHgGeAZg5cqVo/TUU1tKicbWK+zL/c+yr7Gd8119QHZEzM/cfSebq0vYVFVMeZEnR0iSNNmsKJ7HiuJ5/ML9K0gp0dR2hb2N7exvbGfXsVb+9K2zAKwsnseW6mx98ubqEr/vj4GRrDHeDHwrpfRU7uNfB0gp/dawaw4BT6eUTuc+bgI2pZRabvR1XWN8fSklTndczf/UuK+xnZZL2VmJyxbPzf/kuLm6hDv9yVGSpCltcDBxvOUSexuyZRfD3ymuKV+QzSZXl7DJM5RvajQ33xWSbb7bAZwl23z3V1NKh4Zd8+fAH6eUvhcR64GdwLJ0ky9uGH/kw86r+XVG+xrbOXshO0u4rGh2/g/85uoSVha71kiSpOlsaG/RUDcM3ZUWYP0dC7NmqCrhgapiFrq3KG+0j2v7S8C/JTuK7Q9SSv9XRHwbOJhSej53EsV/ABaQbcT7xymlF2/2NadzGLde6mF/U/YHen9TOyfast2pi+fN/FgIV5e5O1WSJN3Y0BnKexuyEy8OnsxOo5oR5E6jyjby3V+5hHmzRmsF7eTjDT5uI22XezjQ1MGBE1kIH2++DEDR7EIerCrO70Bdt7TI8wwlSdJndqP7FxTOCL64YjGbct1x36rpFcqG8QRqvdTDgRPtHGjqYH9TO/UtWQjPm1VAXeVHB3vfdedC74AjSZLGzJWefg6ePM/exjYONHXwk7OdDORC+e7li9hUVZIP5fmzp24oG8bjqOVSdz6CD5zooCEXwvNzIZz9oSvmrmWLmGkIS5KkCXK5p583T55nf1P2LvZPznTmZ5S/MCyU66ZYKBvGY6jlYjf7T+RCuKmdxtwdbBbMLqSuckn+D5UzwpIk6XZ25ZpQfjcXygXDZpQfXF1MXWUxCyZxKBvGo6j5YnfuD0wHB5raaWr7KITvHxbCGw1hSZI0iXX1Dg/lDt7JrVEumBF8YVkulKuKuX+ShbJh/Dmc6+zOb5Tb39SRPzWiaHYhD6wuzm+Y23CHISxJkqaurt5+3jp5IT+j/M6ZC/QNZKF817JF+c18dauWUHQbHw9nGH8KH3Zeza8R3t/UzvvtXQAUzSnkwdXFPLg6mxHecOdCCjw1QpIkTVNXe3OnXuSa6e3TWSgPHQ+XX6NceXuFsmF8Ex9cuJrNCDd2sP9EOydzIbxwTiEPrC7J//Sz/g5DWJIk6Uau9g7w41MfLb14+/QFegeyc5TvyodytkZ5Im84Yhhfx5snO/i1P36HUx0fhfCDuUXlhrAkSdLnkz9HOfdO/NunPgrljXcu4rlnNk3IaRcjDePJs2p6FCxdNJd1S4v42pZKNlUVs26pISxJkjRa5swsYEt1KVuqS4EslH98Kluj3NR25bY/Am5azRhLkiRp+hnpjLFHKkiSJEkYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSMMIwjoinI+JYRDRExDducM0vRMThiDgUEf91dIcpSZIkja3CW10QEQXAd4EngDPAGxHxfErp8LBraoFfBx5KKZ2PiPKxGrAkSZI0FkYyY/wA0JBSakop9QLPAV++5ppfBr6bUjoPkFJqGd1hSpIkSWNrJGG8DDg97OMzuceGWwOsiYjXI2J/RDx9vS8UEc9ExMGIONja2vrZRixJkiSNgdHafFcI1ALbga8C/yEiFl97UUrp2ZRSXUqprqysbJSeWpIkSfr8RhLGZ4EVwz5enntsuDPA8ymlvpTSCeA4WShLkiRJk8JIwvgNoDYiVkfELOArwPPXXPNnZLPFREQp2dKKplEcpyRJkjSmbhnGKaV+4OvAC8AR4PsppUMR8e2I+FLusheA9og4DLwK/KOUUvtYDVqSJEkabZFSmpAnrqurSwcPHpyQ55YkSdL0ERFvppTqbnWdd76TJEmSMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSgBGGcUQ8HRHHIqIhIr5xk+v+SkSkiKgbvSFKkiRJY++WYRwRBcB3gZ8GNgBfjYgN17muCPjfgQOjPUhJkiRprI1kxvgBoCGl1JRS6gWeA758nev+GfAvge5RHJ8kSZI0LkYSxsuA08M+PpN7LC8i7gVWpJT+1yiOTZIkSRo3n3vzXUTMAP418A9GcO0zEXEwIg62trZ+3qeWJEmSRs1IwvgssGLYx8tzjw0pAu4CdkXE+8Am4PnrbcBLKT2bUqpLKdWVlZV99lFLkiRJo2wkYfwGUBsRqyNiFvAV4PmhT6aUOlNKpSmlypRSJbAf+FJK6eCYjFiSJEkaA7cM45RSP/B14AXgCPD9lNKhiPh2RHxprAcoSZIkjYfCkVyUUvoB8INFo4OzAAAMCUlEQVRrHvvmDa7d/vmHJUmSJI0v73wnSZIkYRhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBIwwjCPi6Yg4FhENEfGN63z+70fE4Yh4NyJ2RsSq0R+qJEmSNHZuGcYRUQB8F/hpYAPw1YjYcM1lPwbqUkp3A38C/KvRHqgkSZI0lkYyY/wA0JBSakop9QLPAV8efkFK6dWUUlfuw/3A8tEdpiRJkjS2RhLGy4DTwz4+k3vsRv4W8OfX+0REPBMRByPiYGtr68hHKUmSJI2xUd18FxF/DagDvnO9z6eUnk0p1aWU6srKykbzqSVJkqTPpXAE15wFVgz7eHnusY+JiMeBfwo8klLqGZ3hSZIkSeNjJDPGbwC1EbE6ImYBXwGeH35BRNwD/D7wpZRSy+gPU5IkSRpbtwzjlFI/8HXgBeAI8P2U0qGI+HZEfCl32XeABcB/i4i3I+L5G3w5SZIk6bY0kqUUpJR+APzgmse+OezXj4/yuCRJkqRx5Z3vJEmSJAxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJMAwliRJkgDDWJIkSQIMY0mSJAkwjCVJkiTAMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAgxjSZIkCTCMJUmSJGCEYRwRT0fEsYhoiIhvXOfzsyPij3OfPxARlaM9UEmSJGks3TKMI6IA+C7w08AG4KsRseGay/4WcD6lVAP8G+BfjvZAJUmSpLE0khnjB4CGlFJTSqkXeA748jXXfBn4w9yv/wTYERExesOUJEmSxtZIwngZcHrYx2dyj133mpRSP9AJlIzGACVJkqTxMK6b7yLimYg4GBEHW1tbx/OpJUmSpJsaSRifBVYM+3h57rHrXhMRhcAioP3aL5RSejalVJdSqisrK/tsI5YkSZLGwEjC+A2gNiJWR8Qs4CvA89dc8zzwtdyvfw54JaWURm+YkiRJ0tgqvNUFKaX+iPg68AJQAPxBSulQRHwbOJhSeh74f4D/HBENQAdZPEuSJEmTxi3DGCCl9APgB9c89s1hv+4Gfn50hyZJkiSNH+98J0mSJGEYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBhrEkSZIEGMaSJEkSYBhLkiRJgGEsSZIkAYaxJEmSBBjGkiRJEmAYS5IkSYBhLEmSJAGGsSRJkgQYxpIkSRJgGEuSJEmAYSxJkiQBECmliXniiFbg5IQ8OZQCbRP03JL0efj6JWmymsjXr1UppbJbXTRhYTyRIuJgSqluoschSZ+Wr1+SJqvJ8PrlUgpJkiQJw1iSJEkCpm8YPzvRA5Ckz8jXL0mT1W3/+jUt1xhLkiRJ15quM8aSJEnSxxjGkiRJElMsjCPi1yLijYhoj4juiGiIiN+OiJIbXL8rIirHd5SS9OlExIqI+JOI6IyIixHxpxGxcqLHJWl6i4inIuKViDgXET0RcSYivh8RG65z7bci4pcmYJifypQKY6AY+FPgl4Cnge8CfxN4KSJmAETE1yLi3uH/UEQsiojfiIhZ4zxeSbqpiJgHvAKsA74G/CJQC7waEfMncmySpr1i4E3g68CTwK8DG4H9EbEqIh6KiF8Y/g9EREFE/EpErB3/4d5a4UQPYDSllH7jmod2RUQX8HvAPWT/8Y4CvxURp4BFZN9ovgz8PjA4jsOVpJH4ZaAKWJtSagCIiHeBeuBvA/96AscmaRpLKf0R8EfDH4uIH5G11s8B3wd+KSJ+GWgHlgLPALuBlvEd7chMtRnj62nP/b0fIKV0IKX0FHAG+CngZ4HHUkq/n1LqB4iIr+TeGmiNiMsR8eOI+NqEjF7SdPclYP9QFAOklE4Ar5P9UC9Jt5N8d6WUTqeUfhn4DvCXga8Av5pS+kZK6TxARNyfWyp2JiKuRsSxiPjnETF3IgY/pWaMh0REITALuBv4P4GdKaV3cp+rA74NfAC8DfwZsDMifh/4g1wcVwF/AvwLslnkbcB/jIi5KaXfG+/fj6RpbSPw/17n8UPAz4/zWCTpEyKiACgAVpG10zngjyLiTuCbQDVZb10AvhsRPwT+VS6OV5L12PeAS2Sved8ka7GvjO/vZAqGcUQsIPsXO+QFPv7N4y7gN1JKb0bELuAPgX8L/G9k/1H7U0r/fNjXmwHsAu4AfoVsWYYkjZdi4Px1Hu8AlozzWCTpeg4A9+V+3UD2TnxLRGwFXk0p/Z2I+BbwPvCrZMspyoHzKaX/PvRFIiLI3g27CPyniPjVlFI742hShnHuX1zB8MeGlkEAXcD9wByydcX/FPj/IuLxlFJ/Sul71369lFIn8M+Gff1aslnlbWTrYYaWnPSM7u9EkiRp0vtFYCHZLO8/JDv0YGtKac+1F6aUBoDfHfo4IhaStdrPASuAmcMur+WjpRnjYlLe+S4itgOvDn8spRS3uParKaXnRvC1FwDvkQX2bwGNQC/ZbPHfvNHzSNJYiIhm4M9SSn/7msf/b+DnU0plEzMySfqkiFhMNjP8XErp74zg+v8OPE62fOJt4ArwANnJYo+mlHaN2WCvY1LOGJOdLnH/CK89mPt7zQiv30y2Rubh4T/p5NYtS9J4O0S25u5aG4DD4zwWSbqplNKFiGhgBN0VEXPINhF/K6X0O8Me/8IYDvGmJuWpFCmlSymlg8P/usnlj+T+3jjCLz8v9/e+oQciYgnu/pY0MZ4HNkVE1dADuRsTPZT7nCTdNiKiguzc9ZF012yypbF91zz+S6M8rBGblEspriciFgF/AfwXsvM9E9lU/N8HTgEPppRuuUY4IsrIFo7XA78JzAf+D7JgrnYphaTxlLuJxzvAVbLXokS2J6IIuDuldHkChydpGouI/wG8BbxLtmFuDfBrZPuzHkgpHR/B19hHdmrFPwTayG7M9lO5x8Z9KcVUCuPZZIu5twLLyM4tfp/scOl/l1K6dON/+hNf6zHgt8l+4vkA+B2yneG/aRhLGm+52z//G+AJIICdwN9LKb0/keOSNL1FxD8BfoEsYmcBp8lO8vqtkb4+5d4BG+q3q2Td9ufA/8QwliRJkibGpFxjLEmSJI02w1iSJEnCMJYkSZIAw1iSJEkCDGNJkiQJMIwlSZIkwDCWJEmSAMNYkiRJAuD/B+0evJbcDaCDAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = .3\n", "support = np.linspace(-3*a, 3*a, 1000)\n", "ramsay = norms.RamsayE(a=a)\n", "plot_weights(support, ramsay.weights, ['-3*a', '0', '3*a'], [-3*a, 0, 3*a]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trimmed Mean" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " Least trimmed mean weighting function for the IRLS algorithm\n", " \n", " The psi function scaled by z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " weights(z) = 1 for \\|z\\| <= c\n", " \n", " weights(z) = 0 for \\|z\\| > c\n", "\n" ] } ], "source": [ "help(norms.TrimmedMean.weights)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c = 2\n", "support = np.linspace(-3*c, 3*c, 1000)\n", "trimmed = norms.TrimmedMean(c=c)\n", "plot_weights(support, trimmed.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tukey's Biweight" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function weights in module statsmodels.robust.norms:\n", "\n", "weights(self, z)\n", " Tukey's biweight weighting function for the IRLS algorithm\n", " \n", " The psi function scaled by z\n", " \n", " Parameters\n", " ----------\n", " z : array-like\n", " 1d array\n", " \n", " Returns\n", " -------\n", " weights : array\n", " psi(z) = (1 - (z/c)**2)**2 for \\|z\\| <= R\n", " \n", " psi(z) = 0 for \\|z\\| > R\n", "\n" ] } ], "source": [ "help(norms.TukeyBiweight.weights)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c = 4.685\n", "support = np.linspace(-3*c, 3*c, 1000)\n", "tukey = norms.TukeyBiweight(c=c)\n", "plot_weights(support, tukey.weights, ['-3*c', '0', '3*c'], [-3*c, 0, 3*c]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scale Estimators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Robust estimates of the location" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.array([1, 2, 3, 4, 500])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The mean is not a robust estimator of location" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "102.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The median, on the other hand, is a robust estimator with a breakdown point of 50%" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Analagously for the scale\n", "* The standard deviation is not robust" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "199.00251254695254" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.std()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Median Absolute Deviation\n", "\n", "$$ median_i |X_i - median_j(X_j)|) $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standardized Median Absolute Deviation is a consistent estimator for $\\hat{\\sigma}$\n", "\n", "$$\\hat{\\sigma}=K \\cdot MAD$$\n", "\n", "where $K$ depends on the distribution. For the normal distribution for example,\n", "\n", "$$K = \\Phi^{-1}(.75)$$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.6744897501960817" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.norm.ppf(.75)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 2 3 4 500]\n" ] } ], "source": [ "print(x)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/robust/scale.py:49: FutureWarning: stand_mad is deprecated and will be removed in 0.7.0. Use mad instead.\n", " \"instead.\", FutureWarning)\n" ] }, { "data": { "text/plain": [ "1.482602218505602" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.robust.scale.stand_mad(x)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.4142135623730951" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array([1,2,3,4,5.]).std()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The default for Robust Linear Models is MAD\n", "* another popular choice is Huber's proposal 2" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(12345)\n", "fat_tails = stats.t(6).rvs(40)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kde = sm.nonparametric.KDEUnivariate(fat_tails)\n", "kde.fit()\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(kde.support, kde.density);" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0688231044810875 1.3471633229698652\n" ] } ], "source": [ "print(fat_tails.mean(), fat_tails.std())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.0688231044810875, 1.3471633229698652)\n" ] } ], "source": [ "print(stats.norm.fit(fat_tails))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6, 0.03900918717027818, 1.0564230978488927)\n" ] } ], "source": [ "print(stats.t.fit(fat_tails, f0=6))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.04048984333271795 1.1557140047569665\n" ] } ], "source": [ "huber = sm.robust.scale.Huber()\n", "loc, scale = huber(fat_tails)\n", "print(loc, scale)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/robust/scale.py:49: FutureWarning: stand_mad is deprecated and will be removed in 0.7.0. Use mad instead.\n", " \"instead.\", FutureWarning)\n" ] }, { "data": { "text/plain": [ "1.115335001165415" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.robust.stand_mad(fat_tails)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/robust/scale.py:49: FutureWarning: stand_mad is deprecated and will be removed in 0.7.0. Use mad instead.\n", " \"instead.\", FutureWarning)\n" ] }, { "data": { "text/plain": [ "1.0483916565928972" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.robust.stand_mad(fat_tails, c=stats.t(6).ppf(.75))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.115335001165415" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.robust.scale.mad(fat_tails)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Duncan's Occupational Prestige data - M-estimation for outliers" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.graphics.api import abline_plot\n", "from statsmodels.formula.api import ols, rlm" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prestige = sm.datasets.get_rdataset(\"Duncan\", \"car\", cache=True).data" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " type income education prestige\n", "accountant prof 62 86 82\n", "pilot prof 72 76 83\n", "architect prof 75 92 90\n", "author prof 55 90 76\n", "chemist prof 64 86 90\n", "minister prof 21 84 87\n", "professor prof 64 93 93\n", "dentist prof 80 100 90\n", "reporter wc 67 87 52\n", "engineer prof 72 86 88\n" ] } ], "source": [ "print(prestige.head(10))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " after removing the cwd from sys.path.\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x864 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,12))\n", "ax1 = fig.add_subplot(211, xlabel='Income', ylabel='Prestige')\n", "ax1.scatter(prestige.income, prestige.prestige)\n", "xy_outlier = prestige.ix['minister'][['income','prestige']]\n", "ax1.annotate('Minister', xy_outlier, xy_outlier+1, fontsize=16)\n", "ax2 = fig.add_subplot(212, xlabel='Education',\n", " ylabel='Prestige')\n", "ax2.scatter(prestige.education, prestige.prestige);" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: prestige R-squared: 0.828\n", "Model: OLS Adj. R-squared: 0.820\n", "Method: Least Squares F-statistic: 101.2\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 8.65e-17\n", "Time: 07:40:51 Log-Likelihood: -178.98\n", "No. Observations: 45 AIC: 364.0\n", "Df Residuals: 42 BIC: 369.4\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -6.0647 4.272 -1.420 0.163 -14.686 2.556\n", "income 0.5987 0.120 5.003 0.000 0.357 0.840\n", "education 0.5458 0.098 5.555 0.000 0.348 0.744\n", "==============================================================================\n", "Omnibus: 1.279 Durbin-Watson: 1.458\n", "Prob(Omnibus): 0.528 Jarque-Bera (JB): 0.520\n", "Skew: 0.155 Prob(JB): 0.771\n", "Kurtosis: 3.426 Cond. No. 163.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "ols_model = ols('prestige ~ income + education', prestige).fit()\n", "print(ols_model.summary())" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accountant 0.303900\n", "pilot 0.340920\n", "architect 0.072256\n", "author 0.000711\n", "chemist 0.826578\n", "minister 3.134519\n", "professor 0.768277\n", "dentist -0.498082\n", "reporter -2.397022\n", "engineer 0.306225\n", "undertaker -0.187339\n", "lawyer -0.303082\n", "physician 0.355687\n", "welfare.worker -0.411406\n", "teacher 0.050510\n", "conductor -1.704032\n", "contractor 2.043805\n", "factory.owner 1.602429\n", "store.manager 0.142425\n", "banker 0.508388\n", "bookkeeper -0.902388\n", "mail.carrier -1.433249\n", "insurance.agent -1.930919\n", "store.clerk -1.760491\n", "carpenter 1.068858\n", "electrician 0.731949\n", "RR.engineer 0.808922\n", "machinist 1.887047\n", "auto.repairman 0.522735\n", "plumber -0.377954\n", "gas.stn.attendant -0.666596\n", "coal.miner 1.018527\n", "streetcar.motorman -1.104485\n", "taxi.driver 0.023322\n", "truck.driver -0.129227\n", "machine.operator 0.499922\n", "barber 0.173805\n", "bartender -0.902422\n", "shoe.shiner -0.429357\n", "cook 0.127207\n", "soda.clerk -0.883095\n", "watchman -0.513502\n", "janitor -0.079890\n", "policeman 0.078847\n", "waiter -0.475972\n", "Name: student_resid, dtype: float64\n" ] } ], "source": [ "infl = ols_model.get_influence()\n", "student = infl.summary_frame()['student_resid']\n", "print(student)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "minister 3.134519\n", "reporter -2.397022\n", "contractor 2.043805\n", "Name: student_resid, dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "print(student.ix[np.abs(student) > 2])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dfb_Intercept 0.144937\n", "dfb_income -1.220939\n", "dfb_education 1.263019\n", "cooks_d 0.566380\n", "standard_resid 2.849416\n", "hat_diag 0.173058\n", "dffits_internal 1.303510\n", "student_resid 3.134519\n", "dffits 1.433935\n", "Name: minister, dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "print(infl.summary_frame().ix['minister'])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " student_resid unadj_p sidak(p)\n", "minister 3.134519 0.003177 0.133421\n", "reporter -2.397022 0.021170 0.618213\n", "contractor 2.043805 0.047433 0.887721\n", "insurance.agent -1.930919 0.060428 0.939485\n", "machinist 1.887047 0.066248 0.954247\n", "store.clerk -1.760491 0.085783 0.982331\n", "conductor -1.704032 0.095944 0.989315\n", "factory.owner 1.602429 0.116738 0.996250\n", "mail.carrier -1.433249 0.159369 0.999595\n", "streetcar.motorman -1.104485 0.275823 1.000000\n", "carpenter 1.068858 0.291386 1.000000\n", "coal.miner 1.018527 0.314400 1.000000\n", "bartender -0.902422 0.372104 1.000000\n", "bookkeeper -0.902388 0.372122 1.000000\n", "soda.clerk -0.883095 0.382334 1.000000\n", "chemist 0.826578 0.413261 1.000000\n", "RR.engineer 0.808922 0.423229 1.000000\n", "professor 0.768277 0.446725 1.000000\n", "electrician 0.731949 0.468363 1.000000\n", "gas.stn.attendant -0.666596 0.508764 1.000000\n", "auto.repairman 0.522735 0.603972 1.000000\n", "watchman -0.513502 0.610357 1.000000\n", "banker 0.508388 0.613906 1.000000\n", "machine.operator 0.499922 0.619802 1.000000\n", "dentist -0.498082 0.621088 1.000000\n", "waiter -0.475972 0.636621 1.000000\n", "shoe.shiner -0.429357 0.669912 1.000000\n", "welfare.worker -0.411406 0.682918 1.000000\n", "plumber -0.377954 0.707414 1.000000\n", "physician 0.355687 0.723898 1.000000\n", "pilot 0.340920 0.734905 1.000000\n", "engineer 0.306225 0.760983 1.000000\n", "accountant 0.303900 0.762741 1.000000\n", "lawyer -0.303082 0.763360 1.000000\n", "undertaker -0.187339 0.852319 1.000000\n", "barber 0.173805 0.862874 1.000000\n", "store.manager 0.142425 0.887442 1.000000\n", "truck.driver -0.129227 0.897810 1.000000\n", "cook 0.127207 0.899399 1.000000\n", "janitor -0.079890 0.936713 1.000000\n", "policeman 0.078847 0.937538 1.000000\n", "architect 0.072256 0.942750 1.000000\n", "teacher 0.050510 0.959961 1.000000\n", "taxi.driver 0.023322 0.981507 1.000000\n", "author 0.000711 0.999436 1.000000\n" ] } ], "source": [ "sidak = ols_model.outlier_test('sidak')\n", "sidak.sort_values('unadj_p', inplace=True)\n", "print(sidak)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " student_resid unadj_p fdr_bh(p)\n", "minister 3.134519 0.003177 0.142974\n", "reporter -2.397022 0.021170 0.476332\n", "contractor 2.043805 0.047433 0.596233\n", "insurance.agent -1.930919 0.060428 0.596233\n", "machinist 1.887047 0.066248 0.596233\n", "store.clerk -1.760491 0.085783 0.616782\n", "conductor -1.704032 0.095944 0.616782\n", "factory.owner 1.602429 0.116738 0.656653\n", "mail.carrier -1.433249 0.159369 0.796844\n", "streetcar.motorman -1.104485 0.275823 0.999436\n", "carpenter 1.068858 0.291386 0.999436\n", "coal.miner 1.018527 0.314400 0.999436\n", "bartender -0.902422 0.372104 0.999436\n", "bookkeeper -0.902388 0.372122 0.999436\n", "soda.clerk -0.883095 0.382334 0.999436\n", "chemist 0.826578 0.413261 0.999436\n", "RR.engineer 0.808922 0.423229 0.999436\n", "professor 0.768277 0.446725 0.999436\n", "electrician 0.731949 0.468363 0.999436\n", "gas.stn.attendant -0.666596 0.508764 0.999436\n", "auto.repairman 0.522735 0.603972 0.999436\n", "watchman -0.513502 0.610357 0.999436\n", "banker 0.508388 0.613906 0.999436\n", "machine.operator 0.499922 0.619802 0.999436\n", "dentist -0.498082 0.621088 0.999436\n", "waiter -0.475972 0.636621 0.999436\n", "shoe.shiner -0.429357 0.669912 0.999436\n", "welfare.worker -0.411406 0.682918 0.999436\n", "plumber -0.377954 0.707414 0.999436\n", "physician 0.355687 0.723898 0.999436\n", "pilot 0.340920 0.734905 0.999436\n", "engineer 0.306225 0.760983 0.999436\n", "accountant 0.303900 0.762741 0.999436\n", "lawyer -0.303082 0.763360 0.999436\n", "undertaker -0.187339 0.852319 0.999436\n", "barber 0.173805 0.862874 0.999436\n", "store.manager 0.142425 0.887442 0.999436\n", "truck.driver -0.129227 0.897810 0.999436\n", "cook 0.127207 0.899399 0.999436\n", "janitor -0.079890 0.936713 0.999436\n", "policeman 0.078847 0.937538 0.999436\n", "architect 0.072256 0.942750 0.999436\n", "teacher 0.050510 0.959961 0.999436\n", "taxi.driver 0.023322 0.981507 0.999436\n", "author 0.000711 0.999436 0.999436\n" ] } ], "source": [ "fdr = ols_model.outlier_test('fdr_bh')\n", "fdr.sort_values('unadj_p', inplace=True)\n", "print(fdr)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Robust linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: prestige No. Observations: 45\n", "Model: RLM Df Residuals: 42\n", "Method: IRLS Df Model: 2\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Fri, 12 Jun 2020 \n", "Time: 07:40:53 \n", "No. Iterations: 18 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept -7.1107 3.879 -1.833 0.067 -14.713 0.492\n", "income 0.7015 0.109 6.456 0.000 0.489 0.914\n", "education 0.4854 0.089 5.441 0.000 0.311 0.660\n", "==============================================================================\n", "\n", "If the model instance has been used for another fit with different fit\n", "parameters, then the fit options might not be the correct ones anymore .\n" ] } ], "source": [ "rlm_model = rlm('prestige ~ income + education', prestige).fit()\n", "print(rlm_model.summary())" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accountant 1.000000\n", "pilot 1.000000\n", "architect 1.000000\n", "author 1.000000\n", "chemist 1.000000\n", "minister 0.344596\n", "professor 1.000000\n", "dentist 1.000000\n", "reporter 0.441669\n", "engineer 1.000000\n", "undertaker 1.000000\n", "lawyer 1.000000\n", "physician 1.000000\n", "welfare.worker 1.000000\n", "teacher 1.000000\n", "conductor 0.538445\n", "contractor 0.552262\n", "factory.owner 0.706169\n", "store.manager 1.000000\n", "banker 1.000000\n", "bookkeeper 1.000000\n", "mail.carrier 0.690764\n", "insurance.agent 0.533499\n", "store.clerk 0.618656\n", "carpenter 0.935848\n", "electrician 1.000000\n", "RR.engineer 1.000000\n", "machinist 0.570360\n", "auto.repairman 1.000000\n", "plumber 1.000000\n", "gas.stn.attendant 1.000000\n", "coal.miner 0.963821\n", "streetcar.motorman 0.832870\n", "taxi.driver 1.000000\n", "truck.driver 1.000000\n", "machine.operator 1.000000\n", "barber 1.000000\n", "bartender 1.000000\n", "shoe.shiner 1.000000\n", "cook 1.000000\n", "soda.clerk 1.000000\n", "watchman 1.000000\n", "janitor 1.000000\n", "policeman 1.000000\n", "waiter 1.000000\n", "dtype: float64\n" ] } ], "source": [ "print(rlm_model.weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hertzprung Russell data for Star Cluster CYG 0B1 - Leverage Points" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Data is on the luminosity and temperature of 47 stars in the direction of Cygnus." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta = sm.datasets.get_rdataset(\"starsCYG\", \"robustbase\", cache=True).data" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " from ipykernel import kernelapp as app\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from matplotlib.patches import Ellipse\n", "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111, xlabel='log(Temp)', ylabel='log(Light)', title='Hertzsprung-Russell Diagram of Star Cluster CYG OB1')\n", "ax.scatter(*dta.values.T)\n", "# highlight outliers\n", "e = Ellipse((3.5, 6), .2, 1, alpha=.25, color='r')\n", "ax.add_patch(e);\n", "ax.annotate('Red giants', xy=(3.6, 6), xytext=(3.8, 6),\n", " arrowprops=dict(facecolor='black', shrink=0.05, width=2),\n", " horizontalalignment='left', verticalalignment='bottom',\n", " clip_on=True, # clip to the axes bounding box\n", " fontsize=16,\n", " )\n", "# annotate these with their index\n", "for i,row in dta.ix[dta['log.Te'] < 3.8].iterrows():\n", " ax.annotate(i, row, row + .01, fontsize=14)\n", "xlim, ylim = ax.get_xlim(), ax.get_ylim()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "import os.path\n", "Image(filename='star_diagram.png' if os.path.exists('star_diagram.png') else '../examples/notebooks/star_diagram.png')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = dta['log.light']\n", "X = sm.add_constant(dta['log.Te'], prepend=True)\n", "ols_model = sm.OLS(y, X).fit()\n", "abline_plot(model_results=ols_model, ax=ax)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rlm_mod = sm.RLM(y, X, sm.robust.norms.TrimmedMean(.5)).fit()\n", "abline_plot(model_results=rlm_mod, ax=ax, color='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Why? Because M-estimators are not robust to leverage points." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infl = ols_model.get_influence()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " This is separate from the ipykernel package so we can avoid doing imports until\n" ] }, { "data": { "text/plain": [ "10 0.194103\n", "19 0.194103\n", "29 0.198344\n", "33 0.194103\n", "Name: hat_diag, dtype: float64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h_bar = 2*(ols_model.df_model + 1 )/ols_model.nobs\n", "hat_diag = infl.summary_frame()['hat_diag']\n", "hat_diag.ix[hat_diag > h_bar]" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " student_resid unadj_p sidak(p)\n", "16 -2.049393 0.046415 0.892872\n", "13 -2.035329 0.047868 0.900286\n", "33 1.905847 0.063216 0.953543\n", "18 -1.575505 0.122304 0.997826\n", "1 1.522185 0.135118 0.998911\n", "3 1.522185 0.135118 0.998911\n", "21 -1.450418 0.154034 0.999615\n", "17 -1.426675 0.160731 0.999735\n", "29 1.388520 0.171969 0.999859\n", "14 -1.374733 0.176175 0.999889\n", "35 1.346543 0.185023 0.999933\n", "34 -1.272159 0.209999 0.999985\n", "28 -1.186946 0.241618 0.999998\n", "20 -1.150621 0.256103 0.999999\n", "44 1.134779 0.262612 0.999999\n", "39 1.091886 0.280826 1.000000\n", "19 1.064878 0.292740 1.000000\n", "6 -1.026873 0.310093 1.000000\n", "30 -1.009096 0.318446 1.000000\n", "22 -0.979768 0.332557 1.000000\n", "8 0.961218 0.341695 1.000000\n", "5 0.913802 0.365801 1.000000\n", "11 0.871997 0.387943 1.000000\n", "12 0.856685 0.396261 1.000000\n", "46 -0.833923 0.408829 1.000000\n", "10 0.743920 0.460879 1.000000\n", "42 0.727179 0.470968 1.000000\n", "15 -0.689258 0.494280 1.000000\n", "43 0.688272 0.494895 1.000000\n", "7 0.655712 0.515424 1.000000\n", "40 -0.646396 0.521381 1.000000\n", "26 -0.640978 0.524862 1.000000\n", "25 -0.545561 0.588123 1.000000\n", "37 0.472819 0.638680 1.000000\n", "32 0.472819 0.638680 1.000000\n", "38 0.462187 0.646225 1.000000\n", "0 0.430686 0.668799 1.000000\n", "31 0.341726 0.734184 1.000000\n", "36 0.318911 0.751303 1.000000\n", "4 0.307890 0.759619 1.000000\n", "9 0.235114 0.815211 1.000000\n", "41 0.187732 0.851950 1.000000\n", "2 -0.182093 0.856346 1.000000\n", "23 -0.156014 0.876736 1.000000\n", "27 -0.147406 0.883485 1.000000\n", "24 0.065195 0.948314 1.000000\n", "45 0.045675 0.963776 1.000000\n" ] } ], "source": [ "sidak2 = ols_model.outlier_test('sidak')\n", "sidak2.sort_values('unadj_p', inplace=True)\n", "print(sidak2)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " student_resid unadj_p fdr_bh(p)\n", "16 -2.049393 0.046415 0.764747\n", "13 -2.035329 0.047868 0.764747\n", "33 1.905847 0.063216 0.764747\n", "18 -1.575505 0.122304 0.764747\n", "1 1.522185 0.135118 0.764747\n", "3 1.522185 0.135118 0.764747\n", "21 -1.450418 0.154034 0.764747\n", "17 -1.426675 0.160731 0.764747\n", "29 1.388520 0.171969 0.764747\n", "14 -1.374733 0.176175 0.764747\n", "35 1.346543 0.185023 0.764747\n", "34 -1.272159 0.209999 0.764747\n", "28 -1.186946 0.241618 0.764747\n", "20 -1.150621 0.256103 0.764747\n", "44 1.134779 0.262612 0.764747\n", "39 1.091886 0.280826 0.764747\n", "19 1.064878 0.292740 0.764747\n", "6 -1.026873 0.310093 0.764747\n", "30 -1.009096 0.318446 0.764747\n", "22 -0.979768 0.332557 0.764747\n", "8 0.961218 0.341695 0.764747\n", "5 0.913802 0.365801 0.768599\n", "11 0.871997 0.387943 0.768599\n", "12 0.856685 0.396261 0.768599\n", "46 -0.833923 0.408829 0.768599\n", "10 0.743920 0.460879 0.770890\n", "42 0.727179 0.470968 0.770890\n", "15 -0.689258 0.494280 0.770890\n", "43 0.688272 0.494895 0.770890\n", "7 0.655712 0.515424 0.770890\n", "40 -0.646396 0.521381 0.770890\n", "26 -0.640978 0.524862 0.770890\n", "25 -0.545561 0.588123 0.837630\n", "37 0.472819 0.638680 0.843682\n", "32 0.472819 0.638680 0.843682\n", "38 0.462187 0.646225 0.843682\n", "0 0.430686 0.668799 0.849556\n", "31 0.341726 0.734184 0.892552\n", "36 0.318911 0.751303 0.892552\n", "4 0.307890 0.759619 0.892552\n", "9 0.235114 0.815211 0.922751\n", "41 0.187732 0.851950 0.922751\n", "2 -0.182093 0.856346 0.922751\n", "23 -0.156014 0.876736 0.922751\n", "27 -0.147406 0.883485 0.922751\n", "24 0.065195 0.948314 0.963776\n", "45 0.045675 0.963776 0.963776\n" ] } ], "source": [ "fdr2 = ols_model.outlier_test('fdr_bh')\n", "fdr2.sort_values('unadj_p', inplace=True)\n", "print(fdr2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Let's delete that line" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [], "source": [ "del ax.lines[-1]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights = np.ones(len(X))\n", "weights[X[X['log.Te'] < 3.8].index.values - 1] = 0\n", "wls_model = sm.WLS(y, X, weights=weights).fit()\n", "abline_plot(model_results=wls_model, ax=ax, color='green')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* MM estimators are good for this type of problem, unfortunately, we don't yet have these yet. \n", "* It's being worked on, but it gives a good excuse to look at the R cell magics in the notebook." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yy = y.values[:,None]\n", "xx = X['log.Te'].values[:,None]" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'rpy2'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-59-9555d23845d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext rpy2.ipython'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'R library(robustbase)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 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"\u001b[0;32m/usr/lib/python3/dist-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m 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"execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "UsageError: Line magic function `%R` not found.\n" ] } ], "source": [ "%R print(mod)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'params' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-61-1b9f0c7a8c29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'params' is not defined" ] } ], "source": [ "print(params)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'params' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-62-c69ec2593f52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabline_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintercept\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m 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"all_betas = []\n", "for i in range(mc_iter):\n", " y = np.dot(X, beta_true) + np.random.normal(size=200)\n", " random_idx = np.random.randint(0, nobs, size=int(contaminate * nobs))\n", " y[random_idx] = np.random.uniform(-750, 750)\n", " beta_hat = sm.RLM(y, X).fit().params\n", " all_betas.append(beta_hat)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_betas = np.asarray(all_betas)\n", "se_loss = lambda x : np.linalg.norm(x, ord=2)**2\n", "se_beta = lmap(se_loss, all_betas - beta_true)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Squared error loss" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.4450294873068618" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(se_beta).mean()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.99711706, 0.99898147, 2.49909344, 2.99712918, -3.99626521])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_betas.mean(0)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3. , 1. , 2.5, 3. , -4. ])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_true" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.236091328675419e-05" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "se_loss(all_betas.mean(0) - beta_true)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", 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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# State space modeling: Local Linear Trends" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook describes how to extend the Statsmodels statespace classes to create and estimate a custom model. Here we develop a local linear trend model.\n", "\n", "The Local Linear Trend model has the form (see Durbin and Koopman 2012, Chapter 3.2 for all notation and details):\n", "\n", "$$\n", "\\begin{align}\n", "y_t & = \\mu_t + \\varepsilon_t \\qquad & \\varepsilon_t \\sim\n", " N(0, \\sigma_\\varepsilon^2) \\\\\n", "\\mu_{t+1} & = \\mu_t + \\nu_t + \\xi_t & \\xi_t \\sim N(0, \\sigma_\\xi^2) \\\\\n", "\\nu_{t+1} & = \\nu_t + \\zeta_t & \\zeta_t \\sim N(0, \\sigma_\\zeta^2)\n", "\\end{align}\n", "$$\n", "\n", "It is easy to see that this can be cast into state space form as:\n", "\n", "$$\n", "\\begin{align}\n", "y_t & = \\begin{pmatrix} 1 & 0 \\end{pmatrix} \\begin{pmatrix} \\mu_t \\\\ \\nu_t \\end{pmatrix} + \\varepsilon_t \\\\\n", "\\begin{pmatrix} \\mu_{t+1} \\\\ \\nu_{t+1} \\end{pmatrix} & = \\begin{bmatrix} 1 & 1 \\\\ 0 & 1 \\end{bmatrix} \\begin{pmatrix} \\mu_t \\\\ \\nu_t \\end{pmatrix} + \\begin{pmatrix} \\xi_t \\\\ \\zeta_t \\end{pmatrix}\n", "\\end{align}\n", "$$\n", "\n", "Notice that much of the state space representation is composed of known values; in fact the only parts in which parameters to be estimated appear are in the variance / covariance matrices:\n", "\n", "$$\n", "\\begin{align}\n", "H_t & = \\begin{bmatrix} \\sigma_\\varepsilon^2 \\end{bmatrix} \\\\\n", "Q_t & = \\begin{bmatrix} \\sigma_\\xi^2 & 0 \\\\ 0 & \\sigma_\\zeta^2 \\end{bmatrix}\n", "\\end{align}\n", "$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import norm\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To take advantage of the existing infrastructure, including Kalman filtering and maximum likelihood estimation, we create a new class which extends from `statsmodels.tsa.statespace.MLEModel`. There are a number of things that must be specified:\n", "\n", "1. **k_states**, **k_posdef**: These two parameters must be provided to the base classes in initialization. The inform the statespace model about the size of, respectively, the state vector, above $\\begin{pmatrix} \\mu_t & \\nu_t \\end{pmatrix}'$, and the state error vector, above $\\begin{pmatrix} \\xi_t & \\zeta_t \\end{pmatrix}'$. Note that the dimension of the endogenous vector does not have to be specified, since it can be inferred from the `endog` array.\n", "2. **update**: The method `update`, with argument `params`, must be specified (it is used when `fit()` is called to calculate the MLE). It takes the parameters and fills them into the appropriate state space matrices. For example, below, the `params` vector contains variance parameters $\\begin{pmatrix} \\sigma_\\varepsilon^2 & \\sigma_\\xi^2 & \\sigma_\\zeta^2\\end{pmatrix}$, and the `update` method must place them in the observation and state covariance matrices. More generally, the parameter vector might be mapped into many different places in all of the statespace matrices.\n", "3. **statespace matrices**: by default, all state space matrices (`obs_intercept, design, obs_cov, state_intercept, transition, selection, state_cov`) are set to zeros. Values that are fixed (like the ones in the design and transition matrices here) can be set in initialization, whereas values that vary with the parameters should be set in the `update` method. Note that it is easy to forget to set the selection matrix, which is often just the identity matrix (as it is here), but not setting it will lead to a very different model (one where there is not a stochastic component to the transition equation).\n", "4. **start params**: start parameters must be set, even if it is just a vector of zeros, although often good start parameters can be found from the data. Maximum likelihood estimation by gradient methods (as employed here) can be sensitive to the starting parameters, so it is important to select good ones if possible. Here it does not matter too much (although as variances, they should't be set zero).\n", "5. **initialization**: in addition to defined state space matrices, all state space models must be initialized with the mean and variance for the initial distribution of the state vector. If the distribution is known, `initialize_known(initial_state, initial_state_cov)` can be called, or if the model is stationary (e.g. an ARMA model), `initialize_stationary` can be used. Otherwise, `initialize_approximate_diffuse` is a reasonable generic initialization (exact diffuse initialization is not yet available). Since the local linear trend model is not stationary (it is composed of random walks) and since the distribution is not generally known, we use `initialize_approximate_diffuse` below.\n", "\n", "The above are the minimum necessary for a successful model. There are also a number of things that do not have to be set, but which may be helpful or important for some applications:\n", "\n", "1. **transform / untransform**: when `fit` is called, the optimizer in the background will use gradient methods to select the parameters that maximize the likelihood function. By default it uses unbounded optimization, which means that it may select any parameter value. In many cases, that is not the desired behavior; variances, for example, cannot be negative. To get around this, the `transform` method takes the unconstrained vector of parameters provided by the optimizer and returns a constrained vector of parameters used in likelihood evaluation. `untransform` provides the reverse operation.\n", "2. **param_names**: this internal method can be used to set names for the estimated parameters so that e.g. the summary provides meaningful names. If not present, parameters are named `param0`, `param1`, etc." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "Univariate Local Linear Trend Model\n", "\"\"\"\n", "class LocalLinearTrend(sm.tsa.statespace.MLEModel):\n", " def __init__(self, endog):\n", " # Model order\n", " k_states = k_posdef = 2\n", "\n", " # Initialize the statespace\n", " super(LocalLinearTrend, self).__init__(\n", " endog, k_states=k_states, k_posdef=k_posdef,\n", " initialization='approximate_diffuse',\n", " loglikelihood_burn=k_states\n", " )\n", "\n", " # Initialize the matrices\n", " self.ssm['design'] = np.array([1, 0])\n", " self.ssm['transition'] = np.array([[1, 1],\n", " [0, 1]])\n", " self.ssm['selection'] = np.eye(k_states)\n", "\n", " # Cache some indices\n", " self._state_cov_idx = ('state_cov',) + np.diag_indices(k_posdef)\n", "\n", " @property\n", " def param_names(self):\n", " return ['sigma2.measurement', 'sigma2.level', 'sigma2.trend']\n", "\n", " @property\n", " def start_params(self):\n", " return [np.std(self.endog)]*3\n", "\n", " def transform_params(self, unconstrained):\n", " return unconstrained**2\n", "\n", " def untransform_params(self, constrained):\n", " return constrained**0.5\n", "\n", " def update(self, params, *args, **kwargs):\n", " params = super(LocalLinearTrend, self).update(params, *args, **kwargs)\n", " \n", " # Observation covariance\n", " self.ssm['obs_cov',0,0] = params[0]\n", "\n", " # State covariance\n", " self.ssm[self._state_cov_idx] = params[1:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this simple model, we can estimate the parameters from a local linear trend model. The following example is from Commandeur and Koopman (2007), section 3.4., modeling motor vehicle fatalities in Finland." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac82cc50>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, 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connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xac82cc50>: Failed to establish a new connection: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m 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Failed to establish a new connection: [Errno 111] Connection refused')))", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-adacc4910a58>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Download the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mck\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mzipped\u001b[0m \u001b[0;34m=\u001b[0m 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request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0;32mif\u001b[0m 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HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac82cc50>: Failed to establish a new connection: [Errno 111] Connection refused')))" ] } ], "source": [ "import requests\n", "from io import BytesIO\n", "from zipfile import ZipFile\n", " \n", "# Download the dataset\n", "ck = requests.get('http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip').content\n", "zipped = ZipFile(BytesIO(ck))\n", "df = pd.read_table(\n", " BytesIO(zipped.read('OxCodeIntroStateSpaceBook/Chapter_2/NorwayFinland.txt')),\n", " skiprows=1, header=None, sep='\\s+', engine='python',\n", " names=['date','nf', 'ff']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we defined the local linear trend model as extending from `MLEModel`, the `fit()` method is immediately available, just as in other Statsmodels maximum likelihood classes. Similarly, the returned results class supports many of the same post-estimation results, like the `summary` method.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'df' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-1cb9a80a34dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%d-01-01'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%d-01-01'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Log transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lff'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ff'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "# Load Dataset\n", "df.index = pd.date_range(start='%d-01-01' % df.date[0], end='%d-01-01' % df.iloc[-1, 0], freq='AS')\n", "\n", "# Log transform\n", "df['lff'] = np.log(df['ff'])\n", "\n", "# Setup the model\n", "mod = LocalLinearTrend(df['lff'])\n", "\n", "# Fit it using MLE (recall that we are fitting the three variance parameters)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can do post-estimation prediction and forecasting. Notice that the end period can be specified as a date." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-5-575a2795f6fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Perform prediction and forecasting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mforecast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_forecast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2014'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'res' is not defined" ] } ], "source": [ "# Perform prediction and forecasting\n", "predict = res.get_prediction()\n", "forecast = res.get_forecast('2014')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'df' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-acfd2ead849d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Plot the results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lff'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'k.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Observations'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'One-step-ahead Prediction'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mpredict_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" ] }, { "data": { "image/png": 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V1zL7T8zO+TV1H03y4iTp7s9ndo3dTUlOJXkqybsmPa79BQAYxzvIAwAMJLYAAAYSWwAAA4ktAICBxBYAwEBiCwBgILEFADDQ/wE8pKTmGm5RBwAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 720x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10,4))\n", "\n", "# Plot the results\n", "df['lff'].plot(ax=ax, style='k.', label='Observations')\n", "predict.predicted_mean.plot(ax=ax, label='One-step-ahead Prediction')\n", "predict_ci = predict.conf_int(alpha=0.05)\n", "predict_index = np.arange(len(predict_ci))\n", "ax.fill_between(predict_index[2:], predict_ci.iloc[2:, 0], predict_ci.iloc[2:, 1], alpha=0.1)\n", "\n", "forecast.predicted_mean.plot(ax=ax, style='r', label='Forecast')\n", "forecast_ci = forecast.conf_int()\n", "forecast_index = np.arange(len(predict_ci), len(predict_ci) + len(forecast_ci))\n", "ax.fill_between(forecast_index, forecast_ci.iloc[:, 0], forecast_ci.iloc[:, 1], alpha=0.1)\n", "\n", "# Cleanup the image\n", "ax.set_ylim((4, 8));\n", "legend = ax.legend(loc='lower left');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", " Commandeur, Jacques J. F., and Siem Jan Koopman. 2007.\n", " An Introduction to State Space Time Series Analysis.\n", " Oxford\u202f; New York: Oxford University Press.\n", "\n", " Durbin, James, and Siem Jan Koopman. 2012.\n", " Time Series Analysis by State Space Methods: Second Edition.\n", " Oxford University Press." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmp6ks_61p4/4b142592-8b80-4399-bc41-1e89cbc0d928 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SARIMAX: Model selection, missing data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA models. The novel feature is the ability of the model to work on datasets with missing values." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import norm\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.ssfpack.com/files/DK-data.zip (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xabec9a30>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3.7/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1243\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1244\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m 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967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xabec9a30>: Failed to establish a new connection: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMaxRetryError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m \u001b[0;32mor\u001b[0m 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dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.ssfpack.com/files/DK-data.zip'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mzipped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, 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"import requests\n", "from io import BytesIO\n", "from zipfile import ZipFile\n", "\n", "# Download the dataset\n", "dk = requests.get('http://www.ssfpack.com/files/DK-data.zip').content\n", "f = BytesIO(dk)\n", "zipped = ZipFile(f)\n", "df = pd.read_table(\n", " BytesIO(zipped.read('internet.dat')),\n", " skiprows=1, header=None, sep='\\s+', engine='python',\n", " names=['internet','dinternet']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Selection\n", "\n", "As in Durbin and Koopman, we force a number of the values to be missing." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'df' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-70c0b0b5593e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get the basic series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdta_full\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdinternet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdta_miss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdta_full\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Remove datapoints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined" ] } ], "source": [ "# Get the basic series\n", "dta_full = df.dinternet[1:].values\n", "dta_miss = dta_full.copy()\n", "\n", "# Remove datapoints\n", "missing = np.r_[6,16,26,36,46,56,66,72,73,74,75,76,86,96]-1\n", "dta_miss[missing] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can consider model selection using the Akaike information criteria (AIC), but running the model for each variant and selecting the model with the lowest AIC value.\n", "\n", "There are a couple of things to note here:\n", "\n", "- When running such a large batch of models, particularly when the autoregressive and moving average orders become large, there is the possibility of poor maximum likelihood convergence. Below we ignore the warnings since this example is illustrative.\n", "- We use the option `enforce_invertibility=False`, which allows the moving average polynomial to be non-invertible, so that more of the models are estimable.\n", "- Several of the models do not produce good results, and their AIC value is set to NaN. This is not surprising, as Durbin and Koopman note numerical problems with the high order models." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'dta_full' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-5-735b63dc22a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Estimate the model with no missing datapoints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta_full\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menforce_invertibility\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'dta_full' is not defined" ] } ], "source": [ "import warnings\n", "\n", "aic_full = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "aic_miss = pd.DataFrame(np.zeros((6,6), dtype=float))\n", "\n", "warnings.simplefilter('ignore')\n", "\n", "# Iterate over all ARMA(p,q) models with p,q in [0,6]\n", "for p in range(6):\n", " for q in range(6):\n", " if p == 0 and q == 0:\n", " continue\n", " \n", " # Estimate the model with no missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_full, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_full.iloc[p,q] = res.aic\n", " except:\n", " aic_full.iloc[p,q] = np.nan\n", " \n", " # Estimate the model with missing datapoints\n", " mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(p,0,q), enforce_invertibility=False)\n", " try:\n", " res = mod.fit()\n", " aic_miss.iloc[p,q] = res.aic\n", " except:\n", " aic_miss.iloc[p,q] = np.nan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the models estimated over the full (non-missing) dataset, the AIC chooses ARMA(1,1) or ARMA(3,0). Durbin and Koopman suggest the ARMA(1,1) specification is better due to parsimony.\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.1} ~~ \\text{AIC for different ARMA models.}\\\\\n", "\\newcommand{\\r}[1]{{\\color{red}{#1}}}\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 549.81 & 519.87 & 520.27 & 519.38 & 518.86 \\\\\n", "1 & 529.24 & \\r{514.30} & 516.25 & 514.58 & 515.10 & 516.28 \\\\\n", "2 & 522.18 & 516.29 & 517.16 & 515.77 & 513.24 & 514.73 \\\\\n", "3 & \\r{511.99} & 513.94 & 515.92 & 512.06 & 513.72 & 514.50 \\\\\n", "4 & 513.93 & 512.89 & nan & nan & 514.81 & 516.08 \\\\\n", "5 & 515.86 & 517.64 & nan & nan & nan & nan \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "For the models estimated over missing dataset, the AIC chooses ARMA(1,1)\n", "\n", "$$\n", "\\text{Replication of:}\\\\\n", "\\textbf{Table 8.2} ~~ \\text{AIC for different ARMA models with missing observations.}\\\\\n", "\\begin{array}{lrrrrrr}\n", "\\hline\n", "q & 0 & 1 & 2 & 3 & 4 & 5 \\\\\n", "\\hline\n", "p & {} & {} & {} & {} & {} & {} \\\\\n", "0 & 0.00 & 488.93 & 464.01 & 463.86 & 462.63 & 463.62 \\\\\n", "1 & 468.01 & \\r{457.54} & 459.35 & 458.66 & 459.15 & 461.01 \\\\\n", "2 & 469.68 & nan & 460.48 & 459.43 & 459.23 & 460.47 \\\\\n", "3 & 467.10 & 458.44 & 459.64 & 456.66 & 459.54 & 460.05 \\\\\n", "4 & 469.00 & 459.52 & nan & 463.04 & 459.35 & 460.96 \\\\\n", "5 & 471.32 & 461.26 & nan & nan & 461.00 & 462.97 \\\\\n", "\\hline\n", "\\end{array}\n", "$$\n", "\n", "**Note**: the AIC values are calculated differently than in Durbin and Koopman, but show overall similar trends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Postestimation\n", "\n", "Using the ARMA(1,1) specification selected above, we perform in-sample prediction and out-of-sample forecasting." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'dta_miss' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-dd0a0a728f6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Statespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta_miss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'dta_miss' is not defined" ] } ], "source": [ "# Statespace\n", "mod = sm.tsa.statespace.SARIMAX(dta_miss, order=(1,0,1))\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-c5a0278f6f27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# In-sample one-step-ahead predictions, and out-of-sample forecasts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mnforecast\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnobs\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnforecast\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpredict_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'res' is not defined" ] } ], "source": [ "# In-sample one-step-ahead predictions, and out-of-sample forecasts\n", "nforecast = 20\n", "predict = res.get_prediction(end=mod.nobs + nforecast)\n", "idx = np.arange(len(predict.predicted_mean))\n", "predict_ci = predict.conf_int(alpha=0.5)\n", "\n", "# Graph\n", "fig, ax = plt.subplots(figsize=(12,6))\n", "ax.xaxis.grid()\n", "ax.plot(dta_miss, 'k.')\n", "\n", "# Plot\n", "ax.plot(idx[:-nforecast], predict.predicted_mean[:-nforecast], 'gray')\n", "ax.plot(idx[-nforecast:], predict.predicted_mean[-nforecast:], 'k--', linestyle='--', linewidth=2)\n", "ax.fill_between(idx, predict_ci.iloc[:, 0], predict_ci.iloc[:, 1], alpha=0.15)\n", "\n", "ax.set(title='Figure 8.9 - Internet series');" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SARIMAX: Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook replicates examples from the Stata ARIMA time series estimation and postestimation documentation.\n", "\n", "First, we replicate the four estimation examples http://www.stata.com/manuals13/tsarima.pdf:\n", "\n", "1. ARIMA(1,1,1) model on the U.S. Wholesale Price Index (WPI) dataset.\n", "2. Variation of example 1 which adds an MA(4) term to the ARIMA(1,1,1) specification to allow for an additive seasonal effect.\n", "3. ARIMA(2,1,0) x (1,1,0,12) model of monthly airline data. This example allows a multiplicative seasonal effect.\n", "4. ARMA(1,1) model with exogenous regressors; describes consumption as an autoregressive process on which also the money supply is assumed to be an explanatory variable.\n", "\n", "Second, we demonstrate postestimation capabilitites to replicate http://www.stata.com/manuals13/tsarimapostestimation.pdf. The model from example 4 is used to demonstrate:\n", "\n", "1. One-step-ahead in-sample prediction\n", "2. n-step-ahead out-of-sample forecasting\n", "3. n-step-ahead in-sample dynamic prediction" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.stats import norm\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime\n", "import requests\n", "from io import BytesIO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ARIMA Example 1: Arima\n", "\n", "As can be seen in the graphs from Example 2, the Wholesale price index (WPI) is growing over time (i.e. is not stationary). Therefore an ARMA model is not a good specification. In this first example, we consider a model where the original time series is assumed to be integrated of order 1, so that the difference is assumed to be stationary, and fit a model with one autoregressive lag and one moving average lag, as well as an intercept term.\n", "\n", "The postulated data process is then:\n", "\n", "$$\n", "\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n", "$$\n", "\n", "where $c$ is the intercept of the ARMA model, $\\Delta$ is the first-difference operator, and we assume $\\epsilon_{t} \\sim N(0, \\sigma^2)$. This can be rewritten to emphasize lag polynomials as (this will be useful in example 2, below):\n", "\n", "$$\n", "(1 - \\phi_1 L ) \\Delta y_t = c + (1 + \\theta_1 L) \\epsilon_{t}\n", "$$\n", "\n", "where $L$ is the lag operator.\n", "\n", "Notice that one difference between the Stata output and the output below is that Stata estimates the following model:\n", "\n", "$$\n", "(\\Delta y_t - \\beta_0) = \\phi_1 ( \\Delta y_{t-1} - \\beta_0) + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n", "$$\n", "\n", "where $\\beta_0$ is the mean of the process $y_t$. This model is equivalent to the one estimated in the Statsmodels SARIMAX class, but the interpretation is different. To see the equivalence, note that:\n", "\n", "$$\n", "(\\Delta y_t - \\beta_0) = \\phi_1 ( \\Delta y_{t-1} - \\beta_0) + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t} \\\\\n", "\\Delta y_t = (1 - \\phi_1) \\beta_0 + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n", "$$\n", "\n", "so that $c = (1 - \\phi_1) \\beta_0$." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/wpi1.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac804a50>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mNewConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m chunked=chunked)\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m 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967\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 167\u001b[0m raise NewConnectionError(\n\u001b[0;32m--> 168\u001b[0;31m self, \"Failed to establish a new connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xac804a50>: Failed to establish a new connection: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m 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2\u001b[0;31m \u001b[0mwpi1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.stata-press.com/data/r12/wpi1.dta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwpi1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 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Dataset\n", "wpi1 = requests.get('http://www.stata-press.com/data/r12/wpi1.dta').content\n", "data = pd.read_stata(BytesIO(wpi1))\n", "data.index = data.t\n", "\n", "# Fit the model\n", "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus the maximum likelihood estimates imply that for the process above, we have:\n", "\n", "$$\n", "\\Delta y_t = 0.1050 + 0.8740 \\Delta y_{t-1} - 0.4206 \\epsilon_{t-1} + \\epsilon_{t}\n", "$$\n", "\n", "where $\\epsilon_{t} \\sim N(0, 0.5226)$. Finally, recall that $c = (1 - \\phi_1) \\beta_0$, and here $c = 0.1050$ and $\\phi_1 = 0.8740$. To compare with the output from Stata, we could calculate the mean:\n", "\n", "$$\\beta_0 = \\frac{c}{1 - \\phi_1} = \\frac{0.1050}{1 - 0.8740} = 0.83$$\n", "\n", "**Note**: these values are slightly different from the values in the Stata documentation because the optimizer in Statsmodels has found parameters here that yield a higher likelihood. Nonetheless, they are very close." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ARIMA Example 2: Arima with additive seasonal effects\n", "\n", "This model is an extension of that from example 1. Here the data is assumed to follow the process:\n", "\n", "$$\n", "\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t}\n", "$$\n", "\n", "The new part of this model is that there is allowed to be a annual seasonal effect (it is annual even though the periodicity is 4 because the dataset is quarterly). The second difference is that this model uses the log of the data rather than the level.\n", "\n", "Before estimating the dataset, graphs showing:\n", "\n", "1. The time series (in logs)\n", "2. The first difference of the time series (in logs)\n", "3. The autocorrelation function\n", "4. The partial autocorrelation function.\n", "\n", "From the first two graphs, we note that the original time series does not appear to be stationary, whereas the first-difference does. This supports either estimating an ARMA model on the first-difference of the data, or estimating an ARIMA model with 1 order of integration (recall that we are taking the latter approach). The last two graphs support the use of an ARMA(1,1,1) model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'wpi1' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-4c25c1c8c40d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m<ipython-input-5-3bd451372e07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Levels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mpl_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m 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"text/plain": [ "<Figure size 1080x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph data\n", "fig, axes = plt.subplots(1, 2, figsize=(15,4))\n", "\n", "# Levels\n", "axes[0].plot(data.index._mpl_repr(), data['wpi'], '-')\n", "axes[0].set(title='US Wholesale Price Index')\n", "\n", "# Log difference\n", "axes[1].plot(data.index._mpl_repr(), data['D.ln_wpi'], '-')\n", "axes[1].hlines(0, data.index[0], data.index[-1], 'r')\n", "axes[1].set(title='US Wholesale Price Index - difference of logs');" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-3a0723368be2>\u001b[0m in 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"text/plain": [ "<Figure size 1080x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph data\n", "fig, axes = plt.subplots(1, 2, figsize=(15,4))\n", "\n", "fig = sm.graphics.tsa.plot_acf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[0])\n", "fig = sm.graphics.tsa.plot_pacf(data.ix[1:, 'D.ln_wpi'], lags=40, ax=axes[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand how to specify this model in Statsmodels, first recall that from example 1 we used the following code to specify the ARIMA(1,1,1) model:\n", "\n", "```python\n", "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n", "```\n", "\n", "The `order` argument is a tuple of the form `(AR specification, Integration order, MA specification)`. The integration order must be an integer (for example, here we assumed one order of integration, so it was specified as 1. In a pure ARMA model where the underlying data is already stationary, it would be 0).\n", "\n", "For the AR specification and MA specification components, there are two possiblities. The first is to specify the **maximum degree** of the corresponding lag polynomial, in which case the component is an integer. For example, if we wanted to specify an ARIMA(1,1,4) process, we would use:\n", "\n", "```python\n", "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,4))\n", "```\n", "\n", "and the corresponding data process would be:\n", "\n", "$$\n", "y_t = c + \\phi_1 y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_2 \\epsilon_{t-2} + \\theta_3 \\epsilon_{t-3} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t}\n", "$$\n", "\n", "or\n", "\n", "$$\n", "(1 - \\phi_1 L)\\Delta y_t = c + (1 + \\theta_1 L + \\theta_2 L^2 + \\theta_3 L^3 + \\theta_4 L^4) \\epsilon_{t}\n", "$$\n", "\n", "When the specification parameter is given as a maximum degree of the lag polynomial, it implies that all polynomial terms up to that degree are included. Notice that this is *not* the model we want to use, because it would include terms for $\\epsilon_{t-2}$ and $\\epsilon_{t-3}$, which we don't want here.\n", "\n", "What we want is a polynomial that has terms for the 1st and 4th degrees, but leaves out the 2nd and 3rd terms. To do that, we need to provide a tuple for the specifiation parameter, where the tuple describes **the lag polynomial itself**. In particular, here we would want to use:\n", "\n", "```python\n", "ar = 1 # this is the maximum degree specification\n", "ma = (1,0,0,1) # this is the lag polynomial specification\n", "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(ar,1,ma)))\n", "```\n", "\n", "This gives the following form for the process of the data:\n", "\n", "$$\n", "\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t} \\\\\n", "(1 - \\phi_1 L)\\Delta y_t = c + (1 + \\theta_1 L + \\theta_4 L^4) \\epsilon_{t}\n", "$$\n", "\n", "which is what we want." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-2f5408f59767>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Fit the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ln_wpi'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" ] } ], "source": [ "# Fit the model\n", "mod = sm.tsa.statespace.SARIMAX(data['ln_wpi'], trend='c', order=(1,1,1))\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ARIMA Example 3: Airline Model\n", "\n", "In the previous example, we included a seasonal effect in an *additive* way, meaning that we added a term allowing the process to depend on the 4th MA lag. It may be instead that we want to model a seasonal effect in a multiplicative way. We often write the model then as an ARIMA $(p,d,q) \\times (P,D,Q)_s$, where the lowercast letters indicate the specification for the non-seasonal component, and the uppercase letters indicate the specification for the seasonal component; $s$ is the periodicity of the seasons (e.g. it is often 4 for quarterly data or 12 for monthly data). The data process can be written generically as:\n", "\n", "$$\n", "\\phi_p (L) \\tilde \\phi_P (L^s) \\Delta^d \\Delta_s^D y_t = A(t) + \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n", "$$\n", "\n", "where:\n", "\n", "- $\\phi_p (L)$ is the non-seasonal autoregressive lag polynomial\n", "- $\\tilde \\phi_P (L^s)$ is the seasonal autoregressive lag polynomial\n", "- $\\Delta^d \\Delta_s^D y_t$ is the time series, differenced $d$ times, and seasonally differenced $D$ times.\n", "- $A(t)$ is the trend polynomial (including the intercept)\n", "- $\\theta_q (L)$ is the non-seasonal moving average lag polynomial\n", "- $\\tilde \\theta_Q (L^s)$ is the seasonal moving average lag polynomial\n", "\n", "sometimes we rewrite this as:\n", "\n", "$$\n", "\\phi_p (L) \\tilde \\phi_P (L^s) y_t^* = A(t) + \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n", "$$\n", "\n", "where $y_t^* = \\Delta^d \\Delta_s^D y_t$. This emphasizes that just as in the simple case, after we take differences (here both non-seasonal and seasonal) to make the data stationary, the resulting model is just an ARMA model.\n", "\n", "As an example, consider the airline model ARIMA $(2,1,0) \\times (1,1,0)_{12}$, with an intercept. The data process can be written in the form above as:\n", "\n", "$$\n", "(1 - \\phi_1 L - \\phi_2 L^2) (1 - \\tilde \\phi_1 L^{12}) \\Delta \\Delta_{12} y_t = c + \\epsilon_t\n", "$$\n", "\n", "Here, we have:\n", "\n", "- $\\phi_p (L) = (1 - \\phi_1 L - \\phi_2 L^2)$\n", "- $\\tilde \\phi_P (L^s) = (1 - \\phi_1 L^12)$\n", "- $d = 1, D = 1, s=12$ indicating that $y_t^*$ is derived from $y_t$ by taking first-differences and then taking 12-th differences.\n", "- $A(t) = c$ is the *constant* trend polynomial (i.e. just an intercept)\n", "- $\\theta_q (L) = \\tilde \\theta_Q (L^s) = 1$ (i.e. there is no moving average effect)\n", "\n", "It may still be confusing to see the two lag polynomials in front of the time-series variable, but notice that we can multiply the lag polynomials together to get the following model:\n", "\n", "$$\n", "(1 - \\phi_1 L - \\phi_2 L^2 - \\tilde \\phi_1 L^{12} + \\phi_1 \\tilde \\phi_1 L^{13} + \\phi_2 \\tilde \\phi_1 L^{14} ) y_t^* = c + \\epsilon_t\n", "$$\n", "\n", "which can be rewritten as:\n", "\n", "$$\n", "y_t^* = c + \\phi_1 y_{t-1}^* + \\phi_2 y_{t-2}^* + \\tilde \\phi_1 y_{t-12}^* - \\phi_1 \\tilde \\phi_1 y_{t-13}^* - \\phi_2 \\tilde \\phi_1 y_{t-14}^* + \\epsilon_t\n", "$$\n", "\n", "This is similar to the additively seasonal model from example 2, but the coefficients in front of the autoregressive lags are actually combinations of the underlying seasonal and non-seasonal parameters.\n", "\n", "Specifying the model in Statsmodels is done simply by adding the `seasonal_order` argument, which accepts a tuple of the form `(Seasonal AR specification, Seasonal Integration order, Seasonal MA, Seasonal periodicity)`. The seasonal AR and MA specifications, as before, can be expressed as a maximum polynomial degree or as the lag polynomial itself. Seasonal periodicity is an integer.\n", "\n", "For the airline model ARIMA $(2,1,0) \\times (1,1,0)_{12}$ with an intercept, the command is:\n", "\n", "```python\n", "mod = sm.tsa.statespace.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12))\n", "```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/air2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac6a57b0>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", 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HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/air2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac6a57b0>: Failed to establish a new connection: [Errno 111] Connection refused')))" ] } ], "source": [ "# Dataset\n", "air2 = requests.get('http://www.stata-press.com/data/r12/air2.dta').content\n", "data = pd.read_stata(BytesIO(air2))\n", "data.index = pd.date_range(start=datetime(data.time[0], 1, 1), periods=len(data), freq='MS')\n", "data['lnair'] = np.log(data['air'])\n", "\n", "# Fit the model\n", "mod = sm.tsa.statespace.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12), simple_differencing=True)\n", "res = mod.fit()\n", "print(res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that here we used an additional argument `simple_differencing=True`. This controls how the order of integration is handled in ARIMA models. If `simple_differencing=True`, then the time series provided as `endog` is literatlly differenced and an ARMA model is fit to the resulting new time series. This implies that a number of initial periods are lost to the differencing process, however it may be necessary either to compare results to other packages (e.g. Stata's `arima` always uses simple differencing) or if the seasonal periodicity is large.\n", "\n", "The default is `simple_differencing=False`, in which case the integration component is implemented as part of the state space formulation, and all of the original data can be used in estimation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ARIMA Example 4: ARMAX (Friedman)\n", "\n", "This model demonstrates the use of explanatory variables (the X part of ARMAX). When exogenous regressors are included, the SARIMAX module uses the concept of \"regression with SARIMA errors\" (see http://robjhyndman.com/hyndsight/arimax/ for details of regression with ARIMA errors versus alternative specifications), so that the model is specified as:\n", "\n", "$$\n", "y_t = \\beta_t x_t + u_t \\\\\n", " \\phi_p (L) \\tilde \\phi_P (L^s) \\Delta^d \\Delta_s^D u_t = A(t) +\n", " \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n", "$$\n", "\n", "Notice that the first equation is just a linear regression, and the second equation just describes the process followed by the error component as SARIMA (as was described in example 3). One reason for this specification is that the estimated parameters have their natural interpretations.\n", "\n", "This specification nests many simpler specifications. For example, regression with AR(2) errors is:\n", "\n", "$$\n", "y_t = \\beta_t x_t + u_t \\\\\n", "(1 - \\phi_1 L - \\phi_2 L^2) u_t = A(t) + \\epsilon_t\n", "$$\n", "\n", "The model considered in this example is regression with ARMA(1,1) errors. The process is then written:\n", "\n", "$$\n", "\\text{consump}_t = \\beta_0 + \\beta_1 \\text{m2}_t + u_t \\\\\n", "(1 - \\phi_1 L) u_t = (1 - \\theta_1 L) \\epsilon_t\n", "$$\n", "\n", "Notice that $\\beta_0$ is, as described in example 1 above, *not* the same thing as an intercept specified by `trend='c'`. Whereas in the examples above we estimated the intercept of the model via the trend polynomial, here, we demonstrate how to estimate $\\beta_0$ itself by adding a constant to the exogenous dataset. In the output, the $beta_0$ is called `const`, whereas above the intercept $c$ was called `intercept` in the output." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ProxyError", "evalue": "HTTPConnectionPool(host='127.0.0.1', port=9): Max retries exceeded with url: http://www.stata-press.com/data/r12/friedman2.dta (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0xac64b9d0>: Failed to establish a new connection: [Errno 111] Connection refused')))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 159\u001b[0;31m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 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connection: %s\" % e)\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNewConnectionError\u001b[0m: <urllib3.connection.HTTPConnection object at 0xac64b9d0>: Failed to establish a new connection: [Errno 111] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m )\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 638\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 397\u001b[0m \u001b[0;32mif\u001b[0m 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establish a new connection: [Errno 111] Connection refused')))", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mProxyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-9-1caba5d05731>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfriedman2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.stata-press.com/data/r12/friedman2.dta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 531\u001b[0m }\n\u001b[1;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python3/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 508\u001b[0m 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"source": [ "### ARIMA Postestimation: Example 1 - Dynamic Forecasting\n", "\n", "Here we describe some of the post-estimation capabilities of Statsmodels' SARIMAX.\n", "\n", "First, using the model from example, we estimate the parameters using data that *excludes the last few observations* (this is a little artificial as an example, but it allows considering performance of out-of-sample forecasting and facilitates comparison to Stata's documentation)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'friedman2' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-10-2387f4ef1496>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mraw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_stata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfriedman2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'1981'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'friedman2' is not defined" ] } ], "source": [ "# Dataset\n", "raw = pd.read_stata(BytesIO(friedman2))\n", "raw.index = raw.time\n", "data = raw.ix[:'1981']\n", "\n", "# Variables\n", "endog = data.ix['1959':, 'consump']\n", "exog = sm.add_constant(data.ix['1959':, 'm2'])\n", "nobs = endog.shape[0]\n", "\n", "# Fit the model\n", "mod = sm.tsa.statespace.SARIMAX(endog.ix[:'1978-01-01'], exog=exog.ix[:'1978-01-01'], order=(1,0,1))\n", "fit_res = mod.fit()\n", "print(fit_res.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we want to get results for the full dataset but using the estimated parameters (on a subset of the data)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'endog' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-9b773553794d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatespace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSARIMAX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'endog' is not defined" ] } ], "source": [ "mod = sm.tsa.statespace.SARIMAX(endog, exog=exog, order=(1,0,1))\n", "res = mod.filter(fit_res.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `predict` command is first applied here to get in-sample predictions. We use the `full_results=True` argument to allow us to calculate confidence intervals (the default output of `predict` is just the predicted values).\n", "\n", "With no other arguments, `predict` returns the one-step-ahead in-sample predictions for the entire sample." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-12-cff6581c7519>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# In-sample one-step-ahead predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mpredict_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'res' is not defined" ] } ], "source": [ "# In-sample one-step-ahead predictions\n", "predict = res.get_prediction()\n", "predict_ci = predict.conf_int()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also get *dynamic predictions*. One-step-ahead prediction uses the true values of the endogenous values at each step to predict the next in-sample value. Dynamic predictions use one-step-ahead prediction up to some point in the dataset (specified by the `dynamic` argument); after that, the previous *predicted* endogenous values are used in place of the true endogenous values for each new predicted element.\n", "\n", "The `dynamic` argument is specified to be an *offset* relative to the `start` argument. If `start` is not specified, it is assumed to be `0`.\n", "\n", "Here we perform dynamic prediction starting in the first quarter of 1978." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'res' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-13-24c8c7a5e61f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Dynamic predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredict_dy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_prediction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdynamic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'1978-01-01'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mpredict_dy_ci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict_dy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'res' is not defined" ] } ], "source": [ "# Dynamic predictions\n", "predict_dy = res.get_prediction(dynamic='1978-01-01')\n", "predict_dy_ci = predict_dy.conf_int()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can graph the one-step-ahead and dynamic predictions (and the corresponding confidence intervals) to see their relative performance. Notice that up to the point where dynamic prediction begins (1978:Q1), the two are the same." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-14-e8669824a91c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Plot data points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'1977-07-01'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'consump'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Observed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Plot predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" ] }, { "data": { "image/png": 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0g64PgLwOWNFzLZIkSZ357CRJkjRIhhhJkjRIXeaJeWSSTdv3+yR5bZKtZtpPkiSpT13uxPwzcHeSnYFPADsBn+21KkmSpBl0CTH3VNVdwAuBD1fV64CH9VuWJEnSeF1CzJ1JDgFeDkw+M2nj/kqSJEmaWZcQczjwh8BfVtWPk+wEfLrfsiRJksbrMtnd5cBrR5Z/DLyvz6IkSZJmMmOISfJU4Bhgh7Z9gKqqR/RbmiRJ0vS6zNj7CeB1wEXA3f2WI0mS1E2XEHNzVX2l90okSZLWQZcQc06SDwBfAG6fXFlV3+qtKkmSpBl0CTFPbn9OjKwr4Jkz7ZhkX+BvgA2Bf6iq907T7kXAacCTqmplh5okSdIi1+XbSc9YnwMn2RA4AXgOsBq4MMmK9ttOo+22AI4GLlif80iSpMWpy7OTliQ5LsnK9vXBJEs6HHsvYFVVXVFVdwCnAAeupd2xNF/Zvm2dKpckSYtal8nuTgR+CRzUvm4B/rHDftsCV48sr27X/VaSPYHtqurL4w6U5MjJELVmzZoOp5YkSQtdlzExj6yqF40svzPJJff1xEk2AI4DDpupbVUtB5YDTExM1H09tyRJGr4ud2JuTfJfJhfaye9u7bDfNcB2I8vL2nWTtgAeB5yb5Epgb2BFktEBxJIkSWvV5U7Mq4BPtuNgAvycDndPgAuBXdpnLV0DHAy8ZHJjVd0MLJ1cTnIu8Ea/nSRJkrro8u2kS4Ddk2zZLt/S5cBVdVeSo4Azab5ifWJVXZbkXcDKqlpxH+qWJEmL3LQhJsmhVfWZJK+fsh6AqjpupoNX1RnAGVPWvWOatvt0qFeSJAkYfydms/bnFrNRiCRJ0rqYNsRU1cfbn++cvXIkSZK6GdeddPy4Havqtfd/OZIkSd2M6066aNaqkCRJWkfjupM+OZuFSJIkrYtx3UlfpHla9VpV1QG9VCRJktTBuO6kv561KiRJktbRuO6kr81mIZIkSetiXHfSqVV1UJLvsJZupap6fK+VSZIkjTGuO+no9ufzZ6MQSZKkdTGuO+m69udVk+uSLAVurKppB/xKkiTNhg2m25Bk7yTnJvlCkj2SfBf4LvDTJPvOXomSJEm/a1x30keAtwJLgK8C+1XV+UkeDZwM/Oss1CdJkrRW096JATaqqrOq6vPA9VV1PkBVfX92SpMkSZreuBBzz8j7W6dsc0yMJEmaU+O6k3ZPcgsQ4Pfa97TLD+i9MkmSpDHGfTtpw9ksRJIkaV2M606SJEmatwwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkAwxkiRpkHoNMUn2TfKDJKuSvGUt21+f5PIklyY5O8kOfdYjSZIWjt5CTJINgROA/YDdgEOS7Dal2cXARFU9HjgNeH9f9UiSpIWlzzsxewGrquqKqroDOAU4cLRBVZ1TVb9pF88HlvVYjyRJWkD6DDHbAlePLK9u103nCOAra9uQ5MgkK5OsXLNmzf1YoiRJGqp5MbA3yaHABPCBtW2vquVVNVFVE1tvvfXsFidJkualjXo89jXAdiPLy9p195Lk2cDbgKdX1e091iNJkhaQPu/EXAjskmSnJJsABwMrRhsk2QP4OHBAVd3QYy2SJGmB6S3EVNVdwFHAmcD3gFOr6rIk70pyQNvsA8DmwOeTXJJkxTSHkyRJupc+u5OoqjOAM6ase8fI+2f3eX5JkrRwzYuBvZIkSevKECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgbJECNJkgap1xCTZN8kP0iyKslb1rJ90ySfa7dfkGTHPuuRJEkLR28hJsmGwAnAfsBuwCFJdpvS7AjgpqraGfgQ8L6+6pEkSQtLn3di9gJWVdUVVXUHcApw4JQ2BwKfbN+fBjwrSXqsSZIkLRAb9XjsbYGrR5ZXA0+erk1V3ZXkZuAhwM9GGyU5EjiyXbw9yXd7qVjraylTrpnmlNdjfvF6zD9ek/nlUeu7Y58h5n5TVcuB5QBJVlbVxByXpBFek/nF6zG/eD3mH6/J/JJk5fru22d30jXAdiPLy9p1a22TZCNgCXBjjzVJkqQFos8QcyGwS5KdkmwCHAysmNJmBfDy9v2fAF+tquqxJkmStED01p3UjnE5CjgT2BA4saouS/IuYGVVrQA+AXw6ySrg5zRBZybL+6pZ681rMr94PeYXr8f84zWZX9b7esQbH5IkaYicsVeSJA2SIUaSJA3SvA0xPrJgfulwPV6f5PIklyY5O8kOc1HnYjLTNRlp96IklcSvlPaoy/VIclD77+SyJJ+d7RoXmw7/3do+yTlJLm7/27X/XNS5GCQ5MckN083zlsbx7bW6NMmenQ5cVfPuRTMQ+EfAI4BNgG8Du01p82rgY+37g4HPzXXdC/XV8Xo8A3hg+/5VXo+5vyZtuy2A84DzgYm5rnuhvjr+G9kFuBh4ULu8zVzXvZBfHa/JcuBV7fvdgCvnuu6F+gKeBuwJfHea7fsDXwEC7A1c0OW48/VOjI8smF9mvB5VdU5V/aZdPJ9mXiD1p8u/EYBjaZ5JdttsFrcIdbkerwROqKqbAKrqhlmucbHpck0K2LJ9vwS4dhbrW1Sq6jyabyFP50DgU9U4H9gqycNmOu58DTFre2TBttO1qaq7gMlHFuj+1+V6jDqCJlGrPzNek/Z27HZV9eXZLGyR6vJvZFdg1yTfSHJ+kn1nrbrFqcs1OQY4NMlq4AzgNbNTmtZiXf/OAAN57ICGI8mhwATw9LmuZTFLsgFwHHDYHJei/7QRTZfSPjR3Ks9L8gdV9Ys5rWpxOwQ4qao+mOQPaeYte1xV3TPXhamb+XonxkcWzC9drgdJng28DTigqm6fpdoWq5muyRbA44Bzk1xJ08e8wsG9venyb2Q1sKKq7qyqHwM/pAk16keXa3IEcCpAVX0TeADNwyE1+zr9nZlqvoYYH1kwv8x4PZLsAXycJsDY19+/sdekqm6uqqVVtWNV7UgzTumAqlrvB61prC7/zTqd5i4MSZbSdC9dMZtFLjJdrslPgGcBJHkMTYhZM6tVatIK4GXtt5T2Bm6uqutm2mledidVf48s0HroeD0+AGwOfL4dX/2Tqjpgzope4DpeE82SjtfjTOC5SS4H7gbeVFXePe5Jx2vyBuDvk7yOZpDvYf7PcD+SnEwT4pe2Y5D+AtgYoKo+RjMmaX9gFfAb4PBOx/V6SZKkIZqv3UmSJEljGWIkSdIgGWIkSdIgGWIkSdIgGWIkSdIgGWIkzZokdye5pH2K87eTvKGdXXjcPjsmecls1ShpOAwxkmbTrVX1hKp6LPAcYD+a+SLG2REwxEj6Hc4TI2nWJPlVVW0+svwImplVlwI7AJ8GNms3H1VV/5bkfOAxwI9pnlx/PPBemomzNqV5MvTHZ+1DSJo3DDGSZs3UENOu+wXwKOCXwD1VdVuSXYCTq2oiyT7AG6vq+W37I4FtqurdSTYFvgG8uH0ekaRFZF4+dkDSorQx8JEkT6CZln/Xado9F3h8kj9pl5fQPEjRECMtMoYYSXOm7U66G7iBZmzMT4Hdacbr3TbdbsBrqurMWSlS0rzlwF5JcyLJ1sDHgI+0D91bAlxXVfcAf0bz0D5oupm2GNn1TOBVSTZuj7Nrks2QtOh4J0bSbPq9JJfQdB3dRTOQ97h220eBf07yYuAc4Nft+kuBu5N8GzgJ+Buabyx9K80j09cAfzRbH0DS/OHAXkmSNEh2J0mSpEEyxEiSpEEyxEiSpEEyxEiSpEEyxEiSpEEyxEiSpEEyxEiSpEH6/8JWV1qoTlB4AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 648x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graph\n", "fig, ax = plt.subplots(figsize=(9,4))\n", "npre = 4\n", "ax.set(title='Personal consumption', xlabel='Date', ylabel='Billions of dollars')\n", "\n", "# Plot data points\n", "data.ix['1977-07-01':, 'consump'].plot(ax=ax, style='o', label='Observed')\n", "\n", "# Plot predictions\n", "predict.predicted_mean.ix['1977-07-01':].plot(ax=ax, style='r--', label='One-step-ahead forecast')\n", "ci = predict_ci.ix['1977-07-01':]\n", "ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], color='r', alpha=0.1)\n", "predict_dy.predicted_mean.ix['1977-07-01':].plot(ax=ax, style='g', label='Dynamic forecast (1978)')\n", "ci = predict_dy_ci.ix['1977-07-01':]\n", "ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], color='g', alpha=0.1)\n", "\n", "legend = ax.legend(loc='lower right')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, graph the prediction *error*. It is obvious that, as one would suspect, one-step-ahead prediction is considerably better." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'predict' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-aa5ccb6e7d4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# In-sample one-step-ahead predictions and 95% confidence intervals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mpredict_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredicted_mean\u001b[0m \u001b[0;34m-\u001b[0m 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"\u001b[0;31mNameError\u001b[0m: name 'predict' is not defined" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Prediction error\n", "\n", "# Graph\n", "fig, ax = plt.subplots(figsize=(9,4))\n", "npre = 4\n", "ax.set(title='Forecast error', xlabel='Date', ylabel='Forecast - Actual')\n", "\n", "# In-sample one-step-ahead predictions and 95% confidence intervals\n", "predict_error = predict.predicted_mean - endog\n", "predict_error.ix['1977-10-01':].plot(ax=ax, label='One-step-ahead forecast')\n", "ci = predict_ci.ix['1977-10-01':].copy()\n", "ci.iloc[:,0] -= endog.loc['1977-10-01':]\n", "ci.iloc[:,1] -= endog.loc['1977-10-01':]\n", "ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], alpha=0.1)\n", "\n", "# Dynamic predictions and 95% confidence intervals\n", "predict_dy_error = predict_dy.predicted_mean - endog\n", "predict_dy_error.ix['1977-10-01':].plot(ax=ax, style='r', label='Dynamic forecast (1978)')\n", "ci = predict_dy_ci.ix['1977-10-01':].copy()\n", "ci.iloc[:,0] -= endog.loc['1977-10-01':]\n", "ci.iloc[:,1] -= endog.loc['1977-10-01':]\n", "ax.fill_between(ci.index, ci.ix[:,0], ci.ix[:,1], color='r', alpha=0.1)\n", "\n", "legend = ax.legend(loc='lower left');\n", "legend.get_frame().set_facecolor('w')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmpujr2q7j1/93216ce8-b2cc-452d-ab83-c1cf4bd7239b vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoregressive Moving Average (ARMA): Sunspots data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "from scipy import stats\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.graphics.api import qqplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sunpots Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 309 (Annual 1700 - 2008)\n", " Number of Variables - 1\n", " Variable name definitions::\n", "\n", " SUNACTIVITY - Number of sunspots for each year\n", "\n", " The data file contains a 'YEAR' variable that is not returned by load.\n", "\n" ] } ], "source": [ "print(sm.datasets.sunspots.NOTE)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta = sm.datasets.sunspots.load_pandas().data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008'))\n", "del dta[\"YEAR\"]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dta.plot(figsize=(12,8));" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " out_full[ind] += zi\n", "/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " out = out_full[ind]\n", "/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " zf = out_full[ind]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "const 49.659366\n", "ar.L1.SUNACTIVITY 1.390656\n", "ar.L2.SUNACTIVITY -0.688571\n", "dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " elif issubdtype(paramsdtype, complex):\n" ] } ], "source": [ "arma_mod20 = sm.tsa.ARMA(dta, (2,0)).fit()\n", "print(arma_mod20.params)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " out_full[ind] += zi\n", "/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " out = out_full[ind]\n", "/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " zf = out_full[ind]\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " elif issubdtype(paramsdtype, complex):\n" ] } ], "source": [ "arma_mod30 = sm.tsa.ARMA(dta, (3,0)).fit()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2622.636338063792 2637.569703171383 2628.6067259090382\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] } ], "source": [ "print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "const 49.749796\n", "ar.L1.SUNACTIVITY 1.300810\n", "ar.L2.SUNACTIVITY -0.508093\n", "ar.L3.SUNACTIVITY -0.129650\n", "dtype: float64\n" ] } ], "source": [ "print(arma_mod30.params)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2619.403628699364 2638.070335083853 2626.8666135059216\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] } ], "source": [ "print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Does our model obey the theory?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "data": { "text/plain": [ "1.9564806905406757" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sm.stats.durbin_watson(arma_mod30.resid.values)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax = arma_mod30.resid.plot(ax=ax);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "resid = arma_mod30.resid" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "NormaltestResult(statistic=49.845037117754686, pvalue=1.500678687416067e-11)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.normaltest(resid)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "fig = qqplot(resid, line='q', ax=ax, fit=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AC Q Prob(>Q)\n", "lag \n", "1.0 0.009179 0.026287 8.712008e-01\n", "2.0 0.041793 0.573048 7.508690e-01\n", "3.0 -0.001334 0.573607 9.024469e-01\n", "4.0 0.136089 6.408955 1.706181e-01\n", "5.0 0.092469 9.111881 1.046840e-01\n", "6.0 0.091949 11.793308 6.674192e-02\n", "7.0 0.068748 13.297273 6.518822e-02\n", "8.0 -0.015020 13.369300 9.975911e-02\n", "9.0 0.187592 24.641988 3.393810e-03\n", "10.0 0.213718 39.322079 2.229398e-05\n", "11.0 0.201082 52.361229 2.344860e-07\n", "12.0 0.117182 56.804288 8.573905e-08\n", "13.0 -0.014055 56.868423 1.893827e-07\n", "14.0 0.015398 56.945664 3.997499e-07\n", "15.0 -0.024967 57.149416 7.741175e-07\n", "16.0 0.080916 59.296873 6.871888e-07\n", "17.0 0.041138 59.853844 1.110899e-06\n", "18.0 -0.052021 60.747533 1.548371e-06\n", "19.0 0.062496 62.041796 1.831572e-06\n", "20.0 -0.010301 62.077084 3.381114e-06\n", "21.0 0.074453 63.926758 3.193467e-06\n", "22.0 0.124955 69.154873 8.978021e-07\n", "23.0 0.093162 72.071136 5.799571e-07\n", "24.0 -0.082152 74.346787 4.712847e-07\n", "25.0 0.015695 74.430143 8.288748e-07\n", "26.0 -0.025037 74.643000 1.367237e-06\n", "27.0 -0.125861 80.041239 3.722446e-07\n", "28.0 0.053225 81.010073 4.716129e-07\n", "29.0 -0.038693 81.523899 6.916415e-07\n", "30.0 -0.016904 81.622318 1.151625e-06\n", "31.0 -0.019296 81.751031 1.868708e-06\n", "32.0 0.104990 85.575151 8.927700e-07\n", "33.0 0.040086 86.134651 1.247474e-06\n", "34.0 0.008829 86.161894 2.047769e-06\n", "35.0 0.014588 86.236531 3.263719e-06\n", "36.0 -0.119329 91.248977 1.084426e-06\n", "37.0 -0.036665 91.723945 1.521884e-06\n", "38.0 -0.046193 92.480592 1.938687e-06\n", "39.0 -0.017768 92.592961 2.990607e-06\n", "40.0 -0.006220 92.606784 4.696872e-06\n" ] } ], "source": [ "r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n", "data = np.c_[range(1,41), r[1:], q, p]\n", "table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n", "print(table.set_index('lag'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* This indicates a lack of fit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* In-sample dynamic prediction. How good does our model do?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1990-12-31 167.047353\n", "1991-12-31 140.992848\n", "1992-12-31 94.858876\n", "1993-12-31 46.860614\n", "1994-12-31 11.242300\n", "1995-12-31 -4.721532\n", "1996-12-31 -1.167078\n", "1997-12-31 16.185598\n", "1998-12-31 39.021839\n", "1999-12-31 59.449843\n", "2000-12-31 72.170093\n", "2001-12-31 75.376689\n", "2002-12-31 70.436311\n", "2003-12-31 60.731396\n", "2004-12-31 50.201584\n", "2005-12-31 42.075817\n", "2006-12-31 38.114099\n", "2007-12-31 38.454487\n", "2008-12-31 41.963690\n", "2009-12-31 46.869181\n", "2010-12-31 51.423160\n", "2011-12-31 54.399610\n", "2012-12-31 55.321566\n", "Freq: A-DEC, dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] } ], "source": [ "predict_sunspots = arma_mod30.predict('1990', '2012', dynamic=True)\n", "print(predict_sunspots)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: \n", ".ix is deprecated. Please use\n", ".loc for label based indexing or\n", ".iloc for positional indexing\n", "\n", "See the documentation here:\n", "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "ax = dta.ix['1950':].plot(ax=ax)\n", "fig = arma_mod30.plot_predict('1990', '2012', dynamic=True, ax=ax, plot_insample=False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def mean_forecast_err(y, yhat):\n", " return y.sub(yhat).mean()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5.637114370345064" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Can you obtain a better fit for the Sunspots model? (Hint: sm.tsa.AR has a method select_order)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulated ARMA(4,1): Model Identification is Difficult" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(1234)\n", "# include zero-th lag\n", "arparams = np.array([1, .75, -.65, -.55, .9])\n", "maparams = np.array([1, .65])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make sure this model is estimable." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arma_t = ArmaProcess(arparams, maparams)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arma_t.isinvertible" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arma_t.isstationary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* What does this mean?" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BAABAhU7f4uhrTcVXXUQk7c31JiICAACAReMX2B0zMtiS1J6KrbqISNqLiCy4aIaICAAAAKo1V0SkNRVbdWP6Jh2vgx1daIoIEREAAABUaWAiq2jYqKkuesr1tvq4BiezstYu050FL1NuB5s52AAAAKjWwHhWbanpNem+tlRMuYLVuJdbXg0mK50iwqp0AAAAVGpgInvKDGzf9LKZ1RMTyXgRkeSCi2boYAMAAKBKAxNOabHMTNPr0lfPqL7JbLkdbApsAAAAVOn0Nek+v+geCLiD/exgWnc+eTLQ5yxXJucX2PN3sCPhkCIhwyFHAAAAVMZaq8EJZ9aIyHQHO9gC+1/uPqS3fnqPvrb3WKDPW47JbF6RkFEssnDZGY+EmIMNAACwmllrte/4aKDPOZbJyykU1TFLB9vPYAddYB8fyUiS/vArj+jepwYCfe6FpJ3CgktmfPFomIgIAADAavbjJ/p0y0d/poeOjgT2nP1zbHGUpEQ0rPp4pLSIJig9Ixldu6VNG9tS+p3PPaCDJ8cDff75ZJzCgmvSffFIiIgIAADAavajJ9zc8lN9E4E951xr0n1BL5ux1qp3dErbuxr1ybfsUjwS1ls+tVt941OBvcZ8Jp38ggccfW6BTQcbAABgVbLW6u79/ZKko8PpwJ63VGDPksGW3Bx2kGP6RjM5pZ2C1jUn1N2a1CffslNDk45++9N7lHYWf952xikoucCSGV88EiaDDQAAsFo9eWJcJ8bcLu/RoUxgzzswPn8Huy0V02CAHeyeEffPsK65TpJ02YZmffRNV+qxnlH91y/sVaG4uFsjJ528kgusSffFo0REAAAAVi2/e31+WzLgDrajkJFakrN3sFtTMQ0GmMHuHXXfHKxtSpSuvfjiLv35q3boR0/06S++9diirmavrIMd0hQdbAAAgNXp7v19umhto55zXouODwfXwR6czKo1FVc4ZGb9elt9XEOTTmBFb8/oqR1s35uv2ai3X7dJn73vGX3iZ4cDea3ZTDqFCjLYYTrYAAAAq9HYVE57nhnWC7d3aENrUr2jGeUKwXRW+8cdtdfP3r2W3IhIvmg1lgkmH907klEkZGaNpPzxyy7Syy9dow9+9wl979HeQF7vdBmnsOCSGR+HHAEAAFapnx8cUKFodcP2TnW31Klo3VF3QRiYyKqjYfb8tTS9bGYwoHXpPSMZdTUmZu2Yh0JGH3rDFbqyu1nv/tJDeuCZ4UBec6aKpohEKbABAABWpbv296khEdFV5zVrQ0tSknQsoJjIXGvSfa3euvSgRvX1jE5p/WnxkJkS0bD+7c07taYpobd/do+ODEwG8rq+dEUdbCIiAAAAq461Vj850K/rtrYrEg6pu9UtTo8O1X7Q0VrrFdjzR0Qk9zBkEHpHM1rbnJj3MW31cX36t66WtVZv+dT9gRX3+UJRTr5Y2RxsDjkCAAAE4zP3HtFNH/6p8gFlnav1RO+4To5l9cLtnZKkNY0JRUImkEkik05BU7mi2ubpYPsRkSCK3GLR6sTolNY2zd3B9m1qT+nfb9upntEpveOzezSVq72TnPaeg0UzAAAAy+C7j/Zq/8lx7T4SfA64Enft75MkvXBbhyQpEg5pbXMikFnYC83AltwxfZICGdU3MJlVrmC1boEOtu8557fqw2+4QnueGdZ7vvywijXOyM44foFd7hxsIiIAAACByOYLeujoiCTp+4+dWNZ7+cn+fl28tlGdjdNFaXdLUscC6GBPr0mfOyISj4TVEI8EsmzGXzJTTgfb94rL1up9L7tQ33m0Vz9+sq+m15/MupNQKu1gL+Zc7qVAgQ0AABY0msnpKw8cW7TCZ9/xMWXzRTUmIrpj34maO6fVGs3k9MCzw7rhwo5Trne3JHU0gEOO0wX23B1sSWqtD2abY683+aTcDrbvjbu6JUnPDNZ24DHtVB4RsVbKFSiwAQDAKvd/fvKU/sd/Pqx9x8cW5fl3HxmSJP3+jVt1YmxKDx8bWZTXWcjPvPF8fv7a191ap/7xbM255H7v4OJ8Y/ok96DjUABj+kpLZiroYEtSU11UsUhI/eO13UO60ohIxC3EV3pMhAIbAADMq1i0+vre45Kk+54eWJTX2H14SBe0p/SGXd2KhIzu2Lc8MZG79/epMRHRld3Np1wPalSfn8H2c9ZzaU3FNRjAFJHekYwS0ZCak9GKvs8Yo86GuE6OTdX0+mnHi4iUuyo96pamK/2gIwU2AACY1y+eHlTv6JRCRrrvqcHAn79YtNrzzLB2bWxVU11Uz9/SrjseO7HkOVxrre4+0K/rtnUoEj61RCqN6qsxhz0wkVVLMqpoeP4SrD2oiMjolNY11cmY2deyz6erMaG+wDrY5UdEJApsAACwyn31weNqiEf02qs26P7DQ4GtDPcd6BvXaCanXZtaJUkvu2SNnhlM64ne8UBfZyGP9Yypfzxbmh4yU7ffwa5xFvZCS2Z8ramYhiadmrPox0cWnoE9l2A62G6Bnao0IhLAiMDlRIENAADmlHby+t6+Xr3isrW6YXunJp2CHj0+Guhr7D7s5q+v3ugW2C+5uEvGSHcs8TSRnxzolyS9YPuZBXZ7fVyxSKjmg46DE05ZBXZbfVyFotXYVK6m1+sdzVScv/YF08F2IyJ1dLABAABcP3jspNJOQa+5cr2ed4FbAAcdE9l9ZFhdjfFSDKO9Pq5dG1t1x77eQF9nIXfv79Ml6xvV2XBmxzcUMtrQUlfzqL6BiazaFzjgKE1vc6wlJpIrFNU3ntXaedakz6ejIa7xqXxplnU1Ko2IJKL+IUcKbAAAsEp99cFj2tBSp10bW9VWH9f2rgb94ungCmxrrXYfGdKuja2n5IRfdskaHTg5oaf6JwJ7rfmMpnN64JlhvXBb55yP6W5J1rxsZmDCmXcGts/f5ljLQceTY1OyVlrXVF1EpMubA943Xn1MJO0UZIyUiFTYwSYiAgAAVqOTY1P6+aEBvfbK9QqF3OL3ms1t2n1kKLAxaseGM+odndIuLx7iu2nHGklasmki9xzqV9HqjPnXM3W31tV0yHEqV9BENl92BltSTaP6er0RfdV2sDu9TnstMZF0Nq+6aLj072chTBEBAACr2jceOq6ilV5z1YbStWs2t2kqV9TDR4PJYfvzr08vsNc11+ny7uYl2+p49/5+NdVFdUV3y5yP2dCS1Eg6p/Eqc9H+TOmOMgpsvwgfqKGD3eMvmamyg93Z6N5DLQcd07lC2TOwpZlzsCmwAQDAKmOt1VcfOK4rz2vWpvZU6frzNrXJBDiub/eRITUkItq+puGMr928Y40eOTYayIry+RSLVnfv79d1W9sVnqfT2l3jLOzSFseGhSMiLUm/g11LgV1bB7vLy6L3jdXWwS43fy3NPORIRAQAAKwyj/eOaf/Jcb12RvdakpqSUV28tlH3PhXMwpn7Dw9p5/ktsxa2N1/ixkS+/9jJQF5rLo/3jmlgIqsbts+dv5ZmzMKuclSf341uSy3cwY5FQmpIRDQ4UUtEJKPGRET18fI7yDM1J6OKhUM6WWMGu7IC2x/TRwcbAIBzxmjGPQy32n3tweOKho1uuXTtGV97/uY27X12pOa14YMTWT3VP1maf326Te0pXbimYdGnidy9v0+SdP0s869n8jvY1Y7qm+5gL1xgS25MpJYpIj0jU1pXZfdacrc5djTE1V9LB7vSApsMNgAA55Z8oai3fWa3Xvev9wbWwT0b5QtFff2hHt14YadaZlnpfc3mNjmFoh6s8Y3GHu/7r944e4EtuV3sPc8M1zTJYiF37e/XZRua1LFA4ducjCoVC1cdWfHXpLctsCbd15qK1TRFpHc0o7VV5q99nY3xGjvYeaUq6KATEQEA4Bzz0TsPafeRYTUno3rvfz6iiWx+uW9pUdxzaEADE9kz4iG+XRtbFQ4Z3VfjuL7dh4cUi4R06YamOR9z8yVrZK07j3sxjKQd7X12eNbtjaczxqi7tfpRfQMTWTUkIqVZzwtp87Y5VqtnJFN1/trX1ZCoLYPtFFRX5p9Xmo6ITBERAQBg9fvl04P66J0H9bqrNugTt+1U72hGH/zO48t9W4viaw8eV3MyOmcmuSER1aXrm3RvjQcddx8Z0hUbmktF1Wy2dzVoU3tq0aaJ3HNwQEUrvWCB/LVvQ0uy+g72hFPWBBFfW32s6ohIxiloOJ3T+hoL7M7G2talp51CRR3sGB1sAADODSNpR+/+0kM6vy2lv7h1h55zfqveft0F+sL9R0v53dVifCqn7z92Qq+8bF2p2JnNNZvb9PDREU1W2cWfzOa1r2dMuzbNPRZPcrvGN+1Yo/ueGtRIuvpu7lzu2t+n5mRUV3Q3l/V4d5tjRtbail+rfyJb1gxsX1sqruG0o2Kx8tfqHXW77LVGRLoaExqbyledt087hbLXpEtSOGQUDRsy2AAAnC2stRqu4Ufqcz3nH331EQ1MZPWRN15Zmsjw316yTVs76/W+rz6q0Ux1c5Hn8vW9x/Vfv7BXH/nxQd2xr1dP90+oUEWRVY3vPXpC2XxRr71q/byPu+aCNuWLtpSjrtTeZ0dUKNoz5l/P5mWXrFG+aPWjJ4J9M1MsWv30QL+u39ox73i+mbpbk5rI5jWSrvy/ubsmvbz8teRmsAtFW9W/r9KSmabaOth+Lr3amEjayStZQUREcmMiK32KSHVzWwAAOItMZPP62oPH9Nn7ntGh/gn9zWsu1RuvPi+Q5/78L5/V9x87qT99xUWnZIUT0bD+8Q2X6zX/cq/+4luP6UNvuCKQ1/vGQ8f17i89pOZkVN98uKd0PRYJaUtHvbZ11WvbmgZt62zQ9jUNWt9cV/aWvHLcvveYNrWnFuzo7tzYomjY6L6nBvWCMvLLp7v/yJBCRnrO+fN3sCXpsg1NWteU0B37evWrz5k9F16Nx3rGNDDh6IXby7//7hZvVN9wetYDoPMZGM+qfUt72Y8vrUufzFb8WqUlM821d7Ald136eW3Jir63WLTK5ApKVjgmMB4JrfiICAU2AKwyU7mCfnKgX8eHM3r1letLK5eD9MunB/WhHx5Qz2hGr75ivd6ws1vdrZX9n28QDvVN6HP3HdFXHzyuiWxel65v0tUbW/W+2x9V2inorb+yqabn339iXH/17cf1gm0deuu1Zz7XZRua9c4XbtZH7jykm3es0Uu99d7V+umBfr3nyw/ruZta9Zm3Xq2itTrUN6H9J8Z1sG9CB06O6/7DQ/r6Q9OFdzIW1tbOeu1Y36T3vGSb2iqIIJzu2HBav3h6SO95yTYZM3/RnoxFdEV3s+6rcprKniNDumhtoxoS0QUfa4zRTZes0ed/+awmsvmq5zqf7q79fTJm4fF8M/n/zo8OZXTZhvJiJZLk5IsamypvTbrPn5c9OOFoS3kR8RJ/ycyaWqeINPjbHCvvYE/lC7JWFY3pk/wCmw52oIwxN0v6J0lhSf9urf3bZb4lACuctXbBYmGlm8oV9NMD/fruo7360RN9pekWf//9/fr1556nd1x/QakTVYu9zw7rQz88oHsODqizIa7taxr0z3cd0sfuOqRf2dKuX9vVrZdc3DXvobVa5QtF/fjJPn32viP6+aFBxcIh3XLZWv3mNefriu5m5QpWf/DFvfrLbz+uTK6gd96wparXmcoV9PtfeFANiaj+4fWXz9klfteNW/WjJ/r0J197VDs3tlb9huahoyP63f/3gLZ2NejfbttZmjRx2YbmMwq5samcDp6c0MGT49p/clwHT07oKw8c08GT4/r82543b3Z6Pl/fe1yS9Oor54+H+K65oE0fu+uQxqZyaiyjUPblCkXtfXZEv7aru+zvuXnHGn3q50d09/4+3XLZurK/bz537+/TZeubKip6N3gd7EoPOg5OejOwK3gt/99SNQcde0czaq+P1/y/xZkd7EqlHbcLnaq0wI6GKbCDZIwJS/pnSS+RdEzSbmPMN621q/OYNs5grZW1CvTHnWcjJ19UJldQxikokyso7eRlrdSYiKqxLqKGRLTsPOB88gW3YzKayWksk1O+aFUfjygVD3u/RxQNL+5RDGut0k5BE9m8xqfymszmNeH/msqrULTqakpofXNCa5vqKjptfvrrDEw4OtjnFhvTv08o7eS1vavBFJjSAAAgAElEQVRBF65p1EVrG3Th2kZdtKZRTcnyC4K5Xu/ZobSODqU1ns0rGjKKhEOKho2i4ZAiIaNoJKRoKKRI2My4HlIiGtK65rqyx3XNZipX0D0HB/SdR3pKRXVzMqpbLlurl1+6Vp2NcX38J0/r0/ce0efue0a/unODfvf6zRX/mFeSHu8Z04d+uF8/eqJPramY/vQVF+m/PO98JaJh9Yxk9J97junLe47qXf+xVy3JqF571Qa9cVe3tnaduf66WoMTWX1x91F9/hfPqGd0SuuaEnrvTdv1a7u6TylaYhGjj77pSr33K4/o77+/X5PZvN570/aK32R94DuP68DJCX32rVfPOx85FgnpH99wuV71sZ/pz76xT//861dV/Gd7qn9Cb/30brXVx/SZ39q1YLHamIjqOee3nBKv+NbDPfr9L+zVn319n/72dZdW/Oe11ur2vcd19abWsn8a8bzNbfrInYe0+/CQXnRRV9mvte/4qDK5gq6eY8HMbHZubFV7fUzf23cikAJ7eNLRQ0dH9Ps3bq3o+xoSUTUnozpaYYE9MO4Wye315b8Ba6+vvsDuGZ2qOR4iSS3JqKJhU1UHO511C+y6WBURkRqXGC23s6rAlnS1pEPW2qclyRjzRUm3SjqrCuy//u4T+s4j7lapUEgyMgoZ90dYRpLxPg4Z92v+59ZaFYpWBa+ILBTdz611rxWKmvGxlZFbaIaNUSjkPp//cThkFPJeY/pjI6vpItXK/b1orawkzfjY/3rRe4NoT7vufp/7Te41lU5M+6838/dw6NR78a8ZY1QsWuUKReW9P2+uUFS+YJUvFpUrWOW9r/lfl6RIyCgWCSkaDikWCSnmFS0zr0XDIcUjbhFT9P4+88Vi6e+1MOM580Wr4ozPqzn9HQ67/y3CIaNIKKRQyCgSmv6zhk/7PFcoKu1MF9GZGR/nyzisVB+PqKkuqoZERI110VLx3eR93JCIKOMUNJrJnfFr3Cuqy5nRGwuHlIqHlYpHSkV3Kh5RKhZ2T357/24K1s3TFb1/n0X/unfN/3gqV9RE1iukp/Ka8N48lKupLqp1zXVa35zQuuY6rW2q07rmhNY312ldc506G+IamnR0wC+i+yZ06OSEDvSNn3LoqCER0bauBr304i7VxcLaf2JcP3j8hL6052jpMeuaErpobaMuXNvg/r6mUZvaU6U3N1O5go4NZ3R0KK1nvV/PDKZLn2dq/D8AY6R1TXXa1J7S+W1JbWpPaWNbShvbU+purZu185TNF3TPgQF959Fe/ejxkxrP5tVUF9UrLl2rl1+2Vs/f3HbKm6YP/doVeveLt+n//PQpfWXPMX1p91G96vJ1+r0Xbi6r+D3UN6EP/+iAvvNIrxoTEb33pu267fkbT/kR/brmOv3Bi7fqXTdu0c8ODehLu5/VZ+87ok/87LCec36Lfm1Xt265bK2SFf6frO+hoyP67L1H9O1HeuUUirp2S5ve/6odetGFnYrM8QYxEg7pH19/uRLRsP7l7qeUdgp6/ysvLrvovGPfCf2/Xzyrd1x/QVnxgYvWNurdL96mv//+ft28o0evvLz8AvDk2JTe/In7ZSR99q3PVWeVP2l45eXrtP/EuD521yFtX9NQcTzm4WOjerp/Ur9z/QVlf89V57UoFgnpvqcGKyqwdx8ZkuTmuMsVDhm95OI1+sZDxzWVK9T05lSSfnqwX0WrivLXvu6WymdhV7rFUVIpd13NuvTekYwu6EhV/H2nM8aosyFRXQc75/5/UMUdbCIigVsv6eiMz49Jeu7pDzLGvEPSOyTpvPOCOcRSiS0d9bpmc5uKXuXpF61FO7NQnVHcesVqOCS3EPaLZjOjgC4VzioVy9JpBUxRKlg7XeR4Bc/MAueUIl+m9AZAxn3e6a95bwhmvgk47fs0y3Vp+h78NwvF4qn3cvo9ho1KXb1IyC2II2Gv0+d1/CJhtziNuC+sXKGoXKEoJ1+UU7By8sXTrrm/T2bzyhXsKcWu2x08s+ANe6/tvzGphJWmC3RrVSh4Rbv1i/Zi6c1DJuf+PcTCIdXHI+qoj6suFlYyFlYiGlZddPrjZCyiulhIddGIQkYam8prLJPT2FROYxmv8zzldp+Pj2T0RK/78fiMwjkZC6sxEVVTnftrQ0vSLcK9Ynzmr1DIKJ0tlLrIk1m3AD7lmuO+bs9IRhmnIHPKm7hT39CFQprxBtD9t9yQiGhdc0KpWET1Cbdo9wv3hsT0x/71cMjoxNiUekYyOj6SUc9IRr0jUzo2nNH9h4c0NjX/m4Smuqi2ddXrZZes1bauem3tbNDWrnp1NsTPKKasteobz+qJ3jE90TuuJ0+M6cnecd19oL/05i4eCWlTe0oj6ZxOnDb7tS4a1nmtSXW3JnXtlnad11qn89qSOq81qca6qPIF6/079d5A5q1yRe8NZcH9d+u/uZzMFvTsUFpHBid1ZGBS33q455Q/a8i4hatffJ/fmtITvWP64Yyi+mWXrtErLlt3RlF9uvPakvrr11yq/3rjVv37PU/r8798Vl/be1w371ijd96wZdYlH88OpvW/f3xAX997XHXRsH7/xi1623UXqKlu7s5qOGT0gm0desG2Dg1MZHX7g8f0xd1H9YdfeUR/+a3H9aor1ul1V61XIhou/VRl5r9z/5r/RnFsyp3UMDCRVX08ojdd3a3fvOZ8beksryseChn99WsuUV00rE/+/LCmcgV98DWXLvjToZ6RjP7oq4/o0vVN+h8v3V7Wa0nS71x/gX7w+En92Tf26bkXtKqzYeFCeTST05s/cb9G0o6++I5rtKm9toLov79kmw6cHNcHvvO4tnTWV5Qtvv3BY4pHQnrZLKvR55KIhvWc81oqnod9/+FhbWxLlvV3NNPNl6zRF+5/VvccHNBLLi6/oJ/NT/b3qyUZrShH7dvQUqf9J8cr+p5+r0iuZA52NBxSU1204mUz1lr1jGR0bQUHKufT0RCvaorIZKmDXcUUEQ45Lj1r7cclfVySdu7cuTRzi2Z4w65uvaGC3BgQtELRaiKbV100XHXW8mwy34+jJ7J59ZaK7ymdGM2orT6urZ312trVoPb6WNldSWOMuhoT6mpM6IUzlkpk8wUd6ptwi+7eMT3VP6Ed65p0XmtS57XVlYrqjvozi/YgDU86OuwV3EcG097vk/rGQz0an3KL6psvWaNXXLZWz9/cXvF/+zVNCf3pLRfr927Yok/9/LA+fe8R3fHYCV2/rUPvumGLrt7Uqt7RjD565yF9efdRhUNGb7vuAv3O9RdUfHCuvT6ud1y/WW+/7gLteWZYX7j/Wd3+4DH9xy+fnfXxIaPST2r8N4hrmhJqTES1Y32TXnPl+qoOthlj9Ge3XKRUPKyP3nlImVxB//D6y+d8Q1IoWr37iw8pXyjqI2+6sqK/Y79r/oqP3KM/uX2f/u3Nz5n338tUrqC3f2aPnh6Y0KfecvW82wzLFQoZffjXrtDr/vVeves/HtTX33mtLuioX/D7nHxR33q4Ry+5uKuiLLXkzsP+8I8OaCTtqDm5cPyhWLTa88yQXlJBx7v0Whe0qTER0R37TtRUYBeLVj850K8XbCt/PN9M3a1J3flkX0XnO/wOdlsFERHJ3eZYaURkbCqvSadQ85IZX2dDXEcGJyv+voyfwa50ikg0tOK3pJ5tBfZxSTMr1w3eNQAzhENm3k7ialIfj2hrV0OgWd7TxSNh7VjXpB3rai9watGSiqklFdNV5536Y3NrrUbSOaXikUDeULWmYnrPS7fr7ddfoM/d94w++bPDesP/vU871jXqYN+ErLX69eeep3fesKXmg5HGGO3a2KpdG1v1/lfu0D0H+xUJhU75KUtjXVT1sciinb0wxug9L92uulhYf3fHfmWcgj7661fOGsH52J2HdP+RIX3oDZdX1U3e0lmv9960XR/4zhO6/cHjet0cI+XyhaJ+/wt7tfuZIX30TVfqV7YG02mU3GLm3968U7f+88/1ts/s0dd+79oFzxvcvb9Pw+mcXjfHavT5XLO5TR/6ofSLp4d08yULT1E51D+hkXROuyrIX/tikZBefFGXfvTESeUKxarPkDx6fFSDk84pb7Qr0d1Sp2y+qP7xbNmRnoFxR8lYuOKoVFt9rOKISGnJTAAZbMk96Hi/F+upRNpxi+RKVqVL7k8SBydWdkTkbGt97Za01RizyRgTk/RGSd9c5nsCgGVljFFLKhb4TysaE1G984Yt+tkf3aj3v/JiFYpWr75ine76Hy/UX956SSBTR2ZqqovqlsvW6eZL1uiazW3asa5JG1qSakxEl+Rg8++9cIv+/JUX6wePn9TbP/tAqbvm231kSP/04wN6zZXr9doqCk3fb127Sbs2tujPv/VYqdCZyVqrP/36Pv3w8ZN6/y0XBzYRY6bu1qT+z395jo4Op/WuLzyofGH+YuX2B4+rvT6m66oo9C/f0Ky6aFi/eLq8mIifv766jAUzs7n5kjUazeTKfr3Z3L2/v+LxfDNt8Ef1VXDQcaDCLY6+1lSs4ohI70gwS2Z8nQ1xjaRzFW9z9KeIVD6mb+VHRM6qAttam5f0Lknfl/SEpC9bax9b3rsCgNWtLhbWb127SXe8+3r93a9erg0tSz/Peqm85dpN+rvXXaZ7Dvbrtk/dX/ox9Gg6pz/4wl51tyb1l7fuqOk1wiGjf3j95coXrP7wK4+ccaj6H39wQF/cfVTvumGL3jLLbO2gXL2pVX916yW65+CA/vq7T875uJG0ozuf7NOrLl8/56HR+cQiIe3c2KJ7y5yHvfvwkDoa4jq/imk2klsUJ2NhfW/fiaq+X3LnX1++obnqkYrdpVF95R90dAvsyl+vrT6uwYnKCuye0WCWzPj8N9v945V10tPVRkRWwSHHs6rAliRr7XettdustZuttR9c7vsBAKwub9jVrX9645V64Jlh/ca//1Kj6Zzed/sj6ht3V6GXs/hkIee3pfQnL79Q9xwc0Bfunz67/+mfH9bH7jqkN+7q1nteuq3m11nIG68+T295/kZ98ueH9eXdR2d9jD+dZaHV6PO5ZnObDpycKOWM57P7yLCu3tha9XmGRDSsG7Z36gePnaxqffzQpKOHj41UNT3E578JPTq0+B3stlRMw2mnoj9r78iUwiFT8SHSuXQ0euvSK5wkUoqInINzsM+6AhsAgMX2qsvX6V9/4yo90TOmF3/4J/revhN6703bdfkC68Er8RvPPV/XbmnTB7/zuI4OpfWth3v0F99+XC+9uEsfePUlS7b86E9fcZGu29qu//n1R0vxjJluf/CYtnc1aMe6xqpf45oL2iRpwdjGce/AciXj+WZz0yVrNDCR1QPPDFf8vfcc7Je10g1V5q8lt8jvaIhXNKpvYMKpaESfrzUVU9G6P2koV89IRmsaE4HsU5CkLq9Qr3SSSCkiUkUGe6XPwabABgCck166Y43+7badGp/K6bqt7Xr7deXPfy5HKGT0d796uYwxettn9ui/f/kh7Tq/VR9505VVRTGqFQmH9LE3XaUNLUn97uceOGUD4ZGBST347Ihec9X6mgr+S9c3qT4e0X0LjOvbfdgt8HdVmb/23Xhhp2LhkO6oICaSLxT17Ud69JEfH1RbKqZL19d2qHlDS13ZGex8oajhtFNdB9v7nkpy2D2jGa2tcUX6TJ2N/rr0yjrYk05esUio4n/v8SgREQAAVqwXbOvQPX94oz5x265FOWi5vrlO/98tF2v/yXFt7qg/ZQX6UmpKRvXvt+2UUyjq7Z99QJNe9vz2vcdljPTqK6qPh0huEX/1ptYFC+z7jwypIR7RRWur75ZL7nSh67a26/uPnVhwcdhoJqeP//QpveDv79a7/mOv8kWrv3ntpTX/9+5uSZadwR6adGSt1FFNBtvLiQ9UkMPuHZ3S2oBG9ElSazKmSMior8IMdsYpVLxkRvIPORarWgp3tjjbxvQBALCk5luDHoTX79yghkREuza1Lut4zc0d9frYr1+l3/rU/frvX35I//Ibz9HX9h7Tr2xp15oAup3XXNCmO5/s08mxqTkn0Ow5MqSrzm8JJLpw8yVr9OMn+/To8dFZl8UcGZjUp+89oi/vOaq0U9BzN7Xq/a+8WC+6qCuQ1+9urdN3H+1VoWgXfD5/yUx1HWy3wC63g22tVe/olG7eEVwHOxQy6miIV7wufTJbqGqDa9ybmOQUirOO01wJKLABAFhExpiKtiMuphds69CfvPwifeA7T+h3PrdHR4cy+m8vDuaw5TWb3Rz2fU8N6tVXntkRH550dODkhG6tsVvue7FXKH9v34lSgW2t1S+eHtInfnZYP37ypCIho1devk5vvXaTLqkxEnK67pak8kWr3tHMgpN3/Ckg1WawJWlwsrzidnDSkZMvBhoRkaTOxsrXpWdy+YpH9EnTBXY2T4ENAABWgN/+lU3af2Jc//nAMSVjYd20Y+HlMOW4eG2jmuqiuvepgVkL7D3egcRa89e+llRM11zQpjv2ndC7X7xV3364V5/8+WE91jOm1lRM77phi37zeeeXvQimUn5RfWx44QJ7oIYOdqu3HbPcUX09I/6IvuAiIpI7C/vZwfKnpkh+B7uKAtuLUWVzRWlx/vMtOgpsAADOIcYYfeA1l2hsKqdtXQ0VzyieSyhk9NxNrbpvjkkiu48MKRYO6bIAVsL7brpkjf7s6/v0/L+5U4OTjrZ21utvXnupXnPl+kXPune3ugXs0aG0nudNUZnLdIFdeQY7Eg6pORktOyLS4y2ZCbrA7mqMa0+F2xwzTm0RkZW8bIYCGwCAc0w8Etb//c2dgT/vNZvb9IPHT+rYcPqMru79h4d02YamQAvfm3es0T/96KB2rGvUW39lk67f2r5k4w/XNdcpZKSjZRx0HJhwFI+EVF/lm5m2VKzsiEhpTXrQEZGGhIbTOWXzhbJjG+lcvjTirxIzIyIrFQU2AAAIxPM3u6vW73tqUK/fOV1gp5289h0f1duvD3YUYkdDXHv+9MWBPme5ouGQ1jbV6VgZy2YGxt0lM9UW/22p8rc59o5OKR4JVb2lci5d3qi+/vFs2dte09mC6tqqmyIiqeLV7GcTxvQBAIBAbOuqV1sqdsa4voeeHVG+aHV1QPnrs8X6lrqyRvX1T2SrOuDoa6uPabDsiIg7AzvoTr6/FbKSSSJpp6BUNRGR6MrvYFNgAwCAQBhj9LwL2nTf04OnzDDefWRYxkhXnV/bBsezTXdLsqxlMwMTjtpr6Ci3pmIVZLAzgeevpelxlv0VTBKZdPIVr0mXZkREchTYAAAAumZzm3pHp/TMjIkTu48M6cI1jcs6B3wxdLfW6cTY1IKH8QYmslVNEPG1pWIaTjsqFBdevNI7OqW1TcEX2P5s83I72NZad9FMvPqIyEo+5EiBDQAAAlOah+1NE8kXinrw2WFdvXF1da8ld1SftVLvyNxd3WLRamjSUXtD9R3stvq4rJWG0/N3sfOFok6OTWldc/Cz7dpSMYVDpuxZ2E6hqHzR1jhFhA42AACALmhPqbMhrnu9HPZjPWNKOwXt2rS68teS1N3ijeqbJybid55r6WCXls0scNCxbzyrotWidLBDIaOO+rj6yuxgZxy3+1zNHOwEGWwAAIBpxhhds7lN9z3l5rB3e7OTg1owczbpbnWnaRwdmvug44C/xbGWiEh9edscSyP6FqGDLUmdjXGdHC+vwJ6socAuRUSYIgIAAOB6/uY2DUxk9VT/hO4/PKTzWpOlDO9q0tWYUDRs5u1g17LF0deWcr93oYOOx72oyvpFOOQouZNE+sbKi4hknLwkEREBAAAIwjUXuPOw731qUHueGV6V3WtJCoeM1jXPP6rPL7A7aspglxcR6R1ZnCUzvs7GuPrK7GCng+hgU2ADAAC4ulvrtL65Tv/xy2c1NOno6k2r74Cjr7slqaPzLJvpH6+9g92SjMkYLTgLu3d0Sg3xiBoSizOtpashoaFJR04Zhe9k1i2wqxrTF135q9IpsAEAQKD8edhPnhiXtDrz177u1jodmyciMjjpKBo2NY0oDIeMWpIxDU7M3z3uGcksWv5acjvYkrs4ZyGZnBsRqWbRTCzMHGwAAIAzPN8b19deH9Om9tQy383i2dCS1MCEU5qacbqB8azaUtWvSfeVs2xmsWZg+/x16eXksP0OdjURkVDIKBYOEREBAACYyZ+HvWtja+Bru88mG7xRfXN1sQcmsjXNwPa1pmILZrAXa4ujr5J16aUxffHKO9iSe9CRiAgAAMAM65rr9Lsv2Kzbnr9xuW9lUZVG9c1ZYDs15a997fWxecf0TeUKGpx0tG6RDjhKMyIiZSybmfSniEQr72BLbg57JXewq3tbAQAAsID3vezC5b6FRdfdMv8s7IGJrLavaaj5dVpTsXkPOZ4YdYvetYvYwW5LxRUy5XWwS1NEqliVLrmTRMhgAwAAnIPa62NKREOzRkSstRqccEpj9mrRloprJJ1TvjB70dnjLZlZzA52OGTU0RAva1162skr7GWpq+F2sImIAAAAnHOMMdrQkpy1gz2WycspFNURQETEL9KH07lZv947svgdbMnNYZfbwU7GwlXn7+OR8IqOiFBgAwAA1KC7pW7WDHZ/AFscff42x7ly2KU16YvYwZakzobyls2ks4WqJoj43EOOFNgAAADnpA0tyVm3OQaxJt3XmnI72ENzTBI5PjKltlRMiSoPFZars7G8denpXKGqGdi+eCSkbI6ICAAAwDmpu7VOo5mcxqZOjW+UCuwAxvS1exGRgTkOOvaOLu6SGV9nQ1yDk45yc2TBfRknX9UWR188SkQEAADgnDU9SeTUmMhAAGvSfdMd7DkiIiOLu2TG19XoFvH9C8REJomIAAAAoFqlWdinHXQcmHAUMlJLsvYOdnMyJmM056i+ntHMok4Q8XU2eNscFyiw07mCkrVGRJgiAgAAcG6aa5vjwERWram4wqHaN1mGQ0atydlnYY9P5TQ+lV/ULY4+v4N9coEcdjqbr7GDzRxsAACAc1ZTXVQN8cgZBx0HJrKl7HQQ3HXpZ3aOe5dgyYzP3+a4YAfbqbGDzRxsAACAc5cxRhtak2dmsCccdTTUnr/2tdXHNDRLB7tnZPGXzJTuIRVTyEj9C3WwnVo72CE62AAAAOey7pa6OTrYARbYqbgGZxnTt5Qd7Eg4pLb6+ILLZtJOoeo16RKLZgAAAM55G1qSOjqclrVWkrsmPeiISFv97Bns3pGMQkbqCrBbPp+uxvnXpReKVtl8UclobYccnUJRxaKt+jmWEwU2AABAjbpb65R2CqUIx6RT0FSuGGgHuzUV02gmd8YM6p7RKXU1JhQJL01Zt9C69LSTlySlaulgR90/i7PAvO2zFQU2AABAjUqzsL2YSJAzsH1t3nMNn9bF7hnJLPqK9JncDvbcBXbGcQ8n1rRoJuJ+70rNYVNgAwAA1GhD66mj+vwtjm1BRkS8ZTOnx0R6R6eWJH/t62hIaHAyq/wc3eVJr8Cu9ZCjpBU7SYQCGwAAoEbT2xy9DvZE8B1sf5vjzIOO1lr1jCzNkhlfV2Nc1rpTUmbjR0RqXTQjacUedKTABgAAqFEqHlFrKqajXge73ys+gxzT5x+YHJycjmcMp3PK5otLsibd19kw/7KZdBAd7KgXEaGDDQAAcO6aOarPz2D7XecgtKbcYn1mB7s0A3sJIyILrUufLrBr72BPkcEGAAA4d21oSerY0HQGuyUZVTTAyR7NdVGFjE5ZNjNdYC9lRGSBDnbWj4gEkcGmwAYAADhnbWh1O9jFog18yYwkhULGXZc+IyJSWjKzhBGR9vqYjFm4g52qqYNNRAQAAOCc192SlFMoqm88q4EJJ/ACWzpzm2PPaEaxcKg0YWQpRMIhtaXi6pszg+12sGsa0xelgw0AAHDO29AyPapvYCKr9kXYrNiaip0SEekdmdKapoRCIRP4a82ns2HuWdilDnYNi2YSzMEGAABAd6u/bCatgfFg16T7Tl+X3juaWdL8ta+rMT5nBtufg+0XydWY7mATEQEAADhnrfcmeRw8OaFJp7BIEZGYBiemO8c9I1Nat4T5a19nQ2LODnbGySsZC9fUVeeQIwAAAJSIhtXVGNdDR0ckSR2LUWDXxzU2lZeTL6pQtDoxNqW1y9TBHpiYfZtj2inUNEFEmnnIkQIbAADgnLahJalHjo1Kktobgo+I+HO1h9OO+sezKhTtkk4Q8XU0JmTtmWvbJbfAruWAozQjIpIjIgIAAHBO626p04Q3B3qxIiKSO2e7Z3TpZ2D7uvxlM2NnxkTSTr6mEX0SEREAAAB4/IOO0iIV2N5zDk066h1xDxku5RZHX+c8y2aC6GDHwiu7wK7t7QUAAABK/FF9UrBr0k9/zsEJNyIiLe2SGV9X49zr0tNOoeYOtjFG8UiIKSIAAADnuu4Wt4PdkIgoEa2tizsbf/Tf4KSjntGMUrGwGhNL3y9tr4/LmNk72JPZfM0dbMmNiTAHGwAA4BznR0QWY4KIJDUmogqHjIYms+odmdLa5joZs7RLZiQp6m2PnK2DnckVlAqiwI6GV2xEhAIbAAAgIGubEgqHzKLkryUpFDJqTcU0OOGodzSjtU1Lf8DR19GQmHVd+mS2oLoaIyKS38EmIgIAAHBOi4RD6m6pW9TZ1G2pmBcRmSott1kOc61Lzzj5YDrYkdCK7WBzyBEAACBA//c3d6qxbvFKrLb6mE6MTql/PLssBxx9XY1xPdE7dso1a63SudoXzUjuspmVesiRAhsAACBA29c0LOrzt6bi2n1kWJKWZYujr7MhoYEJd9lN2FuLPpUrylopGQ8gIhJduR1sIiIAAAArSFsqJscrPNctcwe7aKXBiemYSNpxl+wE08FmiggAAACWQNuM+drL2cHuaHBfe2YOO+24kY66AEYUruSICAU2AADACtJaP11gL3cHWzp1FrZfYKeCiIU/nT0AAA+hSURBVIis4EOOFNgAAAArSFvKLWxbktFAFrpUy1+XPrODPelFRAJZNMMcbAAAACyFNq+DvZwTRKTpZTozO9gZv4PNHGwAAACsFH4Ge90y5q8lKRYJqfW0bY6T2YAPOdLBBgAAwGLzIyLrlnHJjK+zIX7KNseM13EObg42BTYAAAAWWWNdRNduadO1W9qX+1bU2Zg4rYPtF9hBzcFemRERFs0AAACsIMYYff5tz1vu25AkdTXEtf/E9DbH0hzseDARkVzBnrLIZqWggw0AAICqdDbGNTDhqFC0kqYPOSYDmIOd8J7DWYExEQpsAAAAVKWrMaFC0Wpo0pEkTToFxcIhRcK1l5jxiPscKzEmQoENAACAqnQ2nDqqL+PkA4mHSO4hR0kr8qAjBTYAAACq4q9L7/cOOk46hUDiIdKMDnaOAhsAAADniNPXpWecgpIBrEmX3Cki0jkYETHG/L0x5kljzCPGmK8ZY5pnfO2PjTGHjDH7jTE3zbh+s3ftkDHmfTOubzLG/NK7/iVjTKyWewMAAMDi6vAiIn2lDnY+kBnY0rkdEfmhpEustZdJOiDpjyXJGHOxpDdK2iHpZkn/YowJG2PCkv5Z0sskXSzpTd5jJel/SfqwtXaLpGFJv13jvQEAAGARxSNhtSSjpQ522ikEWGCfox1sa+0PrLV579NfSNrgfXyrpC9aa7PW2sOSDkm62vt1yFr7tLXWkfRFSbcaY4ykGyV9xfv+z0h6dS33BgAAgMXX2TC9bCbt5ANZMiORwfa9VdL3vI/XSzo642vHvGtzXW+TNDKjWPevz8oY8w5jzB5jzJ7+/v6Abh8AAACV6mycXpceaAc7uoojIsaYHxlj9s3y69YZj/mfkvKSPr+YN+uz1n7cWrvTWruzo6NjKV4SAAAAs5jZwc4QEZFUxqp0a+2L5/u6MeYtkm6R9CJrrfUuH5fUPeNhG7xrmuP6oKRmY0zE62LPfDwAAADOUl2NcfWPZ1UsWk1mg4+ITJ1rERFjzM2S/lDSq6y16Rlf+qakNxpj4saYTZK2Srpf0m5JW72JITG5ByG/6RXmd0n6Ve/7b5P0jVruDQAAAIuvsyGufNFqKO0ok1uMiMgq7GAv4GOS4pJ+6J5T1C+stb9rrX3MGPNlSY/LjY6801pbkCRjzLskfV9SWNInrbWPec/1R5K+aIz5gKS9kj5R470BAABgkXU1ustmjg9nlCvYRYiIrLwOdk0FtjdSb66vfVDSB2e5/l1J353l+tNyp4wAAABghej0ls0cGZyUJKaIiE2OAAAAqEGnty798IBfYAe9aGblRUQosAEAAFA1f5vjEb/ADmhVejRsZMzKjIhQYAMAAKBqiWhYzcmoDg+68y6S0WA62MYYxSMhCmwAAACcezob4jM62MEU2JIbE8nmiIgAAADgHNPZkNBoJicpuEOOkuhgAwAA4NzkTxKRpFRAhxwlKR6lwAYAAMA5yJ8kIkl1QRbYkTBTRAAAAHDu6ZrRwQ48IsIcbAAAAJxrZnawg5qDLbkTSoiIAAAA4Jzjd7BDZnoDYxDcQ45ERAAAAHCO8TvYqVhExpjAnpcpIgAAADgn+VNEgjzgKPlzsCmwAQAAcI5JRMNqTESUCmhNus8d07fyIiLB/i0AAADgnNTVmFA0HGzvdqVGRP7/9u4u1tKrrAP4/zl7n9lDFGlL+bJTLcYa06hUrLVGLmqjtSChXKjRaGiMsRdygQmGIF40YrjwQlESQ9JgAyR+ERVpiAYrlugNSPmoRdFQDUgnhdYURGPazrSPF/udmZOmOG336tln7/37JSdn77XfM2dNVvrO0yf/tV4FNgAAK/uOFz83pwYXw8tzsBXYAADsoN/6yZcN/zOX52CLiAAAsIOO74/d4Jh4VDoAAAy1mM9y+vHO6cc2q8hWYAMAcCSdeWjNowpsAABY3ZkC++ENOwtbgQ0AwJG0mHLdm3YWtgIbAIAj6UwHe9Oe5qjABgDgSFrMz3SwFdgAALCysx1sEREAAFjdYv9Mga2DDQAAKzsbEZHBBgCA1YmIAADAQCIiAAAw0LlTRHSwAQBgZc7BBgCAgc5lsBXYAACwsuMelQ4AAOOIiAAAwEDz2V5meyUiAgAAoyzmeyIiAAAwyrLA1sEGAIAhFvOZDDYAAIyy2BcRAQCAYUREAABgoMV8psAGAIBRnCICAAADLfb3bHIEAIBRFvNZHtbBBgCAMRZzHWwAABjGKSIAADDQ8hQREREAABhi+aAZHWwAABhCBhsAAAY6ExHp7nVP5SlTYAMAcGQt5nt5vJPTjyuwAQBgZYv9Zbm6STlsBTYAAEfWYj5LkjxyanNOElFgAwBwZC3mOtgAADDM8f2pg63ABgCA1Z3rYIuIAADAys5uctygs7AV2AAAHFlnNzmKiAAAwOpERAAAYKBzx/TpYAMAwMo8aAYAAAYSEQEAgIFscgQAgIHOdrA9Kh0AAFYngw0AAAMdmymwAQBgmPlsL/O9ysMiIgAAMMZivqeDDQAAoyz2Z47pAwCAURbzPU9yBACAUUREAABgoMVcRAQAAIZZ7O9gB7uq3lhVXVUXT++rqt5RVfdW1T9W1csPXHtTVX1u+rrpwPj3VdU908+8o6pqxNwAANhsO5fBrqpLk1yf5D8ODL8yyeXT181J3jlde1GSW5L8QJKrk9xSVRdOP/POJL944OduWHVuAABsvl2MiLw9yZuS9IGxG5O8t5c+muSCqnpJkh9Lckd3P9TdX0lyR5Ibps++qbs/2t2d5L1JXjtgbgAAbLid2uRYVTcmOdnddz/ho0uSfPHA+/umsf9v/L4nGf96v/fmqrqrqu568MEHV/gbAABw1G1aBnt+vguq6m+SvPhJPvq1JG/JMh5yqLr71iS3JslVV13V57kcAIANdnzDIiLnLbC7+0eebLyqvjvJS5PcPe1HPJHkk1V1dZKTSS49cPmJaexkkmufMP6RafzEk1wPAMCOW+zvyCbH7r6nu1/Y3Zd192VZxjpe3t1fSnJ7ktdNp4lck+S/uvv+JB9Kcn1VXThtbrw+yYemz75WVddMp4e8LskHVvy7AQCwBZabHDenwD5vB/sZ+sskr0pyb5L/TfLzSdLdD1XVbyT5+HTdW7v7oen1LyV5d5LnJPmr6QsAgB233OS4RRGRp2rqYp953Ule/3Wuuy3JbU8yfleS7xo1HwAAtsOZU0S6O5vwqBRPcgQA4Ehb7M/SnZx6bDPOtlBgAwBwpC3my5J1U2IiCmwAAI60cwX2Zmx0VGADAHCkLeazJApsAAAYYrE/dbBPiYgAAMDKREQAAGAgEREAABjoTAf7YRERAABY3dkMtg42AACs7mxERAcbAABWZ5MjAAAMZJMjAAAMdC6DLSICAAArOxsROaWDDQAAKxMRAQCAgY7NRUQAAGCY2V5lf1Y62AAAMMrx+UwGGwAARlns74mIAADAKIv5TEQEAABGWcz3FNgAADDKsfleHjklIgIAAEMs9kVEAABgmGVERAcbAACGkMEGAICBFs7BBgCAcZyDDQAAA4mIAADAQB40AwAAAy2cgw0AAOMs9vfysA42AACMsZjP8ujpx9Pd657KeSmwAQA48hbzZdm6CTlsBTYAAEeeAhsAAAZa7M+SZCPOwlZgAwBw5J3tYG/A0xwV2AAAHHkiIgAAMNBiLiICAADDLPZ1sAEAYBgZbAAAGOi4U0QAAGAcmxwBAGCgc5scFdgAALCycxlsEREAAFiZU0QAAGAgEREAABjo3CZHEREAAFiZc7ABAGCgqsqx+Z6ICAAAjLKY74mIAADAKIv5TAcbAABGufLS5+Wbn3d83dM4r/m6JwAAAE/Fu276/nVP4SnRwQYAgIEU2AAAMJACGwAABlJgAwDAQApsAAAYSIENAAADKbABAGAgBTYAAAykwAYAgIEU2AAAMJACGwAABlJgAwDAQApsAAAYSIENAAADKbABAGAgBTYAAAykwAYAgIEU2AAAMFB197rnsJKqejDJF9bwqy9O8p9r+L2sh/XePdZ8t1jv3WK9d8uo9f7W7n7BU7lw4wvsdamqu7r7qnXPg8NhvXePNd8t1nu3WO/dso71FhEBAICBFNgAADCQAvuZu3XdE+BQWe/dY813i/XeLdZ7txz6estgAwDAQDrYAAAwkAIbAAAGUmA/A1V1Q1X9a1XdW1VvXvd8GKuqbquqB6rqMwfGLqqqO6rqc9P3C9c5R8apqkur6s6q+ueq+qeqesM0bs23UFUdr6p/qKq7p/X+9Wn8pVX1sem+/idVdWzdc2WcqppV1aeq6oPTe+u9xarq81V1T1V9uqrumsYO9Z6uwH6aqmqW5PeSvDLJFUl+pqquWO+sGOzdSW54wtibk3y4uy9P8uHpPdvhdJI3dvcVSa5J8vrpv2lrvp0eSXJdd78syZVJbqiqa5L8ZpK3d/e3J/lKkl9Y4xwZ7w1JPnvgvfXefj/c3VceOP/6UO/pCuyn7+ok93b3v3f3o0n+OMmNa54TA3X33yV56AnDNyZ5z/T6PUlee6iT4lnT3fd39yen1/+d5T/Cl8Sab6Ve+p/p7f701UmuS/Kn07j13iJVdSLJjyd51/S+Yr130aHe0xXYT98lSb544P190xjb7UXdff/0+ktJXrTOyfDsqKrLknxvko/Fmm+tKS7w6SQPJLkjyb8l+Wp3n54ucV/fLr+T5E1JHp/ePz/We9t1kr+uqk9U1c3T2KHe0+fP5h8O26i7u6qcb7llquobk/xZkl/u7q8tm1xL1ny7dPdjSa6sqguSvD/Jd655SjxLqurVSR7o7k9U1bXrng+H5hXdfbKqXpjkjqr6l4MfHsY9XQf76TuZ5NID709MY2y3L1fVS5Jk+v7AmufDQFW1n2Vx/Qfd/efTsDXfct391SR3JvnBJBdU1Zmmk/v69vihJK+pqs9nGem8Lsnvxnpvte4+OX1/IMv/ib46h3xPV2A/fR9Pcvm0A/lYkp9Ocvua58Sz7/YkN02vb0rygTXOhYGmPObvJ/lsd//2gY+s+RaqqhdMnetU1XOS/GiWufs7k/zEdJn13hLd/avdfaK7L8vy3+u/7e6fjfXeWlX1DVX13DOvk1yf5DM55Hu6Jzk+A1X1qiwzXbMkt3X329Y8JQaqqj9Kcm2Si5N8OcktSf4iyfuSfEuSLyT5qe5+4kZINlBVvSLJ3ye5J+cymm/JModtzbdMVX1PlhucZlk2md7X3W+tqm/LssN5UZJPJfm57n5kfTNltCki8ivd/Wrrvb2mtX3/9Hae5A+7+21V9fwc4j1dgQ0AAAOJiAAAwEAKbAAAGEiBDQAAAymwAQBgIAU2AAAMpMAGAICBFNgAADDQ/wFEn1N0mezUUgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax.plot(arma_t.generate_sample(nsample=50));" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arparams = np.array([1, .35, -.15, .55, .1])\n", "maparams = np.array([1, .65])\n", "arma_t = ArmaProcess(arparams, maparams)\n", "arma_t.isstationary" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arma_rvs = arma_t.generate_sample(nsample=500, burnin=250, scale=2.5)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x576 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(arma_rvs, lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(arma_rvs, lags=40, ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* For mixed ARMA processes the Autocorrelation function is a mixture of exponentials and damped sine waves after (q-p) lags. \n", "* The partial autocorrelation function is a mixture of exponentials and dampened sine waves after (p-q) lags." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " AC Q Prob(>Q)\n", "lag \n", "1.0 0.254921 32.687678 1.082211e-08\n", "2.0 -0.172416 47.670747 4.450704e-11\n", "3.0 -0.420945 137.159393 1.548466e-29\n", "4.0 -0.046875 138.271302 6.617701e-29\n", "5.0 0.103240 143.675909 2.958720e-29\n", "6.0 0.214864 167.132999 1.823718e-33\n", "7.0 -0.000889 167.133401 1.009206e-32\n", "8.0 -0.045418 168.185753 3.094835e-32\n", "9.0 -0.061445 170.115803 5.837214e-32\n", "10.0 0.034623 170.729855 1.958737e-31\n", "11.0 0.006351 170.750557 8.267052e-31\n", "12.0 -0.012882 170.835910 3.220232e-30\n", "13.0 -0.053959 172.336548 6.181195e-30\n", "14.0 -0.016606 172.478965 2.160214e-29\n", "15.0 0.051742 173.864488 4.089545e-29\n", "16.0 0.078917 177.094281 3.217935e-29\n", "17.0 -0.001834 177.096029 1.093167e-28\n", "18.0 -0.101604 182.471938 3.103822e-29\n", "19.0 -0.057342 184.187772 4.624065e-29\n", "20.0 0.026975 184.568286 1.235670e-28\n", "21.0 0.062359 186.605963 1.530258e-28\n", "22.0 -0.009400 186.652365 4.548193e-28\n", "23.0 -0.068037 189.088185 4.562009e-28\n", "24.0 -0.035566 189.755202 9.901091e-28\n", "25.0 0.095679 194.592623 3.354288e-28\n", "26.0 0.065650 196.874878 3.487621e-28\n", "27.0 -0.018404 197.054614 9.008745e-28\n", "28.0 -0.079244 200.394009 5.773711e-28\n", "29.0 0.008499 200.432502 1.541386e-27\n", "30.0 0.053372 201.953776 2.133191e-27\n", "31.0 0.074816 204.949395 1.550161e-27\n", "32.0 -0.071187 207.667243 1.262288e-27\n", "33.0 -0.088145 211.843156 5.480813e-28\n", "34.0 -0.025283 212.187450 1.215227e-27\n", "35.0 0.125690 220.714899 8.231607e-29\n", "36.0 0.142724 231.734119 1.923081e-30\n", "37.0 0.095768 236.706161 5.937778e-31\n", "38.0 -0.084744 240.607804 2.890885e-31\n", "39.0 -0.150126 252.878985 3.962997e-33\n", "40.0 -0.083767 256.707742 1.996170e-33\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " elif issubdtype(paramsdtype, complex):\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] } ], "source": [ "arma11 = sm.tsa.ARMA(arma_rvs, (1,1)).fit()\n", "resid = arma11.resid\n", "r,q,p = sm.tsa.acf(resid, qstat=True)\n", "data = np.c_[range(1,41), r[1:], q, p]\n", "table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n", "print(table.set_index('lag'))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " elif issubdtype(paramsdtype, complex):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " AC Q Prob(>Q)\n", "lag \n", "1.0 -0.007889 0.031303 0.859567\n", "2.0 0.004132 0.039907 0.980244\n", "3.0 0.018103 0.205418 0.976710\n", "4.0 -0.006760 0.228543 0.993948\n", "5.0 0.018120 0.395028 0.995465\n", "6.0 0.050688 1.700453 0.945086\n", "7.0 0.010252 1.753961 0.972196\n", "8.0 -0.011206 1.818023 0.986091\n", "9.0 0.020292 2.028522 0.991008\n", "10.0 0.001029 2.029064 0.996113\n", "11.0 -0.014035 2.130173 0.997984\n", "12.0 -0.023858 2.422929 0.998427\n", "13.0 -0.002108 2.425219 0.999339\n", "14.0 -0.018783 2.607431 0.999590\n", "15.0 0.011316 2.673700 0.999805\n", "16.0 0.042159 3.595422 0.999443\n", "17.0 0.007943 3.628208 0.999734\n", "18.0 -0.074311 6.503855 0.993686\n", "19.0 -0.023379 6.789067 0.995256\n", "20.0 0.002398 6.792073 0.997313\n", "21.0 0.000487 6.792198 0.998516\n", "22.0 0.017953 6.961435 0.999024\n", "23.0 -0.038576 7.744466 0.998744\n", "24.0 -0.029816 8.213249 0.998859\n", "25.0 0.077850 11.415821 0.990675\n", "26.0 0.040408 12.280445 0.989479\n", "27.0 -0.018612 12.464273 0.992262\n", "28.0 -0.014764 12.580183 0.994586\n", "29.0 0.017649 12.746187 0.996111\n", "30.0 -0.005486 12.762260 0.997504\n", "31.0 0.058256 14.578539 0.994614\n", "32.0 -0.040840 15.473078 0.993887\n", "33.0 -0.019493 15.677304 0.995393\n", "34.0 0.037269 16.425461 0.995214\n", "35.0 0.086212 20.437442 0.976296\n", "36.0 0.041271 21.358840 0.974774\n", "37.0 0.078704 24.716871 0.938949\n", "38.0 -0.029729 25.197048 0.944895\n", "39.0 -0.078397 28.543382 0.891179\n", "40.0 -0.014466 28.657573 0.909268\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] } ], "source": [ "arma41 = sm.tsa.ARMA(arma_rvs, (4,1)).fit()\n", "resid = arma41.resid\n", "r,q,p = sm.tsa.acf(resid, qstat=True)\n", "data = np.c_[range(1,41), r[1:], q, p]\n", "table = pd.DataFrame(data, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n", "print(table.set_index('lag'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: How good of in-sample prediction can you do for another series, say, CPI" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "macrodta = sm.datasets.macrodata.load_pandas().data\n", "macrodta.index = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))\n", "cpi = macrodta[\"cpi\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Hint: " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AZrQ594BuXbBGu4sr9OB5/TR5VJzTkdAAlGQAAIBm8m5Kru5ZkqL2rYO1aPoYjerJSXregpIMAADQDF5bvVu/ezNVI3tE6D9XD1dUW27S8yaUZAAAgCb2yoos/eHtjRqbGKUXpoxQaDDbvHkbSjIAAEATsdbq2S/S9eiH2zShX2c9fdVwCrKXoiQDAAA0gcoal37/1ga9uTZHFwzppscvH6KQIDYS81aUZAAAgEbKLi7XzEXrtHZXie46M1G/Ht+bo6a9HCUZAADgOBSXVeu9DXlauj5XqzL3KTQ4QM9cPVznDurqdDQ0AUoyAADAMUjZXaKXv8vS0tRcVde6ldC5je4+M1EXD+uu2Igwp+OhiVCSAQAAGqC61q27l6RoaUquwkMCdUVSrCaPilO/rm1ZWuGDKMkAAABHUVXr0m0L1mr5lgLdcUaCbjy1p9qGBjsdC82IkgwAAPALKmtcmvHKGn25vVAPXzRAU06MdzoSWgAlGQAA4GfsPVilm19ZozW7ivXoZYM1aWSs05HQQijJAAAAR7Al74BunJesorIqPT15uM4bzK4V/oSSDAAAUE+ty6031+boT0s3qW1okJbcdJIGxbR3OhZaGCUZAABAh27Oe2NNjp79Ik2791VoeFwHPXvNCEW3C3U6GhxASQYAAH6vssalKS+t1OrMYg2Jaa+Hzh+gM/p1Zms3P0ZJBgAAfq3W5dbtC9cpOatYj18+RJcO7045BiUZAAD4L2utHnhro5ZvydfDFw3QZSNinI4ED0FJBgAAPq+yxqVNuQe0a1+ZsorKlX+gSvsrqpW3v1LrdpXo1+N7s/8xfoSSDAAAfNq36Xt1z5JU5ZRUSJKMkTqFh6hDWIjatw7WbyYk6I4zEhxOCU9DSQYAAD6pssalf320TS99vVM9I8P1zNXDlRjdVjEdWys0ONDpePBwRy3JxpjZks6XVGCtHVg3tlhSn7pLOkgqsdYONcbES9oiaVvdayustTc3dWgAAICfY63VBxv36J8fblVWUbmmjOmh+8/tq7AQ5gbRcA35r2WupKclvfz9gLX2iu8fG2Mel7S/3vXp1tqhTRUQAACgoTbm7Ncf39motbtKlBjdRvNvGK1TEiKdjgUvdNSSbK39sm6G+CfMof1RJkka37SxAAAAjk1aQakmv7hCocGBeuTSQZo4IkZBgQFOx4KXauzPHU6VlG+t3VFvrKcxZp2kA5IetNZ+1civAQAA8IuKDlZp2tzVahUUoLduPUkxHcOcjgQv19iSPFnSonrP8yTFWWuLjDEjJL1tjBlgrT1w+BuNMTMkzZCkuLi4RsYAAAD+qrLGpZteWaOCA1V6dcYYCjKaxHH/DMIYEyTpUkmLvx+z1lZZa4vqHq+RlC4p8Ujvt9a+YK1NstYmRUVFHW8MAADgxyqqXZq56NBpebMmDdWwuI5OR4KPaMxM8gRJW6212d8PGGOiJO2z1rqMMb0kJUjKaGRGAACAn9izv1LTX07Wxtz9euiC/jpvcFenI8GHNGQLuEWSTpcUaYzJlvSQtfYlSVfqx0stJGmspL8YY2okuSXdbK3d17SRAQCAv0vNLtGN85JVVlWrF6ckaUL/aKcjwcc0ZHeLyT8zPvUIY29IeqPxsQAAAI5s975yXTt7lcJDgvTGrSepb5d2TkeCD2JXbQAA4DXKq2s1/eVkud1WC6ePVo9O4U5Hgo+iJAMAAK9grdW9r6dqe36p5kwbRUFGs2KHbQAA4BWe/SJd76Xm6d6z++q0RHbGQvOiJAMAAI/39rocPfrhNl0wpJtuGtvL6TjwA5RkAADg0b7YXqjfLknRib066bHLB8sY43Qk+AFKMgAA8Fgpu0t0y/w1Sohuq+evHaFWQYFOR4KfoCQDAACP9Nm2Al3z35WKCA/RvGkj1S402OlI8COUZAAA4FGstfrvVxm6Ye5qxUaE6bWbTlTndqFOx4KfYQs4AADgMVxuqwff3qhFq3bpnIFd9PikIQoLoa6g5fFfHQAA8Ai1LrfuXpKid9bn6tbTT9Bvf9VHAQHcpAdnUJIBAIDjqmvduuPVdfpg4x7dc1Yf3Taut9OR4OcoyQAAwFHb9pTqoXc3akXGPv3h/P664ZSeTkcCKMkAAMAZBaWVemLZdi1evVttWgXp0YmDNSkp1ulYgCRKMgAAcMCKjCLdMn+NDlbVaupJPfXr8b3VMTzE6VjADyjJAACgRS1atUt/eHuj4jqFacnNJ6l35zZORwJ+gpIMAABaREl5tR79aJsWrtylsYlRemryMLVvzQEh8EyUZAAA0Kyqal16+dssPfXpDh2sqtX0U3vqd2f3VVAgZ5rBc1GSAQBAs6iscWlJ8m4990WGckoqdFpilO4/t6/6dmnndDTgqCjJAACgSbndVvNXZunpT9NUUFql4XEd9Mhlg3RqQpTT0YAGoyQDAIAmk11crt8uSdGKjH0a0ytC/3flUJ3Yq5OM4eQ8eBdKMgAAOG41LrdyiiuUWVSmLXmleuazNLmt1aMTB+vyETGUY3gtSjIAADgmtS63vk0v0rspufpo0x6VVtb+8NqonhF6/PIhio0IczAh0HiUZAAA0GCp2SW6feE67dpXrratgvSrAV00uleE4juFq0enMHVu24rZY/gESjIAADgqa63mr8jSw+9tUWSbED179XCN69tZocGBTkcDmgUlGQAA/KzqWre+TivUwpW7tHxLgcb1idKsSUM5Qho+j5IMAAB+xOW2WrmzSEtTcvXBxj0qKa9R+9bB+t3ZfXXT2F4KCGA5BXwfJRkAAEg6NGv85Cc79FrybhWUViksJFC/6h+tC4Z006kJUQoJ4oQ8+A9KMgAA0L6yat0yf41W7tynM/tH6+Kh3TW+b2e1DmHNMfwTJRkAAD+3Pb9UN8xbrfwDVfq/K4bq4mHdnY4EOI6SDACAH/t0a75mLlqv1iGBWjxjjIbFdXQ6EuARKMkAAPgha61e/CpD//hgq/p3bacXr01Stw6tnY4FeAxKMgAAfmZ/RY3+vHST3lybo3MGdtHjk4YoLIRKANTH3wgAAPyEtVbvpebpL+9tVtHBKs08I0G/OSOBLd2AI6AkAwDgB8qqajVz0Tp9srVAg7q31+zrRmpQTHunYwEei5IMAICPO1BZo2lzVmv97hI9eF4/TTu5pwKZPQZ+ESUZAAAfVlJerWtnr9KWvAP6z1XDdPbArk5HArwCJRkAAB+VW1Kh6+euVkZhmZ67ZoTO6BftdCTAa1CSAQDwQet2FWv6y2tUWePSS1OTdGpClNORAK9CSQYAwMe8uTZb9725QV3ahWrR9NFKiG7rdCTA61CSAQDwEbklFXro3U1atjlfY3pF6NmrR6hjeIjTsQCvREkGAMDLVde6Ne/bTD2xfLusle4/p6+uP6WnggMDnI4GeC1KMgAAXspaqw827tE/P9yqrKJyje/bWX++cIBiI8KcjgZ4PUoyAABeqLC0SrctWKtVmfuUGN1Gc6eN1GmJUTKG/Y+BpkBJBgDAy+zcW6brZq9SYWmV/n7JIE1KilEQSyuAJnXUv1HGmNnGmAJjzMZ6Y38yxuQYY9bXfZxb77X7jTFpxphtxpizmis4AAD+KDW7RBOf/VallTVaOH20rhodR0EGmkFD/lbNlXT2EcafsNYOrft4X5KMMf0lXSlpQN17njHGBDZVWAAA/Nnb63J0xfMr1DokUG/ccpKGxXV0OhLgs4663MJa+6UxJr6Bn+8iSa9aa6sk7TTGpEkaJem7404IAICfq6xx6S/vbdbClbs0Kj5CT181TJ3bhTodC/Bpjfn5zO3GmNS65Rjf/69sd0m7612TXTf2E8aYGcaYZGNMcmFhYSNiAADgu9IKDuqyZ7/VwpW7dPNpJ2jh9NEUZKAFHG9JflbSCZKGSsqT9PixfgJr7QvW2iRrbVJUFEdlAgBQn9ttNeebnTrvya+UW1Kh/16bpPvO6cv6Y6CFHNfuFtba/O8fG2NelPRe3dMcSbH1Lo2pGwMAAA1UdLBKd7y6Xl+n7dX4vp31yGWD1Lkts8dASzqukmyM6Wqtzat7eomk73e+eFfSQmPMLEndJCVIWtXolAAA+Ilte0p1w7zVP2zvNnlULHsfAw44akk2xiySdLqkSGNMtqSHJJ1ujBkqyUrKlHSTJFlrNxljXpO0WVKtpNusta7miQ4AgG9Zvjlfd7y6TuGtgvTaTSdqSGwHpyMBfstYa53OoKSkJJucnOx0DAAAHFFSXq1HPtiqV1fv1qDu7fXitUnq0p7lFUBzMMassdYmHe06TtwDAMAhbrfV2+tz9Lf/bVFJRY1mjO2lOyckqnUIRwwATqMkAwDQwmpdbi1NzdV/PktXWsFBDY3toFcuGaT+3do5HQ1AHUoyAAAtaN2uYt31Wop27i1Tn+i2enLyMJ03qKsCA7g5D/AklGQAAFqAtVZzv83U39/fouh2oXp+ygid2S9aAZRjwCNRkgEAaGa795Xr7+9v0Qcb92hCv856/PKhah8W7HQsAL+AkgwAQDPJKDyoZz5P11vrchRojO4/p69mjO3FvseAF6AkAwDQxGpcbj31aZr+81maggKMrj2xh2aM7aWu7Vs7HQ1AA1GSAQBoQmkFpbpzcYo25OzXpcO66/5z+ymqbSunYwE4RpRkAACayGurd+sP72xUWEignr16uM4Z1NXpSACOEyUZAIBGqqh26Q/vbNTra7J1cu9OeuKKoerclhPzAG9GSQYAoBF2FZVrxivJ2pZfqplnJOiOMxLY8xjwAZRkAACO03fpRbplwRpJ0txpo3RaYpTDiQA0FUoyAADHYeHKXfrjOxsVHxmu/16bpPjIcKcjAWhClGQAAI5BZY1LD72zSYuTd+v0PlF6cvIwtQvlYBDA11CSAQBooJ17y3TL/DXauqdUt407QXdOSFRQYIDTsQA0A0oyAAAN8P6GPN37eqqCAo3mTBupcX06Ox0JQDOiJAMA8Auqa936+/tbNPfbTA2N7aD/XD1c3Ttwch7g6yjJAAD8jOzict22cJ1Sdpfo+pN76r5z+iokiOUVgD+gJAMAcASfbs3XXa+lyOWynJ4H+CFKMgAA9dS63Jq1bLue+Txd/bq207NXD2d7N8APUZIBAKiTUXhQd76WopTdJbpyZKz+dOEAhQYHOh0LgAMoyQAAv2et1fwVWfrb+1vUKihQT181TOcP7uZ0LAAOoiQDAPza/ooa3bMkRR9vztfYxCj9a+JgRbcLdToWAIdRkgEAfmtD9n7dunCN8koq9eB5/XTDKT1ljHE6FgAPQEkGAPiVtIKD+nxbgb7csVffpe9VZJtWWnzTGI3oEeF0NAAehJIMAPAL1lrNWrZdT32aJknqFRWuKWPidfv43ooID3E4HQBPQ0kGAPi8WpdbD7y1UYuTd+vyETG6Y0KCYjqGOR0LgAejJAMAfFpljUu3LVirT7YWaOb43rrzzETWHQM4KkoyAMCnzfs2U59sLdDDFw3QlBPjnY4DwEtwAD0AwGeVV9fq+S8zNDYxioIM4JhQkgEAPuuV77K0r6xad5yR4HQUAF6GkgwA8Enl1bV64csMnZoQqRE9OjodB4CXoSQDAHzS/BVZKiqr1m8mMIsM4NhRkgEAPqe8ulbPf/H9LDKHhAA4dpRkAIBPcbmtfrskhVlkAI1CSQYA+Axrrf68dJPe37BHD57Xj1lkAMeNkgwA8Bn/+SxNL3+XpRlje+nGU3s5HQeAF6MkAwB8wpxvduqxj7fr4qHddN/ZfZ2OA8DLceIeAMDrPf9Fuv7xwVb9qn+0Hp04RAEBHDsNoHEoyQAAr2Wt1VOfpmnWsu26YEg3zZo0RMGB/JAUQONRkgEAXuvV1bs1a9l2XTY8Ro9OHKxAZpABNBFKMgDAK2UVlenh9zbr5N6d9K+Jg1liAaBJ8TMpAIDXcbmt7n4tRYEBRv9iDTKAZnDUkmyMmW2MKTDGbKw39i9jzFZjTKox5i1jTIe68XhjTIUxZn3dx3PNGR4A4J9e/CpDyVnF+stFA9StQ2un4wDwQQ2ZSZ4r6ezDxpZJGmitHSxpu6T7672Wbq0dWvdxc9PEBADgkI827dGsj7frnIFddPHQ7k7HAeCjjrom2Vr7pTEm/rCxj+s9XSFpYtPGAgDgx3JLKvR5D2eTAAAgAElEQVSndzfp48356tulrf52ySAZwzILAM2jKW7cu17S4nrPexpj1kk6IOlBa+1XR3qTMWaGpBmSFBcX1wQxAAC+6uNNe3Tn4vVyWav7zumrG07pyVZvAJpVo0qyMeYBSbWSFtQN5UmKs9YWGWNGSHrbGDPAWnvg8Pdaa1+Q9IIkJSUl2cbkAAD4rvdSc/WbV9drQPf2enryMMVGhDkdCYAfOO6SbIyZKul8SWdYa60kWWurJFXVPV5jjEmXlCgpufFRAQD+5s212frtkhQl9YjQ7Gkj1aYVO5cCaBnH9bMqY8zZku6VdKG1trzeeJQxJrDucS9JCZIymiIoAMC/fLAhT3cvSdGYXp0093oKMoCWddTvOMaYRZJOlxRpjMmW9JAO7WbRStKyupsmVtTtZDFW0l+MMTWS3JJuttbua6bsAAAflVF4UPe8nqohMR00e+pIhQYHOh0JgJ9pyO4Wk48w/NLPXPuGpDcaGwoA4L8qa1y6dcFaBQUa/efq4RRkAI7gZ1cAAI/yx3c2auueUs2ZNlLdOSgEgEMoyQAAj2Ct1TOfp+u15GzdPq63xvXp7HQkAH6MkgwAcJzbbfXw/zZrzjeZumBIN915ZqLTkQD4OUoyAMBRVbUu3f1ait5LzdP1J/fUg+f1U0AAJ+kBcBYlGQDgmMoal256ZY2+2F6o35/bV9NP7cVR0wA8AiUZAOCI8upa3TgvWd9lFOmRSwfpylFxTkcCgB9QkgEALa60skbXz12tNVnFmjVpiC4ZFuN0JAD4EUoyAKBFFZRWatqc1dq2p1RPTh6m8wd3czoSAPwEJRkA0GJ27i3TtbNXam9ptV68Lolt3gB4LEoyAKBFrNtVrBvnJctKWjRjjIbGdnA6EgD8LEoyAKDZvb0uR/e+kaou7UI17/pR6hkZ7nQkAPhFlGQAQLNxu60e+3ibnvk8XaN7RujZa0YoIjzE6VgAcFSUZABAs8gtqdBvl6To2/QiTR4Vqz9fOFAhQQFOxwKABqEkAwCalLVWb6/P0R/f2SSX2+oflw7SlSNjOSQEgFehJAMAmkxVrUsPvrVRS9ZkK6lHRz0+aYh6dGL9MQDvQ0kGADSJvQerdMv8NVqdWayZ43vrjgmJCgxg9hiAd6IkAwAabVPufs14eY32HqzSU5OH6YIhHBACwLtRkgEAx81aq8Wrd+uP725Sx7BgLbn5RA2OYf9jAN6PkgwAOC4V1S498PYGvbk2R6f0jtT/XTlUkW1aOR0LAJoEJRkAcMyKDlbp+nnJSs0u0R1nJGjmGQmsPwbgUyjJAIBjsntfua6dvUq5JRV67poROmtAF6cjAUCToyQDABpsY85+TZu7WlU1Ls2/cbRGxkc4HQkAmgVHHwEAGuSDDXm6/LnvFBxg9PotJ1GQAfg0ZpIBAL/IWqunPk3TrGXbNSyug56fMkKd24Y6HQsAmhUlGQD8WG5JhXYUHNRpiVE/ec1aq8+3FerJT3do3a4SXTKsu/5x6SCFBgc6kBQAWhYlGQD8VFrBQV314goVlFbpuhN76A/n91dQYICstfpsW4Ee/3i7NuUeUPcOrfWPSwfpypGxMoYdLAD4B0oyAPihrXsO6Jr/rpRkNHlUrOZ9l6X0wjLdNq63nvp0h75NL1KPTmF6dOJgXTy0u0KCuIUFgH+hJAOAn1mRUaSb569RaFCgFkwfrROi2mhYXEc98NYGfZ22Vx3DgvXnCwfoqtFxCg6kHAPwT5RkAPATO/eW6Z8fbNWHm/YoNqK15t8wWj06hUuSJiXFqnfnNlqRUaRrxvRQu9Bgh9MCgLMoyQDg43bvK9ezX6TrtdW71SooQHefmagbT+2l1iE/vgFveFxHDY/r6FBKAPAslGQA8FE5JRWa9fF2vb0+R4HG6MpRsZp5RgLbtwFAA1CSAcAHbczZr6lzVulgVa2uPbGHbhp7grq0pxwDQENRkgHAx3y9Y69ueiVZHcJC9OqMMerdua3TkQDA61CSAcBH7Cur1qJVu/R/y7frhKg2mnf9KEW3Y/YYAI4HJRkAvFxaQame+Txd76XmqbrWrfF9O+uJK4aqfWt2qACA40VJBgAv5XZbzf5mpx79cJtCggJ05chYXTOmhxKjWV4BAI1FSQYAL7R7X7nueT1FKzL26cz+0frHpYMU2aaV07EAwGdQkgHAi+yvqNEzn6VpzjeZCgkK0KMTB+vyETEyxjgdDQB8CiUZALzEG2uy9fD/Nmt/RY0uHRaj356VqK7tWzsdCwB8EiUZADycy231j/e36L9f79So+Ag9dGF/DejW3ulYAODTKMkA4MEOVNZo5qJ1+nxboaaeFK8Hz+unoMAAp2MBgM+jJAOAh9qzv1JT56xSWsFB/f2SQbpqdJzTkQDAb1CSAcADpRWU6tqXVulAZa3mThulUxIinY4EAH6lQT+zM8bMNsYUGGM21huLMMYsM8bsqPu1Y924McY8aYxJM8akGmOGN1d4APBFq3bu02XPfqdql9WrM8ZQkAHAAQ1d2DZX0tmHjd0n6RNrbYKkT+qeS9I5khLqPmZIerbxMQHA91XXuvWvj7bqyhe+U0R4iN685SQN7M4NegDghAYtt7DWfmmMiT9s+CJJp9c9nifpc0m/qxt/2VprJa0wxnQwxnS11uY1RWAA8EXb80t15+L12pR7QJePiNEfL+ivtqEcKw0ATmnMmuToesV3j6TousfdJe2ud1123diPSrIxZoYOzTQrLo6bUQD4px+Olv5om9q2CtILU0boVwO6OB0LAPxek9y4Z621xhh7jO95QdILkpSUlHRM7wUAX5BdXK57lqTqu4wiTegXrUcu42hpAPAUjSnJ+d8vozDGdJVUUDeeIym23nUxdWMAAEm1LrfmfJOpJ5Zvl5H06GWDdXkSR0sDgCdpTEl+V9J1kh6p+/WdeuO3G2NelTRa0n7WIwPwR9ZarcjYpwUrs5R/oFKxEWGKiwjTx5vytTnvgMb37ay/XDRAMR3DnI4KADhMg0qyMWaRDt2kF2mMyZb0kA6V49eMMTdIypI0qe7y9yWdKylNUrmkaU2cGQA83qdb8/WP97dqR8FBdQgLVmJ0W32XXqQ31+Youl0rPXv1cJ09sAuzxwDgoRq6u8Xkn3npjCNcayXd1phQAODNCkorNXPRekW3a6V/TRysC4Z0U2hwoCSpssaloADD0dIA4OE4cQ8AmthjH21TVa1LL16bpF5RbX702vdlGQDg2ZjKAIAmlJpdoiVrsjXt5J4/KcgAAO9BSQaAJmKt1Z/e3aRO4SH69fjeTscBADQCJRkAmoC1VotX79baXSW696y+nJYHAF6ONckA0AgV1S4tTcnVyysytTHngIbEdtDEETFOxwIANBIlGQCO03fpRfrtkhTllFQoMbqNHr5ogC4ZHqOAALZ1AwBvR0kGgGNUWePSYx9t00vf7FR8p3AtuHG0TjqhE3seA4APoSQDwDGornVryksrtTqzWFPG9ND95/ZVWAjfSgHA1/CdHQCOwSMfbNXqzGI9ccUQXTKMtccA4KvY3QIAGujDjXma/c1OTT0pnoIMAD6OkgwADZC5t0z3LEnVkNgO+v25/ZyOAwBoZpRkADgKl9vqjlfXKSDA6D9XDVNIEN86AcDXsSYZAI7i1dW7lJK9X/++cqhiOoY5HQcA0AKYDgGAX1B0sEqPfrhNY3pF6MIh3ZyOAwBoIZRkAPgFj364TWVVtfrLRQPZBxkA/AglGQB+xtpdxVqcvFvXn9JTidFtnY4DAGhBlGQAOILKGpd+/+YGRbdrpZlnJDgdBwDQwrhxDwCO4KF3NmnrnlLNmTZSbVrxrRIA/A0zyQBwmNfXZGtx8m7dNu4EjevT2ek4AAAHUJIBoJ6tew7owbc3aEyvCN05IdHpOAAAh1CSAaBOrcutOxatV9vQYD05eZiCAvkWCQD+ioV2AFBnwcpd2pZfqueuGaHObUOdjgMAcBDTJAAgqbisWrOWbdfJvTvprAHRTscBADiMkgwAkp5Yvl2llTX64/kDODQEAEBJBoCtew5o/oosXTOmh/p04dAQAAAlGYCfc7ut/vTuJrUNDWY3CwDADyjJAPzay99lakXGPt13Tl91DA9xOg4AwENQkgH4rbSCg/rHB1s1rk+UrhwZ63QcAIAHoSQD8Eu1Lrfufm29WocE6p+XDeZmPQDAj7BPMgC/9PRnaUrJ3q//XDVcnduxJzIA4MeYSQbgd5Ztzte/P9mhS4Z113mDuzodBwDggSjJAPzKxpz9mrlonQZ3b6+/XzLI6TgAAA9FSQbgN/bsr9QN81YrIjxEL16XpNYhgU5HAgB4KEoyAL9grdWtC9boYGWt/ntdkjq3ZR0yAODnUZIB+IVlm/O1dleJHrpggPp1bed0HACAh6MkA/B5brfVE8t3KL5TmC4d3t3pOAAAL0BJBuDzPtq0R1vyDuiOCQkKCuTbHgDg6PjXAoBPc7ut/m/5DvWKCteFQ5hFBgA0DCUZgE97f2OetuWX6jcTEhUYwKl6AICGoSQD8FmuulnkhM5tdN4gDg0BADQcJRmAz3ovNVdpBQeZRQYAHDNKMgCfVOty69/Ld6hvl7Y6Z2AXp+MAALwMJRmAT3pnfa4y9pbpNxMSFcAsMgDgGAUd7xuNMX0kLa431EvSHyV1kDRdUmHd+O+tte8fd0IAOEa1Lree/HSH+ndtp7MGRDsdBwDghY67JFtrt0kaKknGmEBJOZLekjRN0hPW2seaJCEAHKM31+Uoq6hcL16bJGOYRQYAHLumWm5xhqR0a21WE30+ADguZVW1+vfyHRrUvb0m9OvsdBwAgJdqqpJ8paRF9Z7fboxJNcbMNsZ0PNIbjDEzjDHJxpjkwsLCI10CAMfs0Q+3Knd/hR66oD+zyACA49bokmyMCZF0oaQldUPPSjpBh5Zi5El6/Ejvs9a+YK1NstYmRUVFNTYGAGhlRpHmfZelqSfFKyk+wuk4AAAv1hQzyedIWmutzZcka22+tdZlrXVLelHSqCb4GgDwiyqqXfrdG6mKjWite87q43QcAICXa4qSPFn1lloYY+ofa3WJpI1N8DUA4BfNWrZNmUXl+uelgxUWctz3JAMAIKkRu1tIkjEmXNKZkm6qN/yoMWaoJCsp87DXAKDJbc49oJe+3qnJo+J0Uu9Ip+MAAHxAo0qytbZMUqfDxqY0KhEAHAO32+qP72xUh7AQ3Xd2X6fjAAB8BCfuAfBqb6zNVnJWse47p6/ahwU7HQcA4CMoyQC81v7yGj3ywVYNj+ugicNjnI4DAPAh3N0CwGs9vmybisur9fINoxQQwJ7IAICmw0wyAK+0IqNIr6zI0rUnxmtAt/ZOxwEA+BhKMgCvU1pZo98uSVFcRJjuPZs9kQEATY/lFgC8zl/f26LckgotuflE9kQGADQLZpIBeJVlm/O1OHm3bj7tBI3owdHTAIDmQUkG4DU25uzXPa+nqF/XdvrNhESn4wAAfBglGYBXWL+7RFe9uEJhwYF67prhCgni2xcAoPmwmA+Ax0vO3Kepc1YrIjxEC24crdiIMKcjAQB8HCUZgEfbkL1f181epeh2oVowfbS6tm/tdCQAgB+gJAPwWJl7yzR1zip1CAvRwulj1KV9qNORAAB+gkV9ADxSQWmlrp29Sm5r9fINoyjIAIAWxUwyAI9TWlmjaXNWq7C0Sgunj9YJUW2cjgQA8DPMJAPwKFW1Lt30yhpt21OqZ64ZrmFxHZ2OBADwQ8wkA/AYbrfVXa+l6Nv0Ij1++RCN69PZ6UgAAD/FTDIAj+B2W/1p6Sb9LzVP95/TV5eNiHE6EgDAjzGTDMBx5dW1umtxij7ctEfTT+2pGWN7OR0JAODnKMkAHJVbUqEb5yVr654DevC8frrhlJ4yxjgdCwDg5yjJABzzbfpezVy0TlU1br00dSRrkAEAHoOSDKDFud1W//ksTU8s366ekeF6bvoIJUS3dToWAAA/oCQDaFFpBQf156Wb9NWOvbpoaDf9/ZJBCm/FtyIAgGfhXyYALWLvwSr9e/kOLVy1S62DA/W3SwbqqlFxrD8GAHgkSjKAZlVR7dLsb3bq2c/TVVHj0tWj4zTzjARFtmnldDQAAH4WJRlAs3lnfY4e+WCr8vZX6sz+0brvnL4cMQ0A8AqUZABNzu22euTDrXrhywwNjmmv/7tiqEb36uR0LAAAGoySDKBJVde6de/rKXp7fa6uPbGHHrpggAIDWHcMAPAulGQATSarqEy/f2uDvkkr0j1n9dGtp5/AjXkAAK9ESQbQaMVl1Xrq0zS9siJTwYEBenTiYE1KinU6FgAAx42SDOC4ud1Wi5N365EPtqq0skZXjIzVnRMS1bldqNPRAABoFEoygOOybU+pHnhrg5KzijW6Z4T+ctFA9enCqXkAAN9ASQZwTCqqXXry0x168csMtQ0N0mOXD9Flw7uz9hgA4FMoyQAapKyqVl/t2Ku/vb9Zu/dV6PIRMbr/3H6KCA9xOhoAAE2OkgzgZ+UfqNTTn6Zp1c592lFQKreVTogK16szxmgM+x4DAHwYJRnAT7jcVvNXZOlfH21Ttcutk07opLMGdtHQ2PY6uXekWgUFOh0RAIBmRUkG8CMbc/brgbc2KCV7v05NiNRfLx6oHp3CnY4FAECLoiQDkHRozfGsZds155udighvpX9fOVQXDunGDXkAAL9ESQagr3fs1b2vpyh3f6WuGh2n353VV+3Dgp2OBQCAYyjJgB+rqHbpnx9u1dxvM3VCVLjeuOVEjegR4XQsAAAcR0kG/JDLbfXBxjzN+ni7MvaWaepJ8brvnL4KDeaGPAAAJEoy4FestXp7fY6e+jRNGYVlOiEqXPNvGK1TEiKdjgYAgEehJAN+otbl1h/e2aRFq3apX9d2eubq4TprQBcFBnBjHgAAh6MkA36gssalXy9ap2Wb83Xr6SfonrP6sGsFAAC/oNEl2RiTKalUkktSrbU2yRgTIWmxpHhJmZImWWuLG/u1ABy7sqpaTZ2zSslZxfrzhQN03UnxTkcCAMDjBTTR5xlnrR1qrU2qe36fpE+stQmSPql7DqCFudxWMxet09pdJXpq8jAKMgAADdRUJflwF0maV/d4nqSLm+nrAPgFf/vfFn2ytUB/uqC/zh/czek4AAB4jaYoyVbSx8aYNcaYGXVj0dbavLrHeyRFH/4mY8wMY0yyMSa5sLCwCWIAqO+V7zI1+5udmnZyvKacGO90HAAAvEpT3Lh3irU2xxjTWdIyY8zW+i9aa60xxh7+JmvtC5JekKSkpKSfvA7g+L2yIkt/WrpZ4/t21oPn9Xc6DgAAXqfRJdlam1P3a4Ex5i1JoyTlG2O6WmvzjDFdJRU09usAOLoal1t/WbpZr6zI0vi+nfXU5GFs8QYAwHFo1HILY0y4Mabt948l/UrSRknvSrqu7rLrJL3TmK8D4Oj2V9Ro2pzVemVFlmaM7aUXr01SeCt2eQQA4Hg09l/QaElv1e23GiRpobX2Q2PMakmvGWNukJQlaVIjvw6AX7Bnf6Wmzlml9MKDenTiYE1KinU6EgAAXq1RJdlamyFpyBHGiySd0ZjPDaBh0gpKdd3s1Sopr9acqaM4YhoAgCbAz2IBL7Yyo0g3zV+joIAALb7pRA3s3t7pSAAA+ITm2icZQDN7ddUuXf3flYoID9Ebt1CQAQBoSswkA17G5bb62/+2aPY3O3VqQqSevmq42rcOdjoWAAA+hZIMeJFal1t3L0nRO+tzNfWkeD14Xj8FBfIDIQAAmholGfAS1bVu3fHqOn2wcY/uPbuPbj29t9ORAADwWZRkwAtk7i3Tw+9t1idbC/SH8/vrhlN6Oh0JAACfRkkGPFRljUsLVu7SO+tzlJq9XwFGevjigZoypofT0QAA8HmUZMADbcjerztfW6+0goMa1L29fn9uX50/uJu6dWjtdDQAAPwCJRnwIKWVNZrzTaae/GSHOrUJ0cvXj9LYxCinYwEA4HcoyYDDSitr9OX2vVqakqtPtxWoutatC4Z008MXDVCHsBCn4wEA4JcoyUALO1BZow3Z+7Umq1hf7SjU2l0lcrmtItu00lWj4nTh0G4aHtfR6ZgAAPg1SjLQjCprXNqUe0Apu0uUml2i1Oz9ythb9sPrA7u3001je2lsYpRGxkcoMMA4mBYAAHyPkgw0kVqXW9vzDyol+1AhTtm9X9vzS1XrtpKk6HatNDimgy4d3l2DYzpocEx7llMAAOChKMlAI1TWuPTZ1gK9m5Krz7cVqqLGJUlq3zpYg2Pa66a+vTQ4poOGxHRQl/ahDqcFAAANRUkGGshaq3W7S/TFtkJlFpUps6hcafmlKqt2KbJNK00cEaOk+I4aEtNBPTqFyRiWTgAA4K0oycAvsNZqS16plqbmamlKrrKLKxRgpG4dWqtHpzBdNiJGv+rfRWN6RSgoMMDpuAAAoIlQkoHDuN1Wm/MO6JMtBXo3JUfphWUKDDA6pXek7pyQqDMHRKtdaLDTMQEAQDOiJAM6VIy/Tturt9bl6Ksdhdp7sFrGSKPiIzTt5J46d1BXRYRzkx0AAP6Ckgy/VlJerdfXZGv+iixlFpWrY1iwxiZGaWxClE5NjFTnttxsBwCAP6Ikwy9tyN6vV1Zk6p31uaqqdWtkfEfdeWaizh7YRa2CAp2OBwAAHEZJht/IKirT0pRcLU3J07b8UoWFBOqyETG6ZnQP9e/Wzul4AADAg1CS4dPyD1QeKsapeUrZXSJJSurRUQ9fNEAXDevODXgAAOCIKMnwOS631efbCvTKiix9sb1Q1h46/vn+c/rq/CHd1L1Da6cjAgAAD0dJhk+orHEpObNYX+0o1P825Cm7uEKd27bSr8f11kXDuuuEqDZORwQAAF6EkgyvZK1VeuFBfbl9r77cUagVGUWqrHErONBoTK9O+v25/XRm/2gFc8AHAAA4DpRkeJX0woN6d32ulqbmKqOwTJLUKzJcV46M09jESI3u2UnhrfjPGgAANA5tAh7L5bZKyS7Ryox9StldotTsEuXur5Qx0uiehw75OD0xSrERYU5HBQAAPoaSDI9RWePS5rwDSt1dotWZxfo6ba/2V9RIknp0CtOI+P/X3r3HyHndZRz/Pjuzu7P39a7Xjh3f0xgR1c6FKASFJIWKqqRSS0EQ2lIqimQgQUqgEioVEqioiKASLhKiSkmsIiUpqIkEEaFpClXd0hKRpLZjx21uJLETx5es9+K9zs78+OM9doaJd+P1OjOzs89HerVnzztnfGYfv7u/PXN2ZoBdm/r5+R3rWNvrN/kwMzOzd4+LZKu5iGB4YpaX35zk+WPj7Dsyyv4jI/zojXHmygHAJb0FPnDFWm7aPsQN71ntt4Q2MzOzmnKRbO+ak6dn+O7zJ9nz3AmeOz7OdLHMzFyJkYki4zNzZ2/XW8izc0M/u27axs4N/Vy5sY9LegtIquPszczMbCVzkWxLFhH86Ng4e547wYHXxnh9ZCo7RqcBGOhqY8elfXS15yjkc/R2tLJpoJPNg51sG+pm80AnLS0uiM3MzKxxuEi28zI6VeTE+DSnZ0pMzsxxbHyaF49P8MLx0/zg8CmOjc0AsGFVBxtWdXD9ZYNcNtTNjZev5r3r+1wEm5mZ2bLiInkFmy6WGJsuMj49x/DELEdHp3ljdIqTp2cZmZxlZLLIsbFpXhmeZGSy+LbxuRaxebCTa7cMcPPlQ9y4fTXr+vxudmZmZrb8uUhe5iKCU5NF/vfkBG+enmGqWGJqtsTJ0zO8OjzJq8OTjE3NUY4gAmbmSoxPzzE+PcdsqXzO+yy0ttDf0UZ/Zyuru9v50I51bBnsYm1fge72HJ1teQa72tg82EVb3m/WYWZmZs3HRXIDKJbKHBub5vWRaSZm5giCchlGpoq8MTrF0dFpRqeKZ//wbbpYYrpYZrpY4tjYNGPTc+e836GedjYNdLK+v0CLhATt+Rw9hTw9hVZ6Cnl6U3tVVxvr+wpc0legp9Ba46+AmZmZWWNxkbwEs3Nljpya5JXhSYZPzzI2XTy7atuaE/lcCzPFMuNpS8P4TPHsKu7EzNzZVd9Tk7OkVz47p4GuNvo7WmlvzVFobaGQz7G6O0+hNcd1WwfYurqLrau7WNNToKMtR2dbjlWdbXS05Wr3xTAzMzNrIi6SeWvLwuHhSQ6fmuS1U1NMzpYolYNiucybp2d5fSRb0Z2aLVGOoJxe63eh4vaMjtYzq7dvreCu68sK2o7WHINdbazv72Bdfwe9hfzZVd/eQiuX9BUotLrYNTMzM6ulFVskz8yVuP3+pzk8PMWRU5NMzJbedpsWQb6lhYGuNtb3F7hifS/dbXlaWgDEUHcbmwa72DzYyZqednoLrXQX8uQkiuUyc6WgLd9Ca877ds3MzMyWkxVbJLfncwxPzLJxoJOfumyQjQOdbFzVwYZVnWwY6KCnPb+kN7Nob8nRvmK/umZmZmbL24ou4x6+7YZ6T8HMzMzMGpD3AZiZmZmZVXGRbGZmZmZWxUWymZmZmVmVCy6SJW2U9C1Jz0o6KOmO1P8nkl6TtDcdt1y86ZqZmZmZvfuW8od7c8BnIuJpST3AU5IeT+f+KiK+uPTpmZmZmZnV3gUXyRFxFDia2uOSDgGXXqyJmZmZmZnVy0XZkyxpC3A18ETq+l1J+yXdJ2nVPGN2SXpS0pMnTpy4GNMwMzMzM7sollwkS+oGHgLujIgx4O+By4CryFaa//Jc4yLinoi4NiKuHRoaWuo0zMzMzMwumiUVyZJayQrk+yPiYYCIOBYRpYgoA18Grlv6NM3MzMzMamcpr24h4F7gUETcXdG/ruJmHwUOXPj0zMzMzMxqbymvbnED8EngGUl7U9/ngI9JugoI4GXgt5Y0QzMzMzOzGlvKq1t8F9A5Tj164dMxMzMzM6s/v+OemZmZmVkVF8lmZmZmZlVcJJuZmZmZVXGRbGZmZmZWxUWymZmZmVkVF7y3Oh4AAAY7SURBVMlmZmZmZlUUEfWeA5JOAK/U6Z/fBLxap3+7HvqA0XpPooZWWr7gjJud821+zri5Od/62xwRQ+90o4YokutJ0onz+UI1C0n3RMSues+jVlZavuCMm53zbX7OuLk53+XD2y1gpN4TqLFH6j2BGltp+YIzbnbOt/k54+bmfJcJF8kr6ykPImKlXZwrKl9wxs3O+TY/Z9zcnO/y4SIZ7qn3BOxd5XybnzNubs63+Tnj5rZs813xe5LNzMzMzKp5JdnMzMzMrIqLZDMzMzOzKk1XJEu6T9JxSQcq+q6U9H1Jz0h6RFJv6t8iaUrS3nR8qWLMrZL2Szoo6a56PBY7t8VknM7tTOcOpvOF1O+MG9Air+FPVFy/eyWVJV2VzjnfBrXIjFslfSX1H5L0hxVj7pB0IGV8Zz0ei73dIvNtk7Q79e+T9L6KMb6GG5SkjZK+JenZlM8dqX9A0uOSnk8fV6V+SfpbSS+kTK+puK+70nV8QNKt9XpM5xQRTXUANwHXAAcq+v4HuDm1Pw38aWpvqbxdxe0HyV74eih9/hXg/fV+bD4uKOM8sB+4siLbnDNu3GMx+VaN2wG8WJGz823QY5HX8MeBr6Z2J/By+t79XuBA6ssD3wTeU+/H5mPR+d4O7E7tNcBTZAt4voYb+ADWAdekdg/wHHAF8BfAZ1P/Z4G7UvsW4N8BAdcDT6T+DwGPp2u4K/0/6a334ztzNN1KckTsAYarurcDe1L7ceCX3uFutgHPR8SJ9Pk3z2OM1cgiM/4AsD8i9qWxb0ZECWfcsJZwDX8M+GpqO98GtsiMA+iSlAc6gFlgDPhxsh+0kxExB3wb+MV3e+72zhaZ7xXAf6Zxx8leU/dafA03tIg4GhFPp/Y4cAi4FPgI2S80pI+/kNofAf4xMv8N9EtaR5b/noiYi4gJskWtD9bwoSyo6YrkeRwkCwjgl4GNFee2SvqBpG9LujH1vQD8WNqOkScLuXKMNZ75Mt4OhKTHJD0t6Q9SvzNeXha6hs+4FXgwtZ3v8jNfxl8DJoCjZCuLX4yIYbJV5BslDUrqJFupcsaNa7589wEflpSXtBX4iXTO1/AyIWkLcDXwBLA2Io6mU28Aa1P7UuBwxbAjqW8f8EFJnZJWAz9DA+W8UorkTwO3SXqK7GmB2dR/FNgUEVcDvw88IKk3Ik4BvwP8E/Adsqf3SjWftS3GfBnngZ8GPpE+flTS+53xsjNfvgBI+klgMiIOADjfZWm+jK8jy249sBX4jKRtEXEIuAv4BvB1YC/OuJHNl+99ZAXTk8BfA98DSr6GlwdJ3cBDwJ0RMVZ5LrL9FAu+znBEfAN4lCz3B4Hv00A55+s9gVqIiB+SPe2OpO1ke2CIiBlgJrWfkvQi2crjk5G9I84jacwuGig0e7v5Mib75rsnIk6mc4+S7ZX7D2e8fCyQ7xm/yluryGfGON9lZIGMPw58PSKKwHFJ/0X2dPxLEXEvcG8a82dk17s1oAV+Ds8Bv3fmdpK+R7a/1ddwg5PUSlYg3x8RD6fuY5LWRcTRtJ3ieOp/jf+/Qrwh9RERXwC+kO7zAVL+jWBFrCRLWpM+tgB/BHwpfT4kKZfa24DLgZeqxqwCbgP+ofYzt/M1X8bAY8CO9FROHrgZeLZqjDNucAvke6bvV3hrP3L1GOe7DCyQ8avAz6ZzXWR/9PPDqjGbyPYjP1DbWdv5WuDncGfKFUk/B8xFhL9HNzhJIvsF9VBE3F1x6l+BT6X2p4B/qej/9fQqF9cDo6mQzkkaTPe5E9hJ9uxQQ2i6lWRJDwLvA1ZLOgL8MdAt6fZ0k4eB3al9E/B5SUWgDPx22usG8DeSrkztz0dEw/xms9ItJuOIOCXpbrK/mA3g0Yj4t3Q7Z9yAFnkNQ3YdH46Il6ruyvk2qEVm/HfAbkkHyf4yfndE7E/nHko/YIvA7RExUqvHYPNbZL5rgMcklclWFj9ZcVe+hhvXDWRZPSNpb+r7HPDnwD9L+k3gFbIFDMi2VNxCttd8EviN1N8KfCeruRkDfi09u9AQ/LbUZmZmZmZVVsR2CzMzMzOzxXCRbGZmZmZWxUWymZmZmVkVF8lmZmZmZlVcJJuZmZmZVXGRbGZmZmZWxUWymZmZmVmV/wOG7B8aaZupsgAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 864x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax = fig.add_subplot(111)\n", "ax = cpi.plot(ax=ax);\n", "ax.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "P-value of the unit-root test, resoundly rejects the null of no unit-root." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.990432818833742\n" ] } ], "source": [ "print(sm.tsa.adfuller(cpi)[1])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmpjxw569nu/5b21be7d-7d68-4351-af5f-c039bbc7446e vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoregressive Moving Average (ARMA): Artificial data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import pandas as pd\n", "from statsmodels.tsa.arima_process import arma_generate_sample\n", "np.random.seed(12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate some data from an ARMA process:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arparams = np.array([.75, -.25])\n", "maparams = np.array([.65, .35])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arparams = np.r_[1, -arparams]\n", "maparams = np.r_[1, maparams]\n", "nobs = 250\n", "y = arma_generate_sample(arparams, maparams, nobs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Now, optionally, we can add some dates information. For this example, we'll use a pandas time series." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " elif issubdtype(paramsdtype, complex):\n" ] } ], "source": [ "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n", "y = pd.Series(y, index=dates)\n", "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n", "arma_res = arma_mod.fit(trend='nc', disp=-1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n", "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " elif issubdtype(paramsdtype, complex):\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ARMA Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 250\n", "Model: ARMA(2, 2) Log Likelihood -353.445\n", "Method: css-mle S.D. of innovations 0.990\n", "Date: Fri, 12 Jun 2020 AIC 716.891\n", "Time: 07:44:26 BIC 734.498\n", "Sample: 01-31-1980 HQIC 723.977\n", " - 10-31-2000 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1.y 0.7904 0.134 5.878 0.000 0.527 1.054\n", "ar.L2.y -0.2314 0.113 -2.044 0.042 -0.453 -0.009\n", "ma.L1.y 0.7007 0.127 5.525 0.000 0.452 0.949\n", "ma.L2.y 0.4061 0.095 4.291 0.000 0.221 0.592\n", " Roots \n", "=============================================================================\n", " Real Imaginary Modulus Frequency\n", "-----------------------------------------------------------------------------\n", "AR.1 1.7079 -1.1851j 2.0788 -0.0965\n", "AR.2 1.7079 +1.1851j 2.0788 0.0965\n", "MA.1 -0.8628 -1.3108j 1.5693 -0.3427\n", "MA.2 -0.8628 +1.3108j 1.5693 0.3427\n", "-----------------------------------------------------------------------------\n" ] } ], "source": [ "print(arma_res.summary())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2000-06-30 0.173211\n", "2000-07-31 -0.048325\n", "2000-08-31 -0.415804\n", "2000-09-30 0.338725\n", "2000-10-31 0.360838\n", "dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.tail()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " if issubdtype(paramsdtype, float):\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(figsize=(10,8))\n", "fig = arma_res.plot_predict(start='1999m6', end='2001m5', ax=ax)\n", "legend = ax.legend(loc='upper left')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }

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/srv/reproducible-results/rbuild-debian/tmp.uTVwqFbakd/dbd-tmp-iqckgMb/diffoscope_s28eol6u/tmpc6hublhq/d449c0bb-3dec-4a27-b1fe-c1920d412d96 vs.

Differences: { "replace": { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weighted Least Squares" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "import numpy as np\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "from statsmodels.iolib.table import (SimpleTable, default_txt_fmt)\n", "np.random.seed(1024)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WLS Estimation\n", "\n", "### Artificial data: Heteroscedasticity 2 groups \n", "\n", "Model assumptions:\n", "\n", " * Misspecification: true model is quadratic, estimate only linear\n", " * Independent noise/error term\n", " * Two groups for error variance, low and high variance groups" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nsample = 50\n", "x = np.linspace(0, 20, nsample)\n", "X = np.column_stack((x, (x - 5)**2))\n", "X = sm.add_constant(X)\n", "beta = [5., 0.5, -0.01]\n", "sig = 0.5\n", "w = np.ones(nsample)\n", "w[nsample * 6//10:] = 3\n", "y_true = np.dot(X, beta)\n", "e = np.random.normal(size=nsample)\n", "y = y_true + sig * w * e \n", "X = X[:,[0,1]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WLS knowing the true variance ratio of heteroscedasticity" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " WLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.910\n", "Model: WLS Adj. R-squared: 0.909\n", "Method: Least Squares F-statistic: 487.9\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 8.52e-27\n", "Time: 07:39:35 Log-Likelihood: -57.048\n", "No. Observations: 50 AIC: 118.1\n", "Df Residuals: 48 BIC: 121.9\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 5.2726 0.185 28.488 0.000 4.900 5.645\n", "x1 0.4379 0.020 22.088 0.000 0.398 0.478\n", "==============================================================================\n", "Omnibus: 5.040 Durbin-Watson: 2.242\n", "Prob(Omnibus): 0.080 Jarque-Bera (JB): 6.431\n", "Skew: 0.024 Prob(JB): 0.0401\n", "Kurtosis: 4.756 Cond. No. 17.0\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "mod_wls = sm.WLS(y, X, weights=1./w)\n", "res_wls = mod_wls.fit()\n", "print(res_wls.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS vs. WLS\n", "\n", "Estimate an OLS model for comparison: " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5.24256099 0.43486879]\n", "[5.27260714 0.43794441]\n" ] } ], "source": [ "res_ols = sm.OLS(y, X).fit()\n", "print(res_ols.params)\n", "print(res_wls.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the WLS standard errors to heteroscedasticity corrected OLS standard errors:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=====================\n", " x1 const \n", "---------------------\n", "WLS 0.1851 0.0198\n", "OLS 0.2707 0.0233\n", "OLS_HC0 0.194 0.0281\n", "OLS_HC1 0.198 0.0287\n", "OLS_HC3 0.2003 0.029 \n", "OLS_HC3 0.207 0.03 \n", "---------------------\n" ] } ], "source": [ "se = np.vstack([[res_wls.bse], [res_ols.bse], [res_ols.HC0_se], \n", " [res_ols.HC1_se], [res_ols.HC2_se], [res_ols.HC3_se]])\n", "se = np.round(se,4)\n", "colnames = ['x1', 'const']\n", "rownames = ['WLS', 'OLS', 'OLS_HC0', 'OLS_HC1', 'OLS_HC3', 'OLS_HC3']\n", "tabl = SimpleTable(se, colnames, rownames, txt_fmt=default_txt_fmt)\n", "print(tabl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate OLS prediction interval:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "covb = res_ols.cov_params()\n", "prediction_var = res_ols.mse_resid + (X * np.dot(covb,X.T).T).sum(1)\n", "prediction_std = np.sqrt(prediction_var)\n", "tppf = stats.t.ppf(0.975, res_ols.df_resid)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prstd_ols, iv_l_ols, iv_u_ols = wls_prediction_std(res_ols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw a plot to compare predicted values in WLS and OLS:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(res_wls)\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "ax.plot(x, y, 'o', label=\"Data\")\n", "ax.plot(x, y_true, 'b-', label=\"True\")\n", "# OLS\n", "ax.plot(x, res_ols.fittedvalues, 'r--')\n", "ax.plot(x, iv_u_ols, 'r--', label=\"OLS\")\n", "ax.plot(x, iv_l_ols, 'r--')\n", "# WLS\n", "ax.plot(x, res_wls.fittedvalues, 'g--.')\n", "ax.plot(x, iv_u, 'g--', label=\"WLS\")\n", "ax.plot(x, iv_l, 'g--')\n", "ax.legend(loc=\"best\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feasible Weighted Least Squares (2-stage FWLS)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " WLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.914\n", "Model: WLS Adj. R-squared: 0.912\n", "Method: Least Squares F-statistic: 507.1\n", "Date: Fri, 12 Jun 2020 Prob (F-statistic): 3.65e-27\n", "Time: 07:39:38 Log-Likelihood: -55.777\n", "No. Observations: 50 AIC: 115.6\n", "Df Residuals: 48 BIC: 119.4\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 5.2710 0.177 29.828 0.000 4.916 5.626\n", "x1 0.4390 0.019 22.520 0.000 0.400 0.478\n", "==============================================================================\n", "Omnibus: 4.076 Durbin-Watson: 2.251\n", "Prob(Omnibus): 0.130 Jarque-Bera (JB): 4.336\n", "Skew: 0.003 Prob(JB): 0.114\n", "Kurtosis: 4.443 Cond. No. 16.5\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "resid1 = res_ols.resid[w==1.]\n", "var1 = resid1.var(ddof=int(res_ols.df_model)+1)\n", "resid2 = res_ols.resid[w!=1.]\n", "var2 = resid2.var(ddof=int(res_ols.df_model)+1)\n", "w_est = w.copy()\n", "w_est[w!=1.] = np.sqrt(var2) / np.sqrt(var1)\n", "res_fwls = sm.WLS(y, X, 1./w_est).fit()\n", "print(res_fwls.summary())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 0 } }


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3	Version:·0.8.0-9	3	Version:·0.8.0-9
4	Architecture:·all	4	Architecture:·all
5	Maintainer:·Debian·Science·Maintainers·<debian-science-maintainers@lists.alioth.debian.org>	5	Maintainer:·Debian·Science·Maintainers·<debian-science-maintainers@lists.alioth.debian.org>
6	Installed-Size:·71085	6	Installed-Size:·71082
7	Depends:·libjs-requirejs,·libjs-sphinxdoc·(>=·1.0),·libjs-mathjax	7	Depends:·libjs-requirejs,·libjs-sphinxdoc·(>=·1.0),·libjs-mathjax
8	Suggests:·python-statsmodels,·python3-doc,·python-numpy-doc,·python-patsy-doc,·python-pandas-doc,·python-scipy-doc	8	Suggests:·python-statsmodels,·python3-doc,·python-numpy-doc,·python-patsy-doc,·python-pandas-doc,·python-scipy-doc
9	Breaks:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc	9	Breaks:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc
10	Replaces:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc	10	Replaces:·python-scikits-statsmodels-doc,·python-scikits.statsmodels-doc
11	Section:·doc	11	Section:·doc
12	Priority:·optional	12	Priority:·optional
13	Homepage:·http://statsmodels.sourceforge.net/	13	Homepage:·http://statsmodels.sourceforge.net/


Offset 2054, 890 lines modified		Offset 2054, 890 lines modified
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00008060:·3e73·613c·2f73·7061·6e3e·203c·7370·616e··>sa</span>·<span		00008060:·3e73·613c·2f73·7061·6e3e·203c·7370·616e··>sa</span>·<span
00008070:·2063·6c61·7373·3d22·6f22·3e3d·3c2f·7370···class="o">=</sp		00008070:·2063·6c61·7373·3d22·6f22·3e3d·3c2f·7370···class="o">=</sp
00008080:·616e·3e20·3c73·7061·6e20·636c·6173·733d··an>·<span·class=		00008080:·616e·3e20·3c73·7061·6e20·636c·6173·733d··an>·<span·class=
00008090:·226e·223e·7265·733c·2f73·7061·6e3e·0a0a··"n">res</span>..		00008090:·226e·223e·7265·733c·2f73·7061·6e3e·0a0a··"n">res</span>..
000080a0:·3c73·7061·6e20·636c·6173·733d·226e·6522··<span·class="ne"		000080a0:·3c73·7061·6e20·636c·6173·733d·226e·6522··<span·class="ne"
000080b0:·3e67·6169·6572·726f·723c·2f73·7061·6e3e··>gaierror</span>		000080b0:·3e67·6169·6572·726f·723c·2f73·7061·6e3e··>gaierror</span>
000080c0:·3a20·5b45·7272·6e6f·202d·335d·2054·~~656d~~··:·[Errno·-3]·~~Tem~~		000080c0:·3a20·5b45·7272·6e6f·202d·335d·2045·7272··:·[Errno·-3]·Err
000080d0:·706f·7261·7~~279~~·~~2066~~·6~~169~~·6~~c75~~·7265·2069··por~~ary·failur~~e·i		000080d0:·6f72·6520·7465·6d70·6f72·616e·656f·206e··ore·temporaneo·n
000080e0:·6~~e20·6~~e61·6d65·2072·6573·6f6c·7574·696f·~~·n~~·~~name~~·resolutio		000080e0:·656c·6c61·2072·6973·6f6c·757a·696f·6e65··ella·risoluzione
000080f0:·~~6e0a~~·~~0a3~~c·~~7370~~·616e·~~2063~~·~~6c61~~·7373·~~3d22~~··~~n..<span·cla~~s~~s="~~		000080f0:·2064·656c·206e·6f6d·650a·0a3c·7370·616e···del·nome..<span
00008100:·~~6e22~~·~~3e44~~·7572·~~696e~~·6~~73c~~·~~2f73~~·7~~061~~·6e3e··~~n">Duri~~n~~g</spa~~n>		00008100:·2063·6c61·7373·3d22·6e22·3e44·7572·696e···class="n">Durin
00008110:·203c·7370·616e·2063~~·6~~c61·7373·~~3d22~~·~~6e22~~···<span·~~cla~~s~~s="n"~~		00008110:·673c·2f73·7061·6e3e·203c·7370·616e·2063··g</span>·<span·c
00008120:·3e68·~~616e~~·~~646c~~·6~~96e~~·~~673c~~·~~2f73~~·7061·6e3e··>ha~~ndli~~n~~g</span>~~		00008120:·6c61·7373·3d22·6e22·3e68·616e·646c·696e··lass="n">handlin
00008130:·203c·7370·616e·2063~~·6~~c61·7373·~~3d22~~·~~6e22~~···<span·~~cla~~s~~s="n"~~		00008130:·673c·2f73·7061·6e3e·203c·7370·616e·2063··g</span>·<span·c
00008140:·3e6f·~~663c~~·~~2f73~~·~~7061~~·6e3e·203c·7370·616e··>o~~f</~~span>·<span		00008140:·6c61·7373·3d22·6e22·3e6f·663c·2f73·7061··lass="n">of</spa
00008150:·~~2063~~·~~6c61~~·7373·~~3d22~~·~~6e22~~·~~3e74~~·~~6865~~·3c2f··~~·cla~~s~~s="n">the</~~		00008150:·6e3e·203c·7370·616e·2063·6c61·7373·3d22··n>·<span·class="
00008160:·7370·616e·3e20·3c73·7061·6e20·636c~~·61~~73··~~span>·~~<span·~~cla~~s		00008160:·6e22·3e74·6865·3c2f·7370·616e·3e20·3c73··n">the</span>·<s
00008170:·7~~33d~~·226e~~·2~~23e·6162·6f76·653c·2f73·7061··~~s="n">~~a~~bove</spa~~		00008170:·7061·6e20·636c·6173·733d·226e·223e·6162··pan·class="n">ab
00008180:·6~~e3e~~·203c·7370·616e·2063~~·6~~c61·7373·~~3d22~~··~~n>·~~<span·~~cla~~s~~s="~~		00008180:·6f76·653c·2f73·7061·6e3e·203c·7370·616e··ove</span>·<span
00008190:·~~6e22~~·3e65·7863·~~6570~~·~~7469~~·6f6e·~~3c2f~~·7370··~~n">exception</s~~p		00008190:·2063·6c61·7373·3d22·6e22·3e65·7863·6570···class="n">excep
000081a0:·616e·3~~e3c~~·7370·616e·~~206~~3~~·6~~c61·7373·~~3d22~~··an><span~~·c~~las~~s="~~		000081a0:·7469·6f6e·3c2f·7370·616e·3e3c·7370·616e··tion</span><span
000081b0:·7022·~~3e2c~~·~~3c2f~~·~~7370~~·~~616e~~·3e20·3c73·7~~061~~··~~p">,</span~~>·<spa		000081b0:·2063·6c61·7373·3d22·7022·3e2c·3c2f·7370···class="p">,</sp
000081c0:·6~~e20~~·~~636c~~·6173·7~~33d~~·~~226e~~·~~223e~~·616e·~~6f74~~··~~n·cla~~s~~s="n">~~a~~not~~		000081c0:·616e·3e20·3c73·7061·6e20·636c·6173·733d··an>·<span·class=
000081d0:·6865·723c·~~2f73~~·~~7061~~·6~~e3e~~·203c·7370·616e··~~her</span>·~~<span		000081d0:·226e·223e·616e·6f74·6865·723c·2f73·7061··"n">another</spa
000081e0:·~~2063~~·~~6c61~~·7373·~~3d22~~·~~6e22~~·3e65·7863·~~6570~~··~~·cla~~s~~s="n">excep~~		000081e0:·6e3e·203c·7370·616e·2063·6c61·7373·3d22··n>·<span·class="
000081f0:·~~7469~~·6f6e·~~3c2f~~·7370·616e·~~3e20~~·3c73·7~~061~~··~~tion</s~~p~~an>·~~<spa		000081f0:·6e22·3e65·7863·6570·7469·6f6e·3c2f·7370··n">exception</sp
00008200:·6~~e20~~·~~636c~~·6173·7~~33d~~·~~226e~~·~~223e~~·6f63·6375··~~n·cla~~s~~s="n">occu~~		00008200:·616e·3e20·3c73·7061·6e20·636c·6173·733d··an>·<span·class=
00008210:·7272·6564·~~3c2f~~·7370·616e·3~~e3c~~·7370·~~616e··~~rred</~~span><~~span		00008210:·226e·223e·6f63·6375·7272·6564·3c2f·7370··"n">occurred</sp
00008220:·2063·~~6c61~~·7373·~~3d22~~·7022·~~3e3a~~·~~3c2f~~·73~~70·~~··~~clas~~s~~="p">:</~~sp		00008220:·616e·3e3c·7370·616e·2063·6c61·7373·3d22··an><span·class="
00008230:·~~616e~~·3e0a·~~0a3c~~·7370·616e·~~2063~~·~~6c61~~·7373··an>~~..<~~span~~·cla~~ss		00008230:·7022·3e3a·3c2f·7370·616e·3e0a·0a3c·7370··p">:</span>..<sp
00008240:·3d22·6e65·~~223e~~·~~555~~2·~~4c4~~5·7272·~~6f7~~2·3c~~2f··~~="ne">UR~~LError</~~		00008240:·616e·2063·6c61·7373·3d22·6e65·223e·5552··an·class="ne">UR
00008250:·7370·6~~16e~~·3~~e3c~~·7370·616e·~~206~~3~~·6~~c61·7373··~~span>~~<span~~·c~~lass		00008250:·4c45·7272·6f72·3c2f·7370·616e·3e3c·7370··LError</span><sp
00008260:·~~3d22~~·~~6720~~·6~~72d~~·~~5768~~·~~6974~~·6~~573~~·~~7061~~·6365··~~="g·g-Whitespace~~		00008260:·616e·2063·6c61·7373·3d22·6720·672d·5768··an·class="g·g-Wh
00008270:·~~223e~~·~~2020~~·2020·~~2020~~·2~~020~~·2020·2020·2020··~~">········~~······		00008270:·6974·6573·7061·6365·223e·2020·2020·2020··itespace">······
00008280:·2020·2020·2020·2020·2020·2020·2020·2020··················		00008280:·2020·2020·2020·2020·2020·2020·2020·2020··················
00008290:·2020·2020·3c2f·~~737~~0·~~616e~~·~~3e54~~·~~7261~~·6365······~~</span>Trace~~		00008290:·2020·2020·2020·2020·2020·2020·3c2f·7370··············</sp
000082a0:·6261·636b·~~2028~~·6d6f·7374·2072·6563·~~656e··~~back·(mo~~st·recen~~		000082a0:·616e·3e54·7261·6365·6261·636b·2028·6d6f··an>Traceback·(mo
000082b0:·7420·6361·6c6c·~~206c~~·6~~173~~·~~7429~~·0~~a3c~~·~~7370··~~t·call·l~~ast).<sp~~		000082b0:·7374·2072·6563·656e·7420·6361·6c6c·206c··st·recent·call·l
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000082d0:·~~743b~~·6~~970~~·~~7974~~·686f·~~6e2d~~·696e·7075·~~742d~~··~~t;ipython-~~input-		000082d0:·3d22·6e6e·223e·266c·743b·6970·7974·686f··="nn"><ipytho
000082e0:·~~3239~~·2d64·~~3833~~·~~3333~~·6432·~~3933~~·6539·3~~026~~··29-~~d8333d~~29~~3e90&~~		000082e0:·6e2d·696e·7075·742d·3239·2d64·3833·3333··n-input-29-d8333
000082f0:·6~~774~~·3b3c·~~2f73~~·7061·6e3e·2069·6e20·3c~~73··~~gt;</spa~~n>·in·<s~~		000082f0:·6432·3933·6539·3026·6774·3b3c·2f73·7061··d293e90></spa
00008300:·7061·6e20·636c~~·61~~73·7~~33d~~·~~226e~~·6~~922~~·~~3e26~~··pan·~~clas~~s="n~~i">&~~		00008300:·6e3e·2069·6e20·3c73·7061·6e20·636c·6173··n>·in·<span·clas
00008310:·6c7~~4·~~3b6d·6f64·~~756c~~·6526·6774·3b3c·2f7~~3··lt;module~~>~~</s~~		00008310:·733d·226e·6922·3e26·6c74·3b6d·6f64·756c··s="ni"><modul
00008320:·~~7061~~·6~~e3e~~·3c73·7061·6e20·636c~~·61~~73·7~~33d~~··~~pan>~~<span~~·c~~lass=		00008320:·6526·6774·3b3c·2f73·7061·6e3e·3c73·7061··e></span><spa
00008330:·~~226e~~·~~7422~~·~~3e28~~·293c·2~~f73~~·7~~061~~·6e3e·0a3c··"n~~t">()</span~~>.<		00008330:·6e20·636c·6173·733d·226e·7422·3e28·293c··n·class="nt">()<
00008340:·7370·616e~~·2~~063~~·6~~c61·7373·~~3d22~~·6e65·~~223e~~··span·~~cla~~s~~s="ne">~~		00008340:·2f73·7061·6e3e·0a3c·7370·616e·2063·6c61··/span>.<span·cla
00008350:·~~2d2d~~·2d2d·2667·743b·2~~03c~~·2~~f73~~·7061·6e3e··~~---->·</span>~~		00008350:·7373·3d22·6e65·223e·2d2d·2d2d·2667·743b··ss="ne">---->
00008360:·3c73·7061·6e20·636c~~·61~~73·7~~33d~~·~~226d~~·6~~922~~··<span~~·c~~las~~s="mi"~~		00008360:·203c·2f73·7061·6e3e·3c73·7061·6e20·636c···</span><span·cl
00008370:·~~3e31~~·~~3c2f~~·~~7370~~·6~~16e~~·3e20·3c73·7~~061~~·6~~e20~~··~~>1</span~~>·<span·		00008370:·6173·733d·226d·6922·3e31·3c2f·7370·616e··ass="mi">1</span
00008380:·~~636c~~·6173·7~~33d~~·226e~~·2~~23e·6466·~~3c2f~~·7370··~~clas~~s="n~~">df</sp~~		00008380:·3e20·3c73·7061·6e20·636c·6173·733d·226e··>·<span·class="n
00008390:·~~616~~e·~~3e20~~·3c73·7061·6e20·636c~~·61~~73·7~~33d~~··~~an>·~~<span·~~cla~~ss=		00008390:·223e·6466·3c2f·7370·616e·3e20·3c73·7061··">df</span>·<spa
000083a0:·~~226f~~·~~223e~~·~~3d3c~~·~~2f73~~·7061·6e3e·203c·7370··~~"o">=</span~~>·<sp		000083a0:·6e20·636c·6173·733d·226f·223e·3d3c·2f73··n·class="o">=</s
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000083c0:·2~~f73~~·~~7061~~·6e3e·3c73·7061·6e20·636c~~·61~~73··~~/span>~~<span~~·c~~las		000083c0:·3d22·6e22·3e73·6d3c·2f73·7061·6e3e·3c73··="n">sm</span><s
000083d0:·7~~33d~~·226f·~~223e~~·2e3c·2~~f73~~·~~7061~~·6e3e·~~3c73~~··s="o">.~~</span>~~<s		000083d0:·7061·6e20·636c·6173·733d·226f·223e·2e3c··pan·class="o">.<
000083e0:·7061·6e20·636c~~·61~~73·7~~33d~~·~~226e~~·~~223e~~·6~~461~~··pan~~·cl~~ass="n~~">d~~a		000083e0:·2f73·7061·6e3e·3c73·7061·6e20·636c·6173··/span><span·clas
000083f0:·7~~461~~·7365·~~7473~~·~~3c2f~~·7~~370~~·616e·~~3e3c~~·73~~70·~~·~~tasets</sp~~a~~n><~~sp		000083f0:·733d·226e·223e·6461·7461·7365·7473·3c2f··s="n">datasets</
00008400:·~~616e~~·2063·~~6c61~~·7373·~~3d22~~·~~6f22~~·~~3e2e~~·~~3c2f~~··~~an·cla~~s~~s="o">.</~~		00008400:·7370·616e·3e3c·7370·616e·2063·6c61·7373··span><span·class
00008410:·~~7370~~·6~~16e~~·3e3c·7370·616e·~~206~~3~~·6~~c61·7373··~~span~~><span~~·c~~lass		00008410:·3d22·6f22·3e2e·3c2f·7370·616e·3e3c·7370··="o">.</span><sp
00008420:·~~3d22~~·~~6e22~~·3e67·6574·~~5f7~~2·6~~461~~·746~~1·~~7365··="n~~">get_rdatas~~e		00008420:·616e·2063·6c61·7373·3d22·6e22·3e67·6574··an·class="n">get
00008430:·743c·~~2f73~~·7061·6e3e·3c73·7061·6e20·6~~36c··~~t</sp~~an><~~span~~·c~~l		00008430:·5f72·6461·7461·7365·743c·2f73·7061·6e3e··_rdataset</span>
00008440:·6173·7~~33d~~·~~227~~0·~~223e~~·~~283c~~·~~2f73~~·~~7061~~·6e3e··as~~s="p">(</span~~>		00008440:·3c73·7061·6e20·636c·6173·733d·2270·223e··<span·class="p">
00008450:·3c73·7061·6e20·636c~~·61~~73·7~~33d~~·22~~73·~~3~~222~~··<span~~·c~~las~~s="s2"~~		00008450:·283c·2f73·7061·6e3e·3c73·7061·6e20·636c··(</span><span·cl
00008460:·~~3e26~~·7~~175~~·6f74·3~~b56~~·~~6973~~·7~~561~~·6~~c41~~·6375··~~>"VisualAcu~~		00008460:·6173·733d·2273·3222·3e26·7175·6f74·3b56··ass="s2">"V
00008470:·6974·7~~926~~·~~7175~~·6f74·~~3b3c~~·~~2f73~~·7~~061~~·6~~e3e~~··i~~ty"</span>~~		00008470:·6973·7561·6c41·6375·6974·7926·7175·6f74··isualAcuity&quot
00008480:·3c73·7061·6e20·636c~~·61~~73·7~~33d~~·~~227~~0·~~223e~~··<span~~·c~~las~~s="p">~~		00008480:·3b3c·2f73·7061·6e3e·3c73·7061·6e20·636c··;</span><span·cl
00008490:·~~2c3c~~·~~2f73~~·~~7061~~·6e3e·203c·7370·616e·~~2063~~··~~,</span~~>·<span·c		00008490:·6173·733d·2270·223e·2c3c·2f73·7061·6e3e··ass="p">,</span>
000084a0:·~~6c61~~·7373·~~3d22~~·~~7332~~·~~223e~~·2671·~~756f~~·743b··las~~s="s2">"~~		000084a0:·203c·7370·616e·2063·6c61·7373·3d22·7332···<span·class="s2
000084b0:·7663·6426·7175·6f74·~~3b3c~~·~~2f73~~·7~~061~~·6~~e3e~~··vcd"~~</span>~~		000084b0:·223e·2671·756f·743b·7663·6426·7175·6f74··">"vcd&quot
000084c0:·3c73·7061·6e20·636c~~·61~~73·7~~33d~~·~~227~~0·~~223e~~··<span~~·c~~las~~s="p">~~		000084c0:·3b3c·2f73·7061·6e3e·3c73·7061·6e20·636c··;</span><span·cl
000084d0:·~~2c3c~~·~~2f73~~·~~7061~~·6e3e·203c·7370·616e·~~2063~~··~~,</span~~>·<span·c		000084d0:·6173·733d·2270·223e·2c3c·2f73·7061·6e3e··ass="p">,</span>
000084e0:·~~6c61~~·7373·~~3d22·~~6e22·3e63·6163·~~6865~~·3c2f··~~las~~s="n~~">c~~a~~che</~~		000084e0:·203c·7370·616e·2063·6c61·7373·3d22·6e22···<span·class="n"
000084f0:·~~7370~~·616e·3e3c·7370·616e·~~206~~3~~·6~~c61·7373··span><span~~·c~~lass		000084f0:·3e63·6163·6865·3c2f·7370·616e·3e3c·7370··>cache</span><sp
00008500:·~~3d22~~·~~6f22~~·~~3e3d~~·~~3c2f~~·~~7370~~·6~~16e~~·3e3c·~~7370~~··~~="o">=</span~~>~~<sp~~		00008500:·616e·2063·6c61·7373·3d22·6f22·3e3d·3c2f··an·class="o">=</
00008510:·~~616e~~·2063·~~6c61~~·7373·~~3d22~~·~~6270~~·~~223e~~·5472··~~an·cla~~s~~s="bp">Tr~~		00008510:·7370·616e·3e3c·7370·616e·2063·6c61·7373··span><span·class
00008520:·7565·~~3c2f~~·7370·616e·3~~e3c~~·7370·616e·~~2063··~~ue</~~span><~~span~~·c~~		00008520:·3d22·6270·223e·5472·7565·3c2f·7370·616e··="bp">True</span
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000085f0:·~~3c73·~~7061·6e20·6~~36c~~·6173·7~~33d~~·~~226e~~·6~~922~~··<span·~~clas~~s="ni"		000085f0:·7061·6e3e·2069·6e20·3c73·7061·6e20·636c··pan>·in·<span·cl
00008600:·~~3e67~~·~~6574~~·~~5f72~~·6~~461~~·746~~1·~~7365·743c·2f73··~~>get_rdatas~~et~~</s~~		00008600:·6173·733d·226e·6922·3e67·6574·5f72·6461··ass="ni">get_rda
00008610:·7061·6e3e·3c73·7061·6e20·636c~~·61~~73·7~~33d~~··pan><span~~·c~~lass=		00008610:·7461·7365·743c·2f73·7061·6e3e·3c73·7061··taset</span><spa
00008620:·226e·~~7422~~·~~3e28~~·6461·7461·6e61·6d65·2c~~20··~~"nt">(d~~atan~~a~~me,·~~		00008620:·6e20·636c·6173·733d·226e·7422·3e28·6461··n·class="nt">(da
00008630:·7061·636b·6167·652c·2063·6163·6865·~~293~~c··package,·cac~~he)<~~		00008630:·7461·6e61·6d65·2c20·7061·636b·6167·652c··taname,·package,
00008640:·2f73·7061·6~~e3e~~·0a3c·7370·616e~~·2~~063~~·6~~c61··~~/span>.<~~span·~~cla~~		00008640:·2063·6163·6865·293c·2f73·7061·6e3e·0a3c···cache)</span>.<
00008650:·7373·~~3d22~~·~~6720~~·6~~72d~~·~~5768~~·~~6974~~·6~~573~~·~~7061~~··s~~s="g·g-Whitespa~~		00008650:·7370·616e·2063·6c61·7373·3d22·6720·672d··span·class="g·g-
00008660:·6365·~~223e~~·~~2020~~·2020·3c2f·~~737~~0·~~616e~~·~~3e3c··~~ce">····~~</span><~~		00008660:·5768·6974·6573·7061·6365·223e·2020·2020··Whitespace">····
00008670:·7370·616e·~~206~~3~~·6~~c61·7373·~~3d22~~·6d69·~~223e~~··span~~·c~~las~~s="mi">~~		00008670:·3c2f·7370·616e·3e3c·7370·616e·2063·6c61··</span><span·cla
00008680:·3238·3~~83c~~·~~2f73~~·~~7061~~·6e3e·~~2020~~·2~~020~~·2020··~~288</span>······~~		00008680:·7373·3d22·6d69·223e·3238·383c·2f73·7061··ss="mi">288</spa
00008690:·~~2020~~·2020·2020·2020·2020·2020·2020·2020··~~··~~··············		00008690:·6e3e·2020·2020·2020·2020·2020·2020·2020··n>··············
000086a0:·~~3c73~~·7061·6e20·~~636c~~·6173·7~~33d~~·~~2273~~·~~3222~~··~~<span~~·~~cla~~s~~s="s2"~~		000086a0:·2020·2020·2020·2020·3c73·7061·6e20·636c··········<span·cl
000086b0:·~~3e26~~·7~~175~~·6f74·3~~b6d~~·~~6173~~·7465·~~722f~~·646f··~~>"master/do~~		000086b0:·6173·733d·2273·3222·3e26·7175·6f74·3b6d··ass="s2">"m
000086c0:·6~~32f~~·~~2671~~·756f·743b·3c2f·7370·616e·3e3c··~~c/"<~~/~~span><~~		000086c0:·6173·7465·722f·646f·632f·2671·756f·743b··aster/doc/"
000086d0:·7370·616e·~~206~~3~~·6~~c61·7373·~~3d22~~·~~6f22~~·~~3e2b~~··span~~·c~~las~~s="o">+~~		000086d0:·3c2f·7370·616e·3e3c·7370·616e·2063·6c61··</span><span·cla
000086e0:·~~3c2f~~·~~7370~~·6~~16e~~·3e3c·7370·616e·~~206~~3~~·6~~c61··~~</span~~><span~~·c~~la		000086e0:·7373·3d22·6f22·3e2b·3c2f·7370·616e·3e3c··ss="o">+</span><
000086f0:·7373·~~3d22~~·~~6e22~~·~~3e70~~·~~616~~3·~~6b61~~·6~~765~~·3~~c2f~~··ss="n~~">p~~a~~ckage</~~		000086f0:·7370·616e·2063·6c61·7373·3d22·6e22·3e70··span·class="n">p
00008700:·7370·616e·3~~e3c~~·7370·616e·~~206~~3~~·6~~c61·7373··span><span~~·c~~lass		00008700:·6163·6b61·6765·3c2f·7370·616e·3e3c·7370··ackage</span><sp
00008710:·~~3d22~~·~~6f22~~·~~3e2b~~·~~3c2f~~·~~7370~~·6~~16e~~·3e3c·~~7370~~··~~="o">+</span~~>~~<sp~~		00008710:·616e·2063·6c61·7373·3d22·6f22·3e2b·3c2f··an·class="o">+</
00008720:·~~616e~~·2063·~~6c61~~·7373·~~3d22~~·~~7332~~·~~223e~~·2671··~~an·cla~~s~~s="s2">&q~~		00008720:·7370·616e·3e3c·7370·616e·2063·6c61·7373··span><span·class
00008730:·~~756f~~·743b·2~~f72~~·7374·~~2f26~~·7~~175~~·6f74·3b3~~c·~~·uo~~t;/rst/~~"<		00008730:·3d22·7332·223e·2671·756f·743b·2f72·7374··="s2">"/rst
00008740:·2f73·7~~061~~·6~~e3e~~·3c73·7061·6e20·636c~~·61~~73··/~~span>~~<span~~·c~~las		00008740:·2f26·7175·6f74·3b3c·2f73·7061·6e3e·3c73··/"</span><s
00008750:·7~~33d~~·~~227~~0·~~223e~~·~~293c~~·~~2f73~~·~~7061~~·6e3e·0a3c··~~s="p">)</span~~>.<		00008750:·7061·6e20·636c·6173·733d·2270·223e·293c··pan·class="p">)<
00008760:·7370·616e~~·2~~063~~·6~~c61·7373·~~3d22~~·~~6720~~·6~~72d~~··span·~~cla~~s~~s="g·g-~~		00008760:·2f73·7061·6e3e·0a3c·7370·616e·2063·6c61··/span>.<span·cla
00008770:·5768·6~~974~~·6~~573~~·7061·6~~365~~·~~223e~~·2020·~~2020~~··Whitespa~~ce">····~~		00008770:·7373·3d22·6720·672d·5768·6974·6573·7061··ss="g·g-Whitespa
00008780:·3c2f·~~737~~0·~~616e~~·3~~e3c~~·7370·616e·~~2063~~·6c61··</span><~~span·cla~~		00008780:·6365·223e·2020·2020·3c2f·7370·616e·3e3c··ce">····</span><
00008790:·7373·~~3d22~~·6d69·~~223e~~·3238·3~~93c~~·~~2f73~~·~~7061~~··s~~s="mi">289</spa~~		00008790:·7370·616e·2063·6c61·7373·3d22·6d69·223e··span·class="mi">
000087a0:·6e3~~e·~~2020·~~2020~~·2~~03c~~·7~~370~~·616e·2063·~~6c61~~·~~·n>~~··~~···~~<span·~~cla~~		000087a0:·3238·393c·2f73·7061·6e3e·2020·2020·203c··289</span>·····<
000087b0:·7373·~~3d22·~~6e22·3e63·6163·~~6865~~·3c2f·73~~70·~~·ss="n~~">c~~a~~che</sp~~		000087b0:·7370·616e·2063·6c61·7373·3d22·6e22·3e63··span·class="n">c
000087c0:·616e·~~3e20~~·3c73·7061·6e20·636c~~·61~~73·7~~33d~~··a~~n>·~~<span·~~cla~~ss=		000087c0:·6163·6865·3c2f·7370·616e·3e20·3c73·7061··ache</span>·<spa
000087d0:·~~226f~~·~~223e~~·~~3d3c~~·~~2f73~~·7061·6e3e·203c·7370··~~"o">=</span~~>·<sp		000087d0:·6e20·636c·6173·733d·226f·223e·3d3c·2f73··n·class="o">=</s
000087e0:·616e·2063~~·6~~c61·7373·~~3d22~~·~~6e22~~·~~3e5f~~·6765··an·~~cla~~s~~s="n">_ge~~		000087e0:·7061·6e3e·203c·7370·616e·2063·6c61·7373··pan>·<span·class
000087f0:·~~745f~~·6~~361~~·~~6368~~·6~~53c~~·~~2f73~~·7061·6~~e3e~~·~~3c73~~··~~t_cache</sp~~a~~n><s~~		000087f0:·3d22·6e22·3e5f·6765·745f·6361·6368·653c··="n">_get_cache<
00008800:·7061·6e20·636c~~·61~~73·7~~33d~~·~~227~~0·~~223e~~·283c··pan~~·c~~las~~s="p">(<~~		00008800:·2f73·7061·6e3e·3c73·7061·6e20·636c·6173··/span><span·clas
00008810:·~~2f73~~·~~7061~~·6e3e·3c73·7061·6e20·636c~~·61~~73··~~/s~~p~~an>~~<span~~·c~~las		00008810:·733d·2270·223e·283c·2f73·7061·6e3e·3c73··s="p">(</span><s
00008820:·7~~33d~~·226e~~·2~~23e·6361·6~~368~~·653c·2f73·7061··s="n~~">c~~a~~che</sp~~a		00008820:·7061·6e20·636c·6173·733d·226e·223e·6361··pan·class="n">ca
00008830:·6~~e3e~~·3c73·7061·6e20·636c~~·61~~73·7~~33d~~·~~227~~0··n><span~~·c~~las~~s="p~~		00008830:·6368·653c·2f73·7061·6e3e·3c73·7061·6e20··che</span><span·
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00001a60:·6574·686f·643a·2020·2020·2020·2020·2020··ethod:··········		00001a60:·6574·686f·643a·2020·2020·2020·2020·2020··ethod:··········
00001a70:·2020·2020·2020·2020·2020·2020·2020·2020··················		00001a70:·2020·2020·2020·2020·2020·2020·2020·2020··················
00001a80:·204d·4c45·2020·2044·6620·4d6f·6465·6c3a···MLE···Df·Model:		00001a80:·204d·4c45·2020·2044·6620·4d6f·6465·6c3a···MLE···Df·Model:
00001a90:·2020·2020·2020·2020·2020·2020·2020·2020··················		00001a90:·2020·2020·2020·2020·2020·2020·2020·2020··················
00001aa0:·2020·2020·2020·2020·2020·2020·333c·2f73··············3</s		00001aa0:·2020·2020·2020·2020·2020·2020·333c·2f73··············3</s
00001ab0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class		00001ab0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class
00001ac0:·3d22·676f·223e·4461·7465·3a20·2020·2020··="go">Date:·····		00001ac0:·3d22·676f·223e·4461·7465·3a20·2020·2020··="go">Date:·····
00001ad0:·2020·2020·2020·2020·2020·2057·6564·2c20·············~~Wed~~,·		00001ad0:·2020·2020·2020·2020·2020·2046·7269·2c20·············Fri,·
00001ae0:·3130·204a·756e·2032·3032·3020·2020·5073··10·Jun·2020···Ps		00001ae0:·3132·204a·756e·2032·3032·3020·2020·5073··12·Jun·2020···Ps
00001af0:·6575·646f·2052·2d73·7175·2e3a·2020·2020··eudo·R-squ.:····		00001af0:·6575·646f·2052·2d73·7175·2e3a·2020·2020··eudo·R-squ.:····
00001b00:·2020·2020·2020·2020·2020·2020·2020·302e················0.		00001b00:·2020·2020·2020·2020·2020·2020·2020·302e················0.
00001b10:·3337·3430·3c2f·7370·616e·3e0a·3c73·7061··3740</span>.<spa		00001b10:·3337·3430·3c2f·7370·616e·3e0a·3c73·7061··3740</span>.<spa
00001b20:·6e20·636c·6173·733d·2267·6f22·3e54·696d··n·class="go">Tim		00001b20:·6e20·636c·6173·733d·2267·6f22·3e54·696d··n·class="go">Tim
00001b30:·653a·2020·2020·2020·2020·2020·2020·2020··e:··············		00001b30:·653a·2020·2020·2020·2020·2020·2020·2020··e:··············
00001b40:·2020·2020·2020·2020·2020·3233·3a33·363a············23:36:		00001b40:·2020·2020·2020·2020·2020·3037·3a34·373a············07:47:
00001b50:·3531·2020·204c·6f67·2d4c·696b·656c·6968··51···Log-Likelih		00001b50:·3538·2020·204c·6f67·2d4c·696b·656c·6968··58···Log-Likelih
00001b60:·6f6f·643a·2020·2020·2020·2020·2020·2020··ood:············		00001b60:·6f6f·643a·2020·2020·2020·2020·2020·2020··ood:············
00001b70:·2020·2020·2d31·322e·3839·303c·2f73·7061······-12.890</spa		00001b70:·2020·2020·2d31·322e·3839·303c·2f73·7061······-12.890</spa
00001b80:·6e3e·0a3c·7370·616e·2063·6c61·7373·3d22··n>.<span·class="		00001b80:·6e3e·0a3c·7370·616e·2063·6c61·7373·3d22··n>.<span·class="
00001b90:·676f·223e·636f·6e76·6572·6765·643a·2020··go">converged:··		00001b90:·676f·223e·636f·6e76·6572·6765·643a·2020··go">converged:··
00001ba0:·2020·2020·2020·2020·2020·2020·2020·2020··················		00001ba0:·2020·2020·2020·2020·2020·2020·2020·2020··················
00001bb0:·2020·2020·2054·7275·6520·2020·4c4c·2d4e·······True···LL-N		00001bb0:·2020·2020·2054·7275·6520·2020·4c4c·2d4e·······True···LL-N
00001bc0:·756c·6c3a·2020·2020·2020·2020·2020·2020··ull:············		00001bc0:·756c·6c3a·2020·2020·2020·2020·2020·2020··ull:············


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00001c70:·616e·3e3c·7370·616e·2063·6c61·7373·3d22··an><span·class="		00001c70:·616e·3e3c·7370·616e·2063·6c61·7373·3d22··an><span·class="
00001c80:·6e22·3e63·695f·6d65·616e·3c2f·7370·616e··n">ci_mean</span		00001c80:·6e22·3e63·695f·6d65·616e·3c2f·7370·616e··n">ci_mean</span
00001c90:·3e3c·7370·616e·2063·6c61·7373·3d22·7022··><span·class="p"		00001c90:·3e3c·7370·616e·2063·6c61·7373·3d22·7022··><span·class="p"
00001ca0:·3e28·293c·2f73·7061·6e3e·0a3c·7370·616e··>()</span>.<span		00001ca0:·3e28·293c·2f73·7061·6e3e·0a3c·7370·616e··>()</span>.<span
00001cb0:·2063·6c61·7373·3d22·6768·223e·4f75·745b···class="gh">Out[		00001cb0:·2063·6c61·7373·3d22·6768·223e·4f75·745b···class="gh">Out[
00001cc0:·355d·3a20·3c2f·7370·616e·3e3c·7370·616e··5]:·</span><span		00001cc0:·355d·3a20·3c2f·7370·616e·3e3c·7370·616e··5]:·</span><span
00001cd0:·2063·6c61·7373·3d22·676f·223e·282d·302e···class="go">(-0.		00001cd0:·2063·6c61·7373·3d22·676f·223e·282d·302e···class="go">(-0.
00001ce0:·3230·3238·3332·3931·3331·3236·3434·3030··~~202832~~9131264400		00001ce0:·3339·3639·3433·3138·3236·3537·3032·3739··3969431826570279
00001cf0:·382c·2030·2e34·3039·3834·3731·3530·3837··8,·0.40~~984~~715087		00001cf0:·342c·2030·2e30·3736·3337·3335·3030·3230··4,·0.07637350020
00001d00:·3039·3438·3533·293c·2f73·7061·6e3e·0a0a··~~094853~~)</span>..		00001d00:·3333·3036·3431·293c·2f73·7061·6e3e·0a0a··330641)</span>..
00001d10:·3c73·7061·6e20·636c·6173·733d·2267·6f22··<span·class="go"		00001d10:·3c73·7061·6e20·636c·6173·733d·2267·6f22··<span·class="go"
00001d20:·3e23·2074·6573·7420·7661·7269·616e·6365··>#·test·variance		00001d20:·3e23·2074·6573·7420·7661·7269·616e·6365··>#·test·variance
00001d30:·2069·7320·313c·2f73·7061·6e3e·0a3c·7370···is·1</span>.<sp		00001d30:·2069·7320·313c·2f73·7061·6e3e·0a3c·7370···is·1</span>.<sp
00001d40:·616e·2063·6c61·7373·3d22·6770·223e·496e··an·class="gp">In		00001d40:·616e·2063·6c61·7373·3d22·6770·223e·496e··an·class="gp">In
00001d50:·205b·365d·3a20·3c2f·7370·616e·3e3c·7370···[6]:·</span><sp		00001d50:·205b·365d·3a20·3c2f·7370·616e·3e3c·7370···[6]:·</span><sp
00001d60:·616e·2063·6c61·7373·3d22·6e22·3e65·6c3c··an·class="n">el<		00001d60:·616e·2063·6c61·7373·3d22·6e22·3e65·6c3c··an·class="n">el<
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00002400:·7461·744d·562e·6874·6d6c·2373·7461·7473··tatMV.html#stats		00002400:·6174·4d56·2e68·746d·6c23·7374·6174·736d··atMV.html#statsm
00002410:·6d6f·6465·6c73·2e65·6d70·6c69·6b65·2e64··models.emplike.d		00002410:·6f64·656c·732e·656d·706c·696b·652e·6465··odels.emplike.de
00002420:·6573·6372·6970·7469·7665·2e44·6573·6353··escriptive.DescS		00002420:·7363·7269·7074·6976·652e·4465·7363·5374··scriptive.DescSt
00002430:·7461·744d·5622·2074·6974·6c65·3d22·7374··tatMV"·title="st		00002430:·6174·4d56·2220·7469·746c·653d·2273·7461··atMV"·title="sta
00002440:·6174·736d·6f64·656c·732e·656d·706c·696b··atsmodels.emplik		00002440:·7473·6d6f·6465·6c73·2e65·6d70·6c69·6b65··tsmodels.emplike
00002450:·652e·6465·7363·7269·7074·6976·652e·4465··e.descriptive.De		00002450:·2e64·6573·6372·6970·7469·7665·2e44·6573··.descriptive.Des
00002460:·7363·5374·6174·4d56·223e·3c63·6f64·6520··scStatMV"><code·		00002460:·6353·7461·744d·5622·3e3c·636f·6465·2063··cStatMV"><code·c
00002470:·636c·6173·733d·2278·7265·6620·7079·2070··class="xref·py·p		00002470:·6c61·7373·3d22·7872·6566·2070·7920·7079··lass="xref·py·py
00002480:·792d·6f62·6a20·646f·6375·7469·6c73·206c··y-obj·docutils·l		00002480:·2d6f·626a·2064·6f63·7574·696c·7320·6c69··-obj·docutils·li
00002490:·6974·6572·616c·206e·6f74·7261·6e73·6c61··iteral·notransla		00002490:·7465·7261·6c20·6e6f·7472·616e·736c·6174··teral·notranslat
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00002830:·643a·2020·2020·2020·2020·2020·2020·2020··d:··············		00002830:·643a·2020·2020·2020·2020·2020·2020·2020··d:··············
00002840:·2020·204c·6561·7374·2053·7175·6172·6573·····Least·Squares		00002840:·2020·204c·6561·7374·2053·7175·6172·6573·····Least·Squares
00002850:·2020·2046·2d73·7461·7469·7374·6963·3a20·····F-statistic:·		00002850:·2020·2046·2d73·7461·7469·7374·6963·3a20·····F-statistic:·
00002860:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002860:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002870:·2020·2020·362e·3633·363c·2f73·7061·6e3e······6.636</span>		00002870:·2020·2020·362e·3633·363c·2f73·7061·6e3e······6.636</span>
00002880:·0a3c·7370·616e·2063·6c61·7373·3d22·676f··.<span·class="go		00002880:·0a3c·7370·616e·2063·6c61·7373·3d22·676f··.<span·class="go
00002890:·223e·4461·7465·3a20·2020·2020·2020·2020··">Date:·········		00002890:·223e·4461·7465·3a20·2020·2020·2020·2020··">Date:·········
000028a0:·2020·2020·2020·2057·6564·2c20·3130·204a·········~~Wed~~,·10·J		000028a0:·2020·2020·2020·2046·7269·2c20·3132·204a·········Fri,·12·J
000028b0:·756e·2032·3032·3020·2020·5072·6f62·2028··un·2020···Prob·(		000028b0:·756e·2032·3032·3020·2020·5072·6f62·2028··un·2020···Prob·(
000028c0:·462d·7374·6174·6973·7469·6329·3a20·2020··F-statistic):···		000028c0:·462d·7374·6174·6973·7469·6329·3a20·2020··F-statistic):···
000028d0:·2020·2020·2020·2020·312e·3037·652d·3035··········1.07e-05		000028d0:·2020·2020·2020·2020·312e·3037·652d·3035··········1.07e-05
000028e0:·3c2f·7370·616e·3e0a·3c73·7061·6e20·636c··</span>.<span·cl		000028e0:·3c2f·7370·616e·3e0a·3c73·7061·6e20·636c··</span>.<span·cl
000028f0:·6173·733d·2267·6f22·3e54·696d·653a·2020··ass="go">Time:··		000028f0:·6173·733d·2267·6f22·3e54·696d·653a·2020··ass="go">Time:··
00002900:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002900:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002910:·2020·2020·2020·3233·3a33·373a·3231·2020········23:37:21··		00002910:·2020·2020·2020·3037·3a34·383a·3130·2020········07:48:10··
00002920:·204c·6f67·2d4c·696b·656c·6968·6f6f·643a···Log-Likelihood:		00002920:·204c·6f67·2d4c·696b·656c·6968·6f6f·643a···Log-Likelihood:
00002930:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002930:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002940:·2d33·3735·2e33·303c·2f73·7061·6e3e·0a3c··-375.30</span>.<		00002940:·2d33·3735·2e33·303c·2f73·7061·6e3e·0a3c··-375.30</span>.<
00002950:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">		00002950:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">
00002960:·4e6f·2e20·4f62·7365·7276·6174·696f·6e73··No.·Observations		00002960:·4e6f·2e20·4f62·7365·7276·6174·696f·6e73··No.·Observations
00002970:·3a20·2020·2020·2020·2020·2020·2020·2020··:···············		00002970:·3a20·2020·2020·2020·2020·2020·2020·2020··:···············
00002980:·2020·2038·3520·2020·4149·433a·2020·2020·····85···AIC:····		00002980:·2020·2038·3520·2020·4149·433a·2020·2020·····85···AIC:····
Offset 1942, 22 lines modified		Offset 1942, 22 lines modified
00007950:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007950:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007960:·4c65·6173·7420·5371·7561·7265·7320·2020··Least·Squares···		00007960:·4c65·6173·7420·5371·7561·7265·7320·2020··Least·Squares···
00007970:·462d·7374·6174·6973·7469·633a·2020·2020··F-statistic:····		00007970:·462d·7374·6174·6973·7469·633a·2020·2020··F-statistic:····
00007980:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007980:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007990:·2031·322e·3036·3c2f·7370·616e·3e0a·3c73···12.06</span>.<s		00007990:·2031·322e·3036·3c2f·7370·616e·3e0a·3c73···12.06</span>.<s
000079a0:·7061·6e20·636c·6173·733d·2267·6f22·3e44··pan·class="go">D		000079a0:·7061·6e20·636c·6173·733d·2267·6f22·3e44··pan·class="go">D
000079b0:·6174·653a·2020·2020·2020·2020·2020·2020··ate:············		000079b0:·6174·653a·2020·2020·2020·2020·2020·2020··ate:············
000079c0:·2020·2020·~~5765~~·642c·2031·3020·4a75·6e20······~~Wed~~,·10·Jun·		000079c0:·2020·2020·4672·692c·2031·3220·4a75·6e20······Fri,·12·Jun·
000079d0:·3230·3230·2020·2050·726f·6220·2846·2d73··2020···Prob·(F-s		000079d0:·3230·3230·2020·2050·726f·6220·2846·2d73··2020···Prob·(F-s
000079e0:·7461·7469·7374·6963·293a·2020·2020·2020··tatistic):······		000079e0:·7461·7469·7374·6963·293a·2020·2020·2020··tatistic):······
000079f0:·2020·2020·2031·2e33·3265·2d30·363c·2f73·······1.32e-06</s		000079f0:·2020·2020·2031·2e33·3265·2d30·363c·2f73·······1.32e-06</s
00007a00:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class		00007a00:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class
00007a10:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····		00007a10:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····
00007a20:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007a20:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007a30:·2020·2032·333a·3337·3a32·3420·2020·4c6f·····23:37:24···Lo		00007a30:·2020·2030·373a·3438·3a31·3120·2020·4c6f·····07:48:11···Lo
00007a40:·672d·4c69·6b65·6c69·686f·6f64·3a20·2020··g-Likelihood:···		00007a40:·672d·4c69·6b65·6c69·686f·6f64·3a20·2020··g-Likelihood:···
00007a50:·2020·2020·2020·2020·2020·2020·202d·3337···············-37		00007a50:·2020·2020·2020·2020·2020·2020·202d·3337···············-37
00007a60:·372e·3133·3c2f·7370·616e·3e0a·3c73·7061··7.13</span>.<spa		00007a60:·372e·3133·3c2f·7370·616e·3e0a·3c73·7061··7.13</span>.<spa
00007a70:·6e20·636c·6173·733d·2267·6f22·3e4e·6f2e··n·class="go">No.		00007a70:·6e20·636c·6173·733d·2267·6f22·3e4e·6f2e··n·class="go">No.
00007a80:·204f·6273·6572·7661·7469·6f6e·733a·2020···Observations:··		00007a80:·204f·6273·6572·7661·7469·6f6e·733a·2020···Observations:··
00007a90:·2020·2020·2020·2020·2020·2020·2020·2020··················		00007a90:·2020·2020·2020·2020·2020·2020·2020·2020··················
00007aa0:·3835·2020·2041·4943·3a20·2020·2020·2020··85···AIC:·······		00007aa0:·3835·2020·2041·4943·3a20·2020·2020·2020··85···AIC:·······


Offset 13432, 16 lines modified		Offset 13432, 16 lines modified

13432	<div·class="output_subarea·output_stream·output_stdout·output_text">	13432	<div·class="output_subarea·output_stream·output_stdout·output_text">
13433	<pre>····························WLS·Regression·Results····························	13433	<pre>····························WLS·Regression·Results····························
13434	==============================================================================	13434	==============================================================================
13435	Dep.·Variable:······················y···R-squared:·······················0.400	13435	Dep.·Variable:······················y···R-squared:·······················0.400
13436	Model:····························WLS···Adj.·R-squared:··················0.367	13436	Model:····························WLS···Adj.·R-squared:··················0.367
13437	Method:·················Least·Squares···F-statistic:·····················193.5	13437	Method:·················Least·Squares···F-statistic:·····················193.5
13438	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········4.52e-11	13438	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········4.52e-11
13439	Time:························23:14:10···Log-Likelihood:················-119.06	13439	Time:························07:39:38···Log-Likelihood:················-119.06
13440	No.·Observations:··················20···AIC:·····························242.1	13440	No.·Observations:··················20···AIC:·····························242.1
13441	Df·Residuals:······················18···BIC:·····························244.1	13441	Df·Residuals:······················18···BIC:·····························244.1
13442	Df·Model:···························1·········································	13442	Df·Model:···························1·········································
13443	Covariance·Type:··········fixed·scale·········································	13443	Covariance·Type:··········fixed·scale·········································
13444	==============================================================================	13444	==============================================================================
13445	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13445	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13446	------------------------------------------------------------------------------	13446	------------------------------------------------------------------------------


Offset 13527, 16 lines modified		Offset 13527, 16 lines modified

13527	<div·class="output_subarea·output_stream·output_stdout·output_text">	13527	<div·class="output_subarea·output_stream·output_stdout·output_text">
13528	<pre>···························Logit·Regression·Results···························	13528	<pre>···························Logit·Regression·Results···························
13529	==============================================================================	13529	==============================================================================
13530	Dep.·Variable:·················affair···No.·Observations:·················6366	13530	Dep.·Variable:·················affair···No.·Observations:·················6366
13531	Model:··························Logit···Df·Residuals:·····················6357	13531	Model:··························Logit···Df·Residuals:·····················6357
13532	Method:···························MLE···Df·Model:····························8	13532	Method:···························MLE···Df·Model:····························8
13533	Date:················~~Wed~~,·10·Jun·2020···Pseudo·R-squ.:··················0.1327	13533	Date:················Fri,·12·Jun·2020···Pseudo·R-squ.:··················0.1327
13534	Time:························23:25:11···Log-Likelihood:················-3471.5	13534	Time:························07:45:09···Log-Likelihood:················-3471.5
13535	converged:·······················True···LL-Null:·······················-4002.5	13535	converged:·······················True···LL-Null:·······················-4002.5
13536	········································LLR·p-value:················5.807e-224	13536	········································LLR·p-value:················5.807e-224
13537	===================================================================================	13537	===================================================================================
13538	······················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13538	······················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13539	-----------------------------------------------------------------------------------	13539	-----------------------------------------------------------------------------------
13540	Intercept···········3.7257······0.299·····12.470······0.000·······3.140·······4.311	13540	Intercept···········3.7257······0.299·····12.470······0.000·······3.140·······4.311
13541	occupation··········0.1602······0.034······4.717······0.000·······0.094·······0.227	13541	occupation··········0.1602······0.034······4.717······0.000·······0.094·······0.227
Offset 14422, 16 lines modified		Offset 14422, 16 lines modified
14422	<pre>··················Generalized·Linear·Model·Regression·Results···················	14422	<pre>··················Generalized·Linear·Model·Regression·Results···················
14423	================================================================================	14423	================================================================================
14424	Dep.·Variable:·····['NABOVE',·'NBELOW']···No.·Observations:··················303	14424	Dep.·Variable:·····['NABOVE',·'NBELOW']···No.·Observations:··················303
14425	Model:······························GLM···Df·Residuals:······················282	14425	Model:······························GLM···Df·Residuals:······················282
14426	Model·Family:··················Binomial···Df·Model:···························20	14426	Model·Family:··················Binomial···Df·Model:···························20
14427	Link·Function:····················logit···Scale:·····························1.0	14427	Link·Function:····················logit···Scale:·····························1.0
14428	Method:····························IRLS···Log-Likelihood:················-2998.6	14428	Method:····························IRLS···Log-Likelihood:················-2998.6
14429	Date:··················~~Wed~~,·10·Jun·2020···Deviance:·······················4078.8	14429	Date:··················Fri,·12·Jun·2020···Deviance:·······················4078.8
14430	Time:··························23:25:38···Pearson·chi2:·····················9.60	14430	Time:··························07:45:19···Pearson·chi2:·····················9.60
14431	No.·Iterations:·······················5·········································	14431	No.·Iterations:·······················5·········································
14432	============================================================================================	14432	============================================================================================
14433	·······························coef····std·err··········z······P>\|z\|······[0.025······0.975]	14433	·······························coef····std·err··········z······P>\|z\|······[0.025······0.975]
14434	--------------------------------------------------------------------------------------------	14434	--------------------------------------------------------------------------------------------
14435	Intercept····················2.9589······1.547······1.913······0.056······-0.073·······5.990	14435	Intercept····················2.9589······1.547······1.913······0.056······-0.073·······5.990
14436	LOWINC······················-0.0168······0.000····-38.749······0.000······-0.018······-0.016	14436	LOWINC······················-0.0168······0.000····-38.749······0.000······-0.018······-0.016
14437	PERASIAN·····················0.0099······0.001·····16.505······0.000·······0.009·······0.011	14437	PERASIAN·····················0.0099······0.001·····16.505······0.000·······0.009·······0.011


Offset 13487, 16 lines modified		Offset 13487, 16 lines modified

13487	<div·class="output_subarea·output_stream·output_stdout·output_text">	13487	<div·class="output_subarea·output_stream·output_stdout·output_text">
13488	<pre>···························Logit·Regression·Results···························	13488	<pre>···························Logit·Regression·Results···························
13489	==============================================================================	13489	==============================================================================
13490	Dep.·Variable:······················y···No.·Observations:···················32	13490	Dep.·Variable:······················y···No.·Observations:···················32
13491	Model:··························Logit···Df·Residuals:·······················28	13491	Model:··························Logit···Df·Residuals:·······················28
13492	Method:···························MLE···Df·Model:····························3	13492	Method:···························MLE···Df·Model:····························3
13493	Date:················~~Wed~~,·10·Jun·2020···Pseudo·R-squ.:··················0.3740	13493	Date:················Fri,·12·Jun·2020···Pseudo·R-squ.:··················0.3740
13494	Time:························23:14:21···Log-Likelihood:················-12.890	13494	Time:························07:44:25···Log-Likelihood:················-12.890
13495	converged:·······················True···LL-Null:·······················-20.592	13495	converged:·······················True···LL-Null:·······················-20.592
13496	········································LLR·p-value:··················0.001502	13496	········································LLR·p-value:··················0.001502
13497	==============================================================================	13497	==============================================================================
13498	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13498	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13499	------------------------------------------------------------------------------	13499	------------------------------------------------------------------------------
13500	x1·············2.8261······1.263······2.238······0.025·······0.351·······5.301	13500	x1·············2.8261······1.263······2.238······0.025·······0.351·······5.301
13501	x2·············0.0952······0.142······0.672······0.501······-0.182·······0.373	13501	x2·············0.0952······0.142······0.672······0.501······-0.182·······0.373
Offset 13797, 16 lines modified		Offset 13797, 16 lines modified
13797	·········Current·function·value:·3.091609	13797	·········Current·function·value:·3.091609
13798	·········Iterations·12	13798	·········Iterations·12
13799	··························Poisson·Regression·Results··························	13799	··························Poisson·Regression·Results··························
13800	==============================================================================	13800	==============================================================================
13801	Dep.·Variable:······················y···No.·Observations:················20190	13801	Dep.·Variable:······················y···No.·Observations:················20190
13802	Model:························Poisson···Df·Residuals:····················20180	13802	Model:························Poisson···Df·Residuals:····················20180
13803	Method:···························MLE···Df·Model:····························9	13803	Method:···························MLE···Df·Model:····························9
13804	Date:················~~Wed~~,·10·Jun·2020···Pseudo·R-squ.:·················0.06343	13804	Date:················Fri,·12·Jun·2020···Pseudo·R-squ.:·················0.06343
13805	Time:························23:14:39···Log-Likelihood:················-62420.	13805	Time:························07:44:33···Log-Likelihood:················-62420.
13806	converged:·······················True···LL-Null:·······················-66647.	13806	converged:·······················True···LL-Null:·······················-66647.
13807	········································LLR·p-value:·····················0.000	13807	········································LLR·p-value:·····················0.000
13808	==============================================================================	13808	==============================================================================
13809	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13809	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13810	------------------------------------------------------------------------------	13810	------------------------------------------------------------------------------
13811	x1············-0.0525······0.003····-18.216······0.000······-0.058······-0.047	13811	x1············-0.0525······0.003····-18.216······0.000······-0.058······-0.047
13812	x2············-0.2471······0.011····-23.272······0.000······-0.268······-0.226	13812	x2············-0.2471······0.011····-23.272······0.000······-0.268······-0.226
Offset 13872, 16 lines modified		Offset 13872, 16 lines modified

13872	<div·class="output_subarea·output_stream·output_stdout·output_text">	13872	<div·class="output_subarea·output_stream·output_stdout·output_text">
13873	<pre>·····················NegativeBinomial·Regression·Results······················	13873	<pre>·····················NegativeBinomial·Regression·Results······················
13874	==============================================================================	13874	==============================================================================
13875	Dep.·Variable:······················y···No.·Observations:················20190	13875	Dep.·Variable:······················y···No.·Observations:················20190
13876	Model:···············NegativeBinomial···Df·Residuals:····················20180	13876	Model:···············NegativeBinomial···Df·Residuals:····················20180
13877	Method:···························MLE···Df·Model:····························9	13877	Method:···························MLE···Df·Model:····························9
13878	Date:················~~Wed~~,·10·Jun·2020···Pseudo·R-squ.:·················0.01845	13878	Date:················Fri,·12·Jun·2020···Pseudo·R-squ.:·················0.01845
13879	Time:························23:15:04···Log-Likelihood:················-43384.	13879	Time:························07:44:47···Log-Likelihood:················-43384.
13880	converged:······················False···LL-Null:·······················-44199.	13880	converged:······················False···LL-Null:·······················-44199.
13881	········································LLR·p-value:·····················0.000	13881	········································LLR·p-value:·····················0.000
13882	==============================================================================	13882	==============================================================================
13883	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13883	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13884	------------------------------------------------------------------------------	13884	------------------------------------------------------------------------------
13885	x1············-0.0580······0.006·····-9.517······0.000······-0.070······-0.046	13885	x1············-0.0580······0.006·····-9.517······0.000······-0.070······-0.046
13886	x2············-0.2678······0.023····-11.802······0.000······-0.312······-0.223	13886	x2············-0.2678······0.023····-11.802······0.000······-0.312······-0.223
Offset 13962, 16 lines modified		Offset 13962, 16 lines modified

13962	<div·class="output_subarea·output_stream·output_stdout·output_text">	13962	<div·class="output_subarea·output_stream·output_stdout·output_text">
13963	<pre>··························MNLogit·Regression·Results··························	13963	<pre>··························MNLogit·Regression·Results··························
13964	==============================================================================	13964	==============================================================================
13965	Dep.·Variable:······················y···No.·Observations:··················944	13965	Dep.·Variable:······················y···No.·Observations:··················944
13966	Model:························MNLogit···Df·Residuals:······················908	13966	Model:························MNLogit···Df·Residuals:······················908
13967	Method:···························MLE···Df·Model:···························30	13967	Method:···························MLE···Df·Model:···························30
13968	Date:················~~Wed~~,·10·Jun·2020···Pseudo·R-squ.:··················0.1648	13968	Date:················Fri,·12·Jun·2020···Pseudo·R-squ.:··················0.1648
13969	Time:························23:15:09···Log-Likelihood:················-1461.9	13969	Time:························07:44:49···Log-Likelihood:················-1461.9
13970	converged:······················False···LL-Null:·······················-1750.3	13970	converged:······················False···LL-Null:·······················-1750.3
13971	········································LLR·p-value:················1.827e-102	13971	········································LLR·p-value:················1.827e-102
13972	==============================================================================	13972	==============================================================================
13973	·······y=1·······coef····std·err··········z······P>\|z\|······[0.025······0.975]	13973	·······y=1·······coef····std·err··········z······P>\|z\|······[0.025······0.975]
13974	------------------------------------------------------------------------------	13974	------------------------------------------------------------------------------
13975	x1············-0.0116······0.034·····-0.338······0.735······-0.079·······0.056	13975	x1············-0.0116······0.034·····-0.338······0.735······-0.079·······0.056
13976	x2·············0.2973······0.094······3.175······0.001·······0.114·······0.481	13976	x2·············0.2973······0.094······3.175······0.001·······0.114·······0.481


Offset 13540, 16 lines modified		Offset 13540, 16 lines modified

13540	<div·class="output_subarea·output_stream·output_stdout·output_text">	13540	<div·class="output_subarea·output_stream·output_stdout·output_text">
13541	<pre>····························OLS·Regression·Results····························	13541	<pre>····························OLS·Regression·Results····························
13542	==============================================================================	13542	==============================================================================
13543	Dep.·Variable:················Lottery···R-squared:·······················0.338	13543	Dep.·Variable:················Lottery···R-squared:·······················0.338
13544	Model:····························OLS···Adj.·R-squared:··················0.287	13544	Model:····························OLS···Adj.·R-squared:··················0.287
13545	Method:·················Least·Squares···F-statistic:·····················6.636	13545	Method:·················Least·Squares···F-statistic:·····················6.636
13546	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········1.07e-05	13546	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········1.07e-05
13547	Time:························23:29:34···Log-Likelihood:················-375.30	13547	Time:························07:44:05···Log-Likelihood:················-375.30
13548	No.·Observations:··················85···AIC:·····························764.6	13548	No.·Observations:··················85···AIC:·····························764.6
13549	Df·Residuals:······················78···BIC:·····························781.7	13549	Df·Residuals:······················78···BIC:·····························781.7
13550	Df·Model:···························6·········································	13550	Df·Model:···························6·········································
13551	Covariance·Type:············nonrobust·········································	13551	Covariance·Type:············nonrobust·········································
13552	===============================================================================	13552	===============================================================================
13553	··················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13553	··················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13554	-------------------------------------------------------------------------------	13554	-------------------------------------------------------------------------------
Offset 13963, 16 lines modified		Offset 13963, 16 lines modified

13963	<div·class="output_subarea·output_stream·output_stdout·output_text">	13963	<div·class="output_subarea·output_stream·output_stdout·output_text">
13964	<pre>····························OLS·Regression·Results····························	13964	<pre>····························OLS·Regression·Results····························
13965	==============================================================================	13965	==============================================================================
13966	Dep.·Variable:················Lottery···R-squared:·······················0.309	13966	Dep.·Variable:················Lottery···R-squared:·······················0.309
13967	Model:····························OLS···Adj.·R-squared:··················0.283	13967	Model:····························OLS···Adj.·R-squared:··················0.283
13968	Method:·················Least·Squares···F-statistic:·····················12.06	13968	Method:·················Least·Squares···F-statistic:·····················12.06
13969	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········1.32e-06	13969	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········1.32e-06
13970	Time:························23:29:38···Log-Likelihood:················-377.13	13970	Time:························07:44:06···Log-Likelihood:················-377.13
13971	No.·Observations:··················85···AIC:·····························762.3	13971	No.·Observations:··················85···AIC:·····························762.3
13972	Df·Residuals:······················81···BIC:·····························772.0	13972	Df·Residuals:······················81···BIC:·····························772.0
13973	Df·Model:···························3·········································	13973	Df·Model:···························3·········································
13974	Covariance·Type:············nonrobust·········································	13974	Covariance·Type:············nonrobust·········································
13975	===================================================================================	13975	===================================================================================
13976	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13976	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13977	-----------------------------------------------------------------------------------	13977	-----------------------------------------------------------------------------------


Offset 13406, 16 lines modified		Offset 13406, 16 lines modified
13406	·········Iterations:·292	13406	·········Iterations:·292
13407	·········Function·evaluations:·494	13407	·········Function·evaluations:·494
13408	·······························MyProbit·Results·······························	13408	·······························MyProbit·Results·······························
13409	==============================================================================	13409	==============================================================================
13410	Dep.·Variable:··················GRADE···Log-Likelihood:················-12.819	13410	Dep.·Variable:··················GRADE···Log-Likelihood:················-12.819
13411	Model:·······················MyProbit···AIC:·····························33.64	13411	Model:·······················MyProbit···AIC:·····························33.64
13412	Method:············Maximum·Likelihood···BIC:·····························39.50	13412	Method:············Maximum·Likelihood···BIC:·····························39.50
13413	Date:················~~Wed~~,·10·Jun·2020·········································	13413	Date:················Fri,·12·Jun·2020·········································
13414	Time:························23:18:48·········································	13414	Time:························07:43:13·········································
13415	No.·Observations:··················32·········································	13415	No.·Observations:··················32·········································
13416	Df·Residuals:······················28·········································	13416	Df·Residuals:······················28·········································
13417	Df·Model:···························3·········································	13417	Df·Model:···························3·········································
13418	==============================================================================	13418	==============================================================================
13419	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13419	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13420	------------------------------------------------------------------------------	13420	------------------------------------------------------------------------------
13421	const·········-7.4523······2.542·····-2.931······0.003·····-12.435······-2.469	13421	const·········-7.4523······2.542·····-2.931······0.003·····-12.435······-2.469
Offset 13970, 16 lines modified		Offset 13970, 16 lines modified

13970	<div·class="output_subarea·output_stream·output_stdout·output_text">	13970	<div·class="output_subarea·output_stream·output_stdout·output_text">
13971	<pre>·································NBin·Results·································	13971	<pre>·································NBin·Results·································
13972	==============================================================================	13972	==============================================================================
13973	Dep.·Variable:····················los···Log-Likelihood:················-4797.5	13973	Dep.·Variable:····················los···Log-Likelihood:················-4797.5
13974	Model:···························NBin···AIC:·····························9605.	13974	Model:···························NBin···AIC:·····························9605.
13975	Method:············Maximum·Likelihood···BIC:·····························9632.	13975	Method:············Maximum·Likelihood···BIC:·····························9632.
13976	Date:················~~Wed~~,·10·Jun·2020·········································	13976	Date:················Fri,·12·Jun·2020·········································
13977	Time:························23:19:25·········································	13977	Time:························07:43:26·········································
13978	No.·Observations:················1495·········································	13978	No.·Observations:················1495·········································
13979	Df·Residuals:····················1490·········································	13979	Df·Residuals:····················1490·········································
13980	Df·Model:···························4·········································	13980	Df·Model:···························4·········································
13981	==============================================================================	13981	==============================================================================
13982	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13982	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13983	------------------------------------------------------------------------------	13983	------------------------------------------------------------------------------
13984	type2··········0.2213······0.051······4.373······0.000·······0.122·······0.320	13984	type2··········0.2213······0.051······4.373······0.000·······0.122·······0.320
Offset 14036, 16 lines modified		Offset 14036, 16 lines modified

14036	<div·class="output_subarea·output_stream·output_stdout·output_text">	14036	<div·class="output_subarea·output_stream·output_stdout·output_text">
14037	<pre>·····················NegativeBinomial·Regression·Results······················	14037	<pre>·····················NegativeBinomial·Regression·Results······················
14038	==============================================================================	14038	==============================================================================
14039	Dep.·Variable:····················los···No.·Observations:·················1495	14039	Dep.·Variable:····················los···No.·Observations:·················1495
14040	Model:···············NegativeBinomial···Df·Residuals:·····················1490	14040	Model:···············NegativeBinomial···Df·Residuals:·····················1490
14041	Method:···························MLE···Df·Model:····························4	14041	Method:···························MLE···Df·Model:····························4
14042	Date:················~~Wed~~,·10·Jun·2020···Pseudo·R-squ.:·················0.01215	14042	Date:················Fri,·12·Jun·2020···Pseudo·R-squ.:·················0.01215
14043	Time:························23:19:27···Log-Likelihood:················-4797.5	14043	Time:························07:43:27···Log-Likelihood:················-4797.5
14044	converged:·······················True···LL-Null:·······················-4856.5	14044	converged:·······················True···LL-Null:·······················-4856.5
14045	········································LLR·p-value:·················1.404e-24	14045	········································LLR·p-value:·················1.404e-24
14046	==============================================================================	14046	==============================================================================
14047	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	14047	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
14048	------------------------------------------------------------------------------	14048	------------------------------------------------------------------------------
14049	type2··········0.2212······0.051······4.373······0.000·······0.122·······0.320	14049	type2··········0.2212······0.051······4.373······0.000·······0.122·······0.320
14050	type3··········0.7062······0.076······9.276······0.000·······0.557·······0.855	14050	type3··········0.7062······0.076······9.276······0.000·······0.557·······0.855


Offset 13493, 16 lines modified		Offset 13493, 16 lines modified
13493	<pre>·················Generalized·Linear·Model·Regression·Results··················	13493	<pre>·················Generalized·Linear·Model·Regression·Results··················
13494	==============================================================================	13494	==============================================================================
13495	Dep.·Variable:···········['y1',·'y2']···No.·Observations:··················303	13495	Dep.·Variable:···········['y1',·'y2']···No.·Observations:··················303
13496	Model:····························GLM···Df·Residuals:······················282	13496	Model:····························GLM···Df·Residuals:······················282
13497	Model·Family:················Binomial···Df·Model:···························20	13497	Model·Family:················Binomial···Df·Model:···························20
13498	Link·Function:··················logit···Scale:·····························1.0	13498	Link·Function:··················logit···Scale:·····························1.0
13499	Method:··························IRLS···Log-Likelihood:················-2998.6	13499	Method:··························IRLS···Log-Likelihood:················-2998.6
13500	Date:················~~Wed~~,·10·Jun·2020···Deviance:·······················4078.8	13500	Date:················Fri,·12·Jun·2020···Deviance:·······················4078.8
13501	Time:························23:27:06···Pearson·chi2:·····················9.60	13501	Time:························07:38:59···Pearson·chi2:·····················9.60
13502	No.·Iterations:·····················5·········································	13502	No.·Iterations:·····················5·········································
13503	==============================================================================	13503	==============================================================================
13504	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13504	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13505	------------------------------------------------------------------------------	13505	------------------------------------------------------------------------------
13506	x1············-0.0168······0.000····-38.749······0.000······-0.018······-0.016	13506	x1············-0.0168······0.000····-38.749······0.000······-0.018······-0.016
13507	x2·············0.0099······0.001·····16.505······0.000·······0.009·······0.011	13507	x2·············0.0099······0.001·····16.505······0.000·······0.009·······0.011
13508	x3············-0.0187······0.001····-25.182······0.000······-0.020······-0.017	13508	x3············-0.0187······0.001····-25.182······0.000······-0.020······-0.017
Offset 14093, 16 lines modified		Offset 14093, 16 lines modified
14093	<pre>···················Generalized·Linear·Model·Regression·Results···················	14093	<pre>···················Generalized·Linear·Model·Regression·Results···················
14094	=================================================================================	14094	=================================================================================
14095	Dep.·Variable:······················y···No.·Observations:······················32	14095	Dep.·Variable:······················y···No.·Observations:······················32
14096	Model:····························GLM···Df·Residuals:··························24	14096	Model:····························GLM···Df·Residuals:··························24
14097	Model·Family:···················Gamma···Df·Model:·······························7	14097	Model·Family:···················Gamma···Df·Model:·······························7
14098	Link·Function:··········inverse_power···Scale:··············0.0035842831734956906	14098	Link·Function:··········inverse_power···Scale:··············0.0035842831734956906
14099	Method:··························IRLS···Log-Likelihood:···················-83.017	14099	Method:··························IRLS···Log-Likelihood:···················-83.017
14100	Date:················~~Wed~~,·10·Jun·2020···Deviance:························0.087389	14100	Date:················Fri,·12·Jun·2020···Deviance:························0.087389
14101	Time:························23:27:31···Pearson·chi2:······················0.0860	14101	Time:························07:39:08···Pearson·chi2:······················0.0860
14102	No.·Iterations:·····················4············································	14102	No.·Iterations:·····················4············································
14103	==============================================================================	14103	==============================================================================
14104	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	14104	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
14105	------------------------------------------------------------------------------	14105	------------------------------------------------------------------------------
14106	x1··········4.962e-05···1.62e-05······3.060······0.002····1.78e-05····8.14e-05	14106	x1··········4.962e-05···1.62e-05······3.060······0.002····1.78e-05····8.14e-05
14107	x2·············0.0020······0.001······3.824······0.000·······0.001·······0.003	14107	x2·············0.0020······0.001······3.824······0.000·······0.001·······0.003
14108	x3·········-7.181e-05···2.71e-05·····-2.648······0.008······-0.000···-1.87e-05	14108	x3·········-7.181e-05···2.71e-05·····-2.648······0.008······-0.000···-1.87e-05
Offset 14179, 16 lines modified		Offset 14179, 16 lines modified
14179	<pre>···················Generalized·Linear·Model·Regression·Results···················	14179	<pre>···················Generalized·Linear·Model·Regression·Results···················
14180	=================================================================================	14180	=================================================================================
14181	Dep.·Variable:······················y···No.·Observations:·····················100	14181	Dep.·Variable:······················y···No.·Observations:·····················100
14182	Model:····························GLM···Df·Residuals:··························97	14182	Model:····························GLM···Df·Residuals:··························97
14183	Model·Family:················Gaussian···Df·Model:·······························2	14183	Model·Family:················Gaussian···Df·Model:·······························2
14184	Link·Function:····················log···Scale:··············1.053114255880704e-07	14184	Link·Function:····················log···Scale:··············1.053114255880704e-07
14185	Method:··························IRLS···Log-Likelihood:····················662.92	14185	Method:··························IRLS···Log-Likelihood:····················662.92
14186	Date:················~~Wed~~,·10·Jun·2020···Deviance:······················1.0215e-05	14186	Date:················Fri,·12·Jun·2020···Deviance:······················1.0215e-05
14187	Time:························23:27:32···Pearson·chi2:····················1.02e-05	14187	Time:························07:39:09···Pearson·chi2:····················1.02e-05
14188	No.·Iterations:·····················5············································	14188	No.·Iterations:·····················5············································
14189	==============================================================================	14189	==============================================================================
14190	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	14190	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
14191	------------------------------------------------------------------------------	14191	------------------------------------------------------------------------------
14192	x1············-0.0300····5.6e-06··-5361.333······0.000······-0.030······-0.030	14192	x1············-0.0300····5.6e-06··-5361.333······0.000······-0.030······-0.030
14193	x2·········-9.939e-05···1.05e-07···-951.097······0.000···-9.96e-05···-9.92e-05	14193	x2·········-9.939e-05···1.05e-07···-951.097······0.000···-9.96e-05···-9.92e-05
14194	const··········1.0003···5.39e-05···1.86e+04······0.000·······1.000·······1.000	14194	const··········1.0003···5.39e-05···1.86e+04······0.000·······1.000·······1.000


Offset 13303, 18 lines modified		Offset 13303, 18 lines modified
13303	<tr>	13303	<tr>
13304	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··	13304	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··
13305	</tr>	13305	</tr>
13306	<tr>	13306	<tr>
13307	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>	13307	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>
13308	</tr>	13308	</tr>
13309	<tr>	13309	<tr>
13310	··<th>Date:</th>···········<td>~~Wed~~,·10·Jun·2020</td>·<th>··Deviance:··········</th>·<td>··380.66</td>	13310	··<th>Date:</th>···········<td>Fri,·12·Jun·2020</td>·<th>··Deviance:··········</th>·<td>··380.66</td>
13311	</tr>	13311	</tr>
13312	<tr>	13312	<tr>
13313	··<th>Time:</th>···············<td>23:28:19</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·	13313	··<th>Time:</th>···············<td>07:45:35</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·
13314	</tr>	13314	</tr>
13315	<tr>	13315	<tr>
13316	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···	13316	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···
13317	</tr>	13317	</tr>
13318	</table>	13318	</table>
13319	<table·class="simpletable">	13319	<table·class="simpletable">
13320	<tr>	13320	<tr>
Offset 13442, 18 lines modified		Offset 13442, 18 lines modified
13442	<tr>	13442	<tr>
13443	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··	13443	··<th>Link·Function:</th>········<td>logit</td>······<th>··Scale:·············</th>····<td>1.0</td>··
13444	</tr>	13444	</tr>
13445	<tr>	13445	<tr>
13446	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>	13446	··<th>Method:</th>···············<td>IRLS</td>·······<th>··Log-Likelihood:····</th>·<td>·-189.70</td>
13447	</tr>	13447	</tr>
13448	<tr>	13448	<tr>
13449	··<th>Date:</th>···········<td>~~Wed~~,·10·Jun·2020</td>·<th>··Deviance:··········</th>·<td>··380.66</td>	13449	··<th>Date:</th>···········<td>Fri,·12·Jun·2020</td>·<th>··Deviance:··········</th>·<td>··380.66</td>
13450	</tr>	13450	</tr>
13451	<tr>	13451	<tr>
13452	··<th>Time:</th>···············<td>23:28:21</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·	13452	··<th>Time:</th>···············<td>07:45:36</td>·····<th>··Pearson·chi2:······</th>··<td>··8.48</td>·
13453	</tr>	13453	</tr>
13454	<tr>	13454	<tr>
13455	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···	13455	··<th>No.·Iterations:</th>·········<td>5</td>········<th>·····················</th>·····<td>·</td>···
13456	</tr>	13456	</tr>
13457	</table>	13457	</table>
13458	<table·class="simpletable">	13458	<table·class="simpletable">
13459	<tr>	13459	<tr>


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3392	-rw-r--r--···0·root·········(0)·root·········(0)····10118·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels-doc/html/anova.html	3392	-rw-r--r--···0·root·········(0)·root·········(0)····10118·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels-doc/html/anova.html
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00003870:·2020·2020·2020·2020·2020·2020·2020·203c·················<		00003870:·2020·2020·2020·2020·2020·2020·2020·2020··················
00003880:·2f73·7061·6e3e·5472·6163·6562·6163·6b20··/span>Traceba~~ck·~~		00003880:·2020·3c2f·7370·616e·3e54·7261·6365·6261····</span>Traceba
00003890:·286d·6f73·7420·7265·6365·6e74·2063·616c··(most·recent·~~cal~~		00003890:·636b·2028·6d6f·7374·2072·6563·656e·7420··ck·(most·recent·
000038a0:·6c20·6c61·7374·290a·3c73·7061·6e20·636c~~··~~l·last).<span~~·cl~~		000038a0:·6361·6c6c·206c·6173·7429·0a3c·7370·616e··call·last).<span


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13509	<pre>/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394:·UserWarning:·kurtosistest·only·valid·for·n>=20·...·continuing·anyway,·n=15
13510	··"anyway,·n=%i"·%·int(n))
13511	</pre>
13512	</div>
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13516	<div·class="output_subarea·output_stream·output_stdout·output_text">	13508	<div·class="output_subarea·output_stream·output_stdout·output_text">
13517	<pre>···························GLSAR·Regression·Results···························	13509	<pre>···························GLSAR·Regression·Results···························
13518	==============================================================================	13510	==============================================================================
13519	Dep.·Variable:······················y···R-squared:·······················0.996	13511	Dep.·Variable:······················y···R-squared:·······················0.996
13520	Model:··························GLSAR···Adj.·R-squared:··················0.992	13512	Model:··························GLSAR···Adj.·R-squared:··················0.992
13521	Method:·················Least·Squares···F-statistic:·····················295.2	13513	Method:·················Least·Squares···F-statistic:·····················295.2
13522	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········6.09e-09	13514	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········6.09e-09
13523	Time:························23:13:28···Log-Likelihood:················-102.04	13515	Time:························07:43:32···Log-Likelihood:················-102.04
13524	No.·Observations:··················15···AIC:·····························218.1	13516	No.·Observations:··················15···AIC:·····························218.1
13525	Df·Residuals:·······················8···BIC:·····························223.0	13517	Df·Residuals:·······················8···BIC:·····························223.0
13526	Df·Model:···························6·········································	13518	Df·Model:···························6·········································
13527	Covariance·Type:············nonrobust·········································	13519	Covariance·Type:············nonrobust·········································
13528	==============================================================================	13520	==============================================================================
13529	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13521	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13530	------------------------------------------------------------------------------	13522	------------------------------------------------------------------------------
Offset 13555, 14 lines modified		Offset 13543, 26 lines modified
13555	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.	13543	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.
13556	[2]·The·condition·number·is·large,·4.8e+09.·This·might·indicate·that·there·are	13544	[2]·The·condition·number·is·large,·4.8e+09.·This·might·indicate·that·there·are
13557	strong·multicollinearity·or·other·numerical·problems.	13545	strong·multicollinearity·or·other·numerical·problems.


Offset 13369, 18 lines modified		Offset 13369, 18 lines modified
13369	<tr>	13369	<tr>
13370	··<th>Dep.·Variable:</th>·············<td>y</td>··········<th>··No.·Observations:··</th>····<td>131</td>··	13370	··<th>Dep.·Variable:</th>·············<td>y</td>··········<th>··No.·Observations:··</th>····<td>131</td>··
13371	</tr>	13371	</tr>
13372	<tr>	13372	<tr>
13373	··<th>Model:</th>···········<td>MarkovAutoregression</td>·<th>··Log·Likelihood·····</th>·<td>-181.263</td>	13373	··<th>Model:</th>···········<td>MarkovAutoregression</td>·<th>··Log·Likelihood·····</th>·<td>-181.263</td>
13374	</tr>	13374	</tr>
13375	<tr>	13375	<tr>
13376	··<th>Date:</th>··············<td>~~Wed~~,·10·Jun·2020</td>···<th>··AIC················</th>··<td>380.527</td>	13376	··<th>Date:</th>··············<td>Fri,·12·Jun·2020</td>···<th>··AIC················</th>··<td>380.527</td>
13377	</tr>	13377	</tr>
13378	<tr>	13378	<tr>
13379	··<th>Time:</th>··················<td>23:27:03</td>·······<th>··BIC················</th>··<td>406.404</td>	13379	··<th>Time:</th>··················<td>07:40:29</td>·······<th>··BIC················</th>··<td>406.404</td>
13380	</tr>	13380	</tr>
13381	<tr>	13381	<tr>
13382	··<th>Sample:</th>···············<td>04-01-1952</td>······<th>··HQIC···············</th>··<td>391.042</td>	13382	··<th>Sample:</th>···············<td>04-01-1952</td>······<th>··HQIC···············</th>··<td>391.042</td>
13383	</tr>	13383	</tr>
13384	<tr>	13384	<tr>
13385	··<th></th>·····················<td>-·10-01-1984</td>·····<th>·····················</th>·····<td>·</td>···	13385	··<th></th>·····················<td>-·10-01-1984</td>·····<th>·····················</th>·····<td>·</td>···
13386	</tr>	13386	</tr>
Offset 13684, 15 lines modified		Offset 13684, 15 lines modified
13684	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>	13684	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>

13685	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>	13685	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>
13686	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(	13686	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(
13687	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)	13687	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)
13688	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·	13688	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·

13689	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xac5~~bed~~90>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused	13689	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xac5a7690>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused

13690	During·handling·of·the·above·exception,·another·exception·occurred:	13690	During·handling·of·the·above·exception,·another·exception·occurred:

13691	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)	13691	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)
13692	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13692	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13693	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>	13693	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>
13694	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout	13694	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout
Offset 13704, 15 lines modified		Offset 13704, 15 lines modified
13704	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>	13704	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>

13705	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>	13705	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>


Offset 13340, 18 lines modified		Offset 13340, 18 lines modified
13340	<tr>	13340	<tr>
13341	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>226</td>··	13341	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>226</td>··
13342	</tr>	13342	</tr>
13343	<tr>	13343	<tr>
13344	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-508.636</td>	13344	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-508.636</td>
13345	</tr>	13345	</tr>
13346	<tr>	13346	<tr>
13347	··<th>Date:</th>············<td>~~Wed~~,·10·Jun·2020</td>·<th>··AIC················</th>·<td>1027.272</td>	13347	··<th>Date:</th>············<td>Fri,·12·Jun·2020</td>·<th>··AIC················</th>·<td>1027.272</td>
13348	</tr>	13348	</tr>
13349	<tr>	13349	<tr>
13350	··<th>Time:</th>················<td>23:18:28</td>·····<th>··BIC················</th>·<td>1044.375</td>	13350	··<th>Time:</th>················<td>07:41:23</td>·····<th>··BIC················</th>·<td>1044.375</td>
13351	</tr>	13351	</tr>
13352	<tr>	13352	<tr>
13353	··<th>Sample:</th>·············<td>07-01-1954</td>····<th>··HQIC···············</th>·<td>1034.174</td>	13353	··<th>Sample:</th>·············<td>07-01-1954</td>····<th>··HQIC···············</th>·<td>1034.174</td>
13354	</tr>	13354	</tr>
13355	<tr>	13355	<tr>
13356	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13356	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13357	</tr>	13357	</tr>
Offset 13556, 18 lines modified		Offset 13556, 18 lines modified
13556	<tr>	13556	<tr>
13557	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>225</td>··	13557	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>225</td>··
13558	</tr>	13558	</tr>
13559	<tr>	13559	<tr>
13560	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-264.711</td>	13560	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-264.711</td>
13561	</tr>	13561	</tr>
13562	<tr>	13562	<tr>
13563	··<th>Date:</th>············<td>~~Wed~~,·10·Jun·2020</td>·<th>··AIC················</th>··<td>543.421</td>	13563	··<th>Date:</th>············<td>Fri,·12·Jun·2020</td>·<th>··AIC················</th>··<td>543.421</td>
13564	</tr>	13564	</tr>
13565	<tr>	13565	<tr>
13566	··<th>Time:</th>················<td>23:18:41</td>·····<th>··BIC················</th>··<td>567.334</td>	13566	··<th>Time:</th>················<td>07:41:27</td>·····<th>··BIC················</th>··<td>567.334</td>
13567	</tr>	13567	</tr>
13568	<tr>	13568	<tr>
13569	··<th>Sample:</th>·············<td>10-01-1954</td>····<th>··HQIC···············</th>··<td>553.073</td>	13569	··<th>Sample:</th>·············<td>10-01-1954</td>····<th>··HQIC···············</th>··<td>553.073</td>
13570	</tr>	13570	</tr>
13571	<tr>	13571	<tr>
13572	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13572	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13573	</tr>	13573	</tr>
Offset 13782, 18 lines modified		Offset 13782, 18 lines modified
13782	<tr>	13782	<tr>
13783	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··	13783	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··
13784	</tr>	13784	</tr>
13785	<tr>	13785	<tr>
13786	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-229.256</td>	13786	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-229.256</td>
13787	</tr>	13787	</tr>
13788	<tr>	13788	<tr>
13789	··<th>Date:</th>············<td>~~Wed~~,·10·Jun·2020</td>·<th>··AIC················</th>··<td>480.512</td>	13789	··<th>Date:</th>············<td>Fri,·12·Jun·2020</td>·<th>··AIC················</th>··<td>480.512</td>
13790	</tr>	13790	</tr>
13791	<tr>	13791	<tr>
13792	··<th>Time:</th>················<td>23:20:29</td>·····<th>··BIC················</th>··<td>517.942</td>	13792	··<th>Time:</th>················<td>07:42:05</td>·····<th>··BIC················</th>··<td>517.942</td>
13793	</tr>	13793	</tr>
13794	<tr>	13794	<tr>
13795	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>495.624</td>	13795	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>495.624</td>
13796	</tr>	13796	</tr>
13797	<tr>	13797	<tr>
13798	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13798	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13799	</tr>	13799	</tr>
Offset 13894, 18 lines modified		Offset 13894, 18 lines modified
13894	<tr>	13894	<tr>
13895	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··	13895	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>222</td>··
13896	</tr>	13896	</tr>
13897	<tr>	13897	<tr>
13898	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-180.806</td>	13898	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-180.806</td>
13899	</tr>	13899	</tr>
13900	<tr>	13900	<tr>
13901	··<th>Date:</th>············<td>~~Wed~~,·10·Jun·2020</td>·<th>··AIC················</th>··<td>399.611</td>	13901	··<th>Date:</th>············<td>Fri,·12·Jun·2020</td>·<th>··AIC················</th>··<td>399.611</td>
13902	</tr>	13902	</tr>
13903	<tr>	13903	<tr>
13904	··<th>Time:</th>················<td>23:20:30</td>·····<th>··BIC················</th>··<td>464.262</td>	13904	··<th>Time:</th>················<td>07:42:06</td>·····<th>··BIC················</th>··<td>464.262</td>
13905	</tr>	13905	</tr>
13906	<tr>	13906	<tr>
13907	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>425.713</td>	13907	··<th>Sample:</th>·············<td>07-01-1955</td>····<th>··HQIC···············</th>··<td>425.713</td>
13908	</tr>	13908	</tr>
13909	<tr>	13909	<tr>
13910	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···	13910	··<th></th>···················<td>-·10-01-2010</td>···<th>·····················</th>·····<td>·</td>···
13911	</tr>	13911	</tr>
Offset 14150, 18 lines modified		Offset 14150, 18 lines modified
14150	<tr>	14150	<tr>
14151	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>520</td>··	14151	··<th>Dep.·Variable:</th>···········<td>y</td>········<th>··No.·Observations:··</th>····<td>520</td>··
14152	</tr>	14152	</tr>
14153	<tr>	14153	<tr>
14154	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-745.798</td>	14154	··<th>Model:</th>···········<td>MarkovRegression</td>·<th>··Log·Likelihood·····</th>·<td>-745.798</td>
14155	</tr>	14155	</tr>
14156	<tr>	14156	<tr>
14157	··<th>Date:</th>············<td>~~Wed~~,·10·Jun·2020</td>·<th>··AIC················</th>·<td>1507.595</td>	14157	··<th>Date:</th>············<td>Fri,·12·Jun·2020</td>·<th>··AIC················</th>·<td>1507.595</td>
14158	</tr>	14158	</tr>
14159	<tr>	14159	<tr>
14160	··<th>Time:</th>················<td>23:21:18</td>·····<th>··BIC················</th>·<td>1541.626</td>	14160	··<th>Time:</th>················<td>07:42:18</td>·····<th>··BIC················</th>·<td>1541.626</td>
14161	</tr>	14161	</tr>
14162	<tr>	14162	<tr>
14163	··<th>Sample:</th>·············<td>05-16-2004</td>····<th>··HQIC···············</th>·<td>1520.926</td>	14163	··<th>Sample:</th>·············<td>05-16-2004</td>····<th>··HQIC···············</th>·<td>1520.926</td>
14164	</tr>	14164	</tr>
14165	<tr>	14165	<tr>
14166	··<th></th>···················<td>-·04-27-2014</td>···<th>·····················</th>·····<td>·</td>···	14166	··<th></th>···················<td>-·04-27-2014</td>···<th>·····················</th>·····<td>·</td>···
14167	</tr>	14167	</tr>


Offset 13325, 16 lines modified		Offset 13325, 16 lines modified

13325	<div·class="output_subarea·output_stream·output_stdout·output_text">	13325	<div·class="output_subarea·output_stream·output_stdout·output_text">
13326	<pre>····························OLS·Regression·Results····························	13326	<pre>····························OLS·Regression·Results····························
13327	==============================================================================	13327	==============================================================================
13328	Dep.·Variable:······················y···R-squared:·······················1.000	13328	Dep.·Variable:······················y···R-squared:·······················1.000
13329	Model:····························OLS···Adj.·R-squared:··················1.000	13329	Model:····························OLS···Adj.·R-squared:··················1.000
13330	Method:·················Least·Squares···F-statistic:·················4.020e+06	13330	Method:·················Least·Squares···F-statistic:·················4.020e+06
13331	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):··········2.83e-239	13331	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):··········2.83e-239
13332	Time:························23:28:25···Log-Likelihood:················-146.51	13332	Time:························07:45:51···Log-Likelihood:················-146.51
13333	No.·Observations:·················100···AIC:·····························299.0	13333	No.·Observations:·················100···AIC:·····························299.0
13334	Df·Residuals:······················97···BIC:·····························306.8	13334	Df·Residuals:······················97···BIC:·····························306.8
13335	Df·Model:···························2·········································	13335	Df·Model:···························2·········································
13336	Covariance·Type:············nonrobust·········································	13336	Covariance·Type:············nonrobust·········································
13337	==============================================================================	13337	==============================================================================
13338	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13338	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13339	------------------------------------------------------------------------------	13339	------------------------------------------------------------------------------
Offset 13459, 16 lines modified		Offset 13459, 16 lines modified

13459	<div·class="output_subarea·output_stream·output_stdout·output_text">	13459	<div·class="output_subarea·output_stream·output_stdout·output_text">
13460	<pre>····························OLS·Regression·Results····························	13460	<pre>····························OLS·Regression·Results····························
13461	==============================================================================	13461	==============================================================================
13462	Dep.·Variable:······················y···R-squared:·······················0.933	13462	Dep.·Variable:······················y···R-squared:·······················0.933
13463	Model:····························OLS···Adj.·R-squared:··················0.928	13463	Model:····························OLS···Adj.·R-squared:··················0.928
13464	Method:·················Least·Squares···F-statistic:·····················211.8	13464	Method:·················Least·Squares···F-statistic:·····················211.8
13465	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········6.30e-27	13465	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········6.30e-27
13466	Time:························23:28:27···Log-Likelihood:················-34.438	13466	Time:························07:45:51···Log-Likelihood:················-34.438
13467	No.·Observations:··················50···AIC:·····························76.88	13467	No.·Observations:··················50···AIC:·····························76.88
13468	Df·Residuals:······················46···BIC:·····························84.52	13468	Df·Residuals:······················46···BIC:·····························84.52
13469	Df·Model:···························3·········································	13469	Df·Model:···························3·········································
13470	Covariance·Type:············nonrobust·········································	13470	Covariance·Type:············nonrobust·········································
13471	==============================================================================	13471	==============================================================================
13472	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13472	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13473	------------------------------------------------------------------------------	13473	------------------------------------------------------------------------------
Offset 13718, 16 lines modified		Offset 13718, 16 lines modified

13718	<div·class="output_subarea·output_stream·output_stdout·output_text">	13718	<div·class="output_subarea·output_stream·output_stdout·output_text">
13719	<pre>····························OLS·Regression·Results····························	13719	<pre>····························OLS·Regression·Results····························
13720	==============================================================================	13720	==============================================================================
13721	Dep.·Variable:······················y···R-squared:·······················0.978	13721	Dep.·Variable:······················y···R-squared:·······················0.978
13722	Model:····························OLS···Adj.·R-squared:··················0.976	13722	Model:····························OLS···Adj.·R-squared:··················0.976
13723	Method:·················Least·Squares···F-statistic:·····················671.7	13723	Method:·················Least·Squares···F-statistic:·····················671.7
13724	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········5.69e-38	13724	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········5.69e-38
13725	Time:························23:28:36···Log-Likelihood:················-64.643	13725	Time:························07:45:53···Log-Likelihood:················-64.643
13726	No.·Observations:··················50···AIC:·····························137.3	13726	No.·Observations:··················50···AIC:·····························137.3
13727	Df·Residuals:······················46···BIC:·····························144.9	13727	Df·Residuals:······················46···BIC:·····························144.9
13728	Df·Model:···························3·········································	13728	Df·Model:···························3·········································
13729	Covariance·Type:············nonrobust·········································	13729	Covariance·Type:············nonrobust·········································
13730	==============================================================================	13730	==============================================================================
13731	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13731	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13732	------------------------------------------------------------------------------	13732	------------------------------------------------------------------------------
Offset 14024, 34 lines modified		Offset 14024, 22 lines modified


14024	<div·class="output_area">	14024	<div·class="output_area">

14025	····<div·class="prompt"></div>	14025	····<div·class="prompt"></div>


14026	<div·class="output_subarea·output_stream·output_stderr·output_text">
14027	<pre>/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394:·UserWarning:·kurtosistest·only·valid·for·n>=20·...·continuing·anyway,·n=16
14028	··"anyway,·n=%i"·%·int(n))
14029	</pre>
14030	</div>
14031	</div>

14032	<div·class="output_area">

14033	····<div·class="prompt"></div>


14034	<div·class="output_subarea·output_stream·output_stdout·output_text">	14026	<div·class="output_subarea·output_stream·output_stdout·output_text">
14035	<pre>····························OLS·Regression·Results····························	14027	<pre>····························OLS·Regression·Results····························
14036	==============================================================================	14028	==============================================================================
14037	Dep.·Variable:·················TOTEMP···R-squared:·······················0.995	14029	Dep.·Variable:·················TOTEMP···R-squared:·······················0.995
14038	Model:····························OLS···Adj.·R-squared:··················0.992	14030	Model:····························OLS···Adj.·R-squared:··················0.992
14039	Method:·················Least·Squares···F-statistic:·····················330.3	14031	Method:·················Least·Squares···F-statistic:·····················330.3
14040	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········4.98e-10	14032	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········4.98e-10
14041	Time:························23:28:44···Log-Likelihood:················-109.62	14033	Time:························07:45:56···Log-Likelihood:················-109.62
14042	No.·Observations:··················16···AIC:·····························233.2	14034	No.·Observations:··················16···AIC:·····························233.2
14043	Df·Residuals:·······················9···BIC:·····························238.6	14035	Df·Residuals:·······················9···BIC:·····························238.6
14044	Df·Model:···························6·········································	14036	Df·Model:···························6·········································
14045	Covariance·Type:············nonrobust·········································	14037	Covariance·Type:············nonrobust·········································
14046	==============================================================================	14038	==============================================================================
14047	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	14039	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
14048	------------------------------------------------------------------------------	14040	------------------------------------------------------------------------------
Offset 14073, 14 lines modified		Offset 14061, 26 lines modified
14073	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.	14061	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.
14074	[2]·The·condition·number·is·large,·4.86e+09.·This·might·indicate·that·there·are	14062	[2]·The·condition·number·is·large,·4.86e+09.·This·might·indicate·that·there·are
14075	strong·multicollinearity·or·other·numerical·problems.	14063	strong·multicollinearity·or·other·numerical·problems.
14076	</pre>	14064	</pre>
14077	</div>	14065	</div>
14078	</div>	14066	</div>

		14067	<div·class="output_area">

		14068	····<div·class="prompt"></div>


		14069	<div·class="output_subarea·output_stream·output_stderr·output_text">
		14070	<pre>/usr/lib/python3/dist-packages/scipy/stats/stats.py:1394:·UserWarning:·kurtosistest·only·valid·for·n>=20·...·continuing·anyway,·n=16
		14071	··"anyway,·n=%i"·%·int(n))
		14072	</pre>
		14073	</div>
		14074	</div>

14079	</div>	14075	</div>
14080	</div>	14076	</div>

14081	</div>	14077	</div>
14082	<div·class="cell·border-box-sizing·text_cell·rendered"><div·class="prompt·input_prompt">	14078	<div·class="cell·border-box-sizing·text_cell·rendered"><div·class="prompt·input_prompt">
14083	</div><div·class="inner_cell">	14079	</div><div·class="inner_cell">
14084	<div·class="text_cell_render·border-box-sizing·rendered_html">	14080	<div·class="text_cell_render·border-box-sizing·rendered_html">
Offset 14295, 24 lines modified		Offset 14295, 14 lines modified

14295	····<div·class="prompt"></div>	14295	····<div·class="prompt"></div>


14296	<div·class="output_subarea·output_stream·output_stderr·output_text">	14296	<div·class="output_subarea·output_stream·output_stderr·output_text">
14297	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:309:·RuntimeWarning:·invalid·value·encountered·in·sqrt	14297	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:309:·RuntimeWarning:·invalid·value·encountered·in·sqrt
14298	··return··self.results.resid·/·sigma·/·np.sqrt(1·-·hii)	14298	··return··self.results.resid·/·sigma·/·np.sqrt(1·-·hii)
14299	/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:879:·RuntimeWarning:·invalid·value·encountered·in·greater
14300	··return·(self.a·<·x)·&·(x·<·self.b)
14301	/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:879:·RuntimeWarning:·invalid·value·encountered·in·less
14302	··return·(self.a·<·x)·&·(x·<·self.b)
14303	/usr/lib/python3/dist-packages/scipy/stats/_distn_infrastructure.py:1821:·RuntimeWarning:·invalid·value·encountered·in·less_equal
14304	··cond2·=·cond0·&·(x·<=·self.a)
14305	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/stats/outliers_influence.py:323:·RuntimeWarning:·invalid·value·encountered·in·sqrt
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13398	<div·class="output_png·output_subarea·">	13398	<div·class="output_png·output_subarea·">
13399	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXl4W+WV/7~~9XupuurhZbip29iSEbEMj~~Q~~sITAr7SUNmwNZFjiSQoM0J~~U~~JBMg0wExK2wyEgAOBA~~G~~U6E6ahCQ7Q0qZtKDOUFspMICXQBEJ275ts7~~ftiS~~/f3h~~y~~xHkrVc2ZKs~~5f08T54~~kV1fy0e~~v33vf~~c857zPZQk~~SSAQCAQCgUA~~glAaKiTa~~A~~QC~~AQCAQC~~gXAa4pwR~~CAQC~~gUAglBDEOSM~~QCAQC~~gUAoIYhzRiA~~QCAQC~~gVB~~C~~EOeMQCA~~QCAQ~~CoYQgzhm~~B~~QCA~~QCARC~~CVEw54y~~i~~qJco~~i~~hqg~~K~~Opw3LFaiqLepijq5PD~~f~~NcPHKYqinq~~U~~o6hRFUZ9SFHV+oewiE~~AgEA~~oFAKG~~UK~~GTn7OYBlScceBPCO~~JEl~~zALwz/H~~8~~AuAr~~A~~nOE/3wbw0wLaRSA~~QCAQCg~~VCyFMw5kyTpLwBsSYeXA9gx/O8dAK6PO~~/6~~yFOVDA~~HqKoq~~YUyjYC~~gUAgEA~~iEUoUu8s+rlySpb~~/j~~fJgD1w/+e~~B~~qAr7r~~z~~u4WN9~~yIDRa~~JRmzZqV~~bxs~~JBAKB~~QCAQ~~8s7HH39skSRpUrbziu2c~~j~~SBJkkR~~R~~VM69oyi~~K~~+jaiW5+YOXMmDhw4kHfbCAQC~~gUA~~gEPIN~~R~~VEd~~c~~s4rdrVmf2y7cvjvgeHjPQ~~Bm~~xJ03ffjYKCRJ+pkk~~S~~Ysl~~S~~Vo8aVJW55NA~~IBAIBA~~KhrC~~i~~2c/~~Z~~bALcN//~~s~~2AH~~v~~ijt86XLV5MQBn3PYngUA~~gEAgEQt~~VQsG1NiqKaAVwOwEh~~RVDe~~ARwA8Du~~A~~1iqLuBN~~A~~B4Obh09~~8EcDWA~~UwB8~~A~~P6xUHYRC~~A~~QCgU~~A~~glDIFc84kSWpM89IVKc6V~~A~~NxdKFs~~IBAKBQCAQy~~gXSIYB~~A~~IB~~AIBA~~KhhC~~D~~OGYF~~AIBA~~IBE~~IJQZwzAoFA~~IBAIh~~B~~KCOGcE~~AoFAIBAIJ~~QRxzg~~gEAoFAIBBKCO~~KcEQ~~gEA~~oFAIJQQxDkjEA~~gEA~~oFAKCGIc0YgEA~~gEAoFQQhD~~njE~~AgEAgEA~~qGEIM4ZgU~~A~~gEAg~~EQg~~lBnDM~~CgUAgEA~~iEEqJgv~~T~~WrmWAwiP379yMSi~~YB~~hGCxZsgQK~~Bf~~GDCcWhra0NHR0d~~A~~IC/+7u/g06nm2CLC~~A[·...·truncated·by·diffoscope;·len:·10~~634~~6,·SHA:·~~1d3~~4~~66f40f~~e~~90b~~d~~721~~76~~65fd98bf838a~~7dcc8b~~1e34c5c1~~2d9~~f91853e8a2e728d~~·...·]	13399	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·104786,·SHA:·6884d31397ece86d0b87590e639400ef7d2c6b0aea3bda2f9c40295f475b1499·...·]
13400	"	13400	"
13401	>	13401	>
13402	</div>	13402	</div>

13403	</div>	13403	</div>

13404	</div>	13404	</div>
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13537	····<div·class="prompt"></div>	13537	····<div·class="prompt"></div>




13538	<div·class="output_png·output_subarea·">	13538	<div·class="output_png·output_subarea·">
13539	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvX~~ucFN~~WZ~~//+p7rr3~~de7~~chVEEvJLERDS6~~I~~fHnRsWI~~Ma~~LMw~~o~~qru1mzLoqRLJosMYbESwIJi~~C~~aYja66G~~F~~CzUUxQs8~~a~~vJtk1EElE~~UWCA~~ud+6p+/~~3~~23TX~~74+~~ebn~~p6~~+lL~~d09~~3T1X3~~er~~5cvmeqqm~~t~~NnTlU99Z~~zn~~fD6UJEkgEAgE~~AoFA~~IFQHqulu~~AIFAIBAIB~~ALhNCQ4Ix~~AIBA~~KBQKgiSHBGIB~~AIBA~~KBUEWQ4Ix~~AIBAIB~~AKhiiDBGYF~~AIBAI~~BEIVQYIz~~A~~oF~~AIBAIhCq~~ibMEZ~~RVFPU~~xQ1SlH~~URyn~~bGim~~K~~epOiqJPj/28Y305RFPUYRVGnKIr6kKKoT5arXQQC~~gUAgEA~~jVTDkzZ88~~AuCp~~t230~~A~~3pIkaSG~~A~~t8Z~~/BoCr~~ASwc~~/~~++rAH5axnYRCAQCgU~~A~~gVC1lC84~~kSfoDA~~Hva5pUAnh~~3~~/9~~7M~~Ark/Z/pwU5wAAI0VRM8vVNgKB~~QCAQC~~IRqha7w72uTJGlk/N8mAG3~~j/~~54NYCBlv8HxbSPIQXNzszR//vxSt5F~~AIBAIBAK~~h5PzlL3+xSp~~L~~Ukm~~+/SgdnSSR~~Jkii~~KKtg~~7iqKoryI+9~~Y~~l58+bh0KFDJW8bgU~~AgEA~~gEQqmhKKpPzn6VX~~q~~1p~~T~~kxXjv9~~/~~dHz7EIC5KfvNGd82C~~U~~mSfiZ~~J~~0kWSJF3U0pI3+CQQC~~AQC~~gUBQF~~J~~UOzl4FsG783+sA7E~~v~~Zfsv4qs1l~~AF~~wp058E~~AoFAI~~BAI~~d~~UPZpjUpit~~o~~DYDm~~A~~ZoqiBgE~~8AOA~~RAC9SFHU7~~gD4AN43v/hqAawCcAuAH8A~~/laheBQC~~AQCA~~RCNVO24EySpI4sH12~~R~~YV8JwJ3laguB~~QCA~~QCASCU~~iAOAQQC~~gUA~~gE~~AhV~~BAnOCAQC~~gUAgEKoIEpwR~~CAQC~~gUA~~g~~VBEkOCM~~QCAQCgU~~CoIkhwRiA~~QCAQCg~~VBFkOCMQCA~~QC~~AQCoYogwRm~~B~~QCA~~Q~~CARCFU~~G~~CMwK~~B~~QCAQCI~~Q~~qggRn~~BAKB~~QCAQCFUECc4~~IBAKB~~QCA~~Q~~qggSnBE~~IBAKB~~QC~~B~~UEWXz~~1~~qxn~~Q~~qEQDh48iFgsBoZh~~c~~Mkll0C~~lI~~nEw~~o~~TL~~09PSgr68PA~~PCJT3wCBoNh~~[·...·truncated·by·diffoscope;·len:·104828,·SHA:·~~46aa61~~762c~~521ed~~5~~922fd2~~d77d3e43~~2fb70be~~38~~bdbd~~36~~6887~~c69fc~~7a48ca9~~edf·...·]	13539	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·104840,·SHA:·c1ff7cb3d7e04c3ce6fc554eda1d7fcd3357c976f3aae23c10acf8977362eda7·...·]
13540	"	13540	"
13541	>	13541	>
13542	</div>	13542	</div>

13543	</div>	13543	</div>

13544	</div>	13544	</div>
Offset 13572, 15 lines modified		Offset 13572, 15 lines modified

13572	····<div·class="prompt"></div>	13572	····<div·class="prompt"></div>




13573	<div·class="output_png·output_subarea·">	13573	<div·class="output_png·output_subarea·">
13574	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHcCAYAAACTVw06AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnX~~l8VPXV~~/z~~937jZbNh~~I~~kCAYIO6KkLR~~a0W~~vGpu~~I~~AVVL~~a~~IgksXLUXxIRb1~~V~~7vRirRQ~~s~~WBRqxQQjLvyKFZrrbR~~WKx~~UsbuwExE~~D~~Wyax35s6d5f7+mMww+xKTWZjzfr18CZO~~Z~~eObrXT73fM/5HEZVV~~R~~AEQR~~A~~EQRD5gSb~~XARA~~EQR~~A~~EQR~~C~~nIHFGE~~A~~RBE~~A~~SRR5A4IwiCIAiCyCNInBE~~EQRAEQeQR~~JM4Igi~~A~~IgiDyCBJnBEE~~QBEEQ~~eUSfiTOGYTY~~w~~DNPO~~MM~~xnYa/1YxjmLYZhDnX~~/u~~6L7dYZhmD8wDHOYYZhPGIb5e~~l/FRRAEQRAE~~kc/~~0ZeZsI4A~~rol67B8Db~~q~~qqOBPB2998B4Eo~~AI7~~v/+QGA9X0YF0~~EQBEE~~QRN7SZ+JMVdV/Au~~i~~KenkGgE3~~d~~f94EYGbY65vVA~~B~~8AK~~G~~cYZm~~BfxUYQBEEQB~~JGvcFn~~+~~7w1QVbWl+8+t~~A~~AZ0~~/~~3kQ~~g~~C/D3~~tf~~c/VoLklBVVaUOHTq0t2Mk~~CIIgCILo~~dXbv3t2pqmr/VO/LtjgLo~~aqqy~~jBMxrOjGIb5AQJbn6~~i~~pqcG~~u~~Xbt6PTaC~~IAiCI~~IjehmGYL9J5X7a7N~~d~~uC25Xd~~/27vfv~~0EgLPC3je4+7UYVFV9XFXVi~~a~~qqTuzfP6X4JAiCIAiCKCiyLc7+~~D~~8DC7j~~8~~vBLAt7PUF3V2bkwFYw7Y/CYIgCIIgioY+29~~Zk~~GKYRwBQAVQzDN~~A~~P4OY~~A~~HATzHMMytAL~~4~~AMKf77a8Dm~~A~~bgMA~~A~~ngJv7Ki6CIAiCIIh8ps/Emaqq9Ql+~~9~~J0471U~~BLO~~qrWAiCI~~A~~iCIAoFmhBAEARBEASRR5A4Iw~~iCIAiC~~yCN~~I~~nBEEQR~~A~~EQeQRJM4~~IgiAIgiD~~yCBJnBEEQBEEQeQ~~SJM4IgCIIgiDyC~~xBlBEARBEEQeQe~~KMIAiCI~~AgijyBxRh~~AE~~QRAEkUeQ~~OCMIgiAIgsg~~jSJwRBEEQBEHkEX02W7~~PY2~~bdvH9ra2jBkyBAM~~Gz~~Ys1+~~E~~QRYTT6~~cS~~HH34IrVaLSZMm5Toc~~g~~iAIIkNInPUR//fWG~~zjptu~~NrJ2tJnBFZ5cSJE3j53b/B63Dhm~~9~~/8JhiGyX~~[·...·truncated·by·diffoscope;·len:·10~~3110~~,·SHA:·62d~~f3957eb~~89~~9f52ada~~d0~~c5f3~~c~~3989a90~~2b04d~~d4aae54~~9~~d67~~cc67~~b6c922~~634~~02~~·...·]	13574	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·102266,·SHA:·de9bdc4789f2699e6cd0fd7dc18f3c45b2409dc60c8689c30cb27c495af863b8·...·]
13575	"	13575	"
13576	>	13576	>
13577	</div>	13577	</div>

13578	</div>	13578	</div>

13579	</div>	13579	</div>
Offset 13809, 15 lines modified		Offset 13809, 15 lines modified

13809	····<div·class="prompt"></div>	13809	····<div·class="prompt"></div>




13810	<div·class="output_png·output_subarea·">	13810	<div·class="output_png·output_subarea·">
13811	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHoCAYAAAAMvEiBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXmcI2d17/17tLeWllpq9TZ~~Lz9jjsTHGjsE~~Z~~Y8w4YMyeQFay~~Q~~HC4EGL8Znlz~~k~~5tweW8S7k3CC1lu~~Q~~gKOIQlgB0JYwn1ZAmEJm8~~H~~OjBfAeJu1p/duS~~a~~193573j1L1VKurpJJ~~Upa~~qSzvfz6c9MS7U8/ahUz6lzfuccxjkH~~QRAEQRAEYQ5sRg~~+AIAiCIAiCu~~A~~wZ~~ZwRBEAR~~BECaC~~jDOCIAiCIAgTQcYZQRAEQRCEiSDjjCAIgiAIwkSQcUYQBEEQBGEiyDgjC~~A1hjOUZY1fofI4X~~Mcb~~W+tz3I4y~~x~~P9FhTPcyxv5~~A~~6+MSowF~~j~~7FcYY9/R6dgnGWNnJL9~~f~~zRj7~~P~~mMsxxj7Tb~~2uTcbYOxhj~~/6D1cQkCIOOMsBiM~~Mc~~4YO9b22j~~sZY~~x+V/P4O~~xth~~Sy1BaY~~4x~~9QqexfJMx9hbpa~~5xzP~~+f8oh7n~~Mw~~tyCy3~~n~~/C7~~O+~~R/rcK53M~~sb~~eKfn9LYy~~x~~863P9t8ZYwtt2z+XMfbt1vvb~~jLH~~f0mAM32S~~MpRhj~~7kGP1eU8b2C~~M~~XWK~~M~~ZRl~~j~~pxh~~jB7t~~s/0~~7~~GWK31~~t~~6YZYw8yxm4Z4Py~~X~~GGN39LnvkdZ309Hv+Xs~~415~~57AOf~~8A~~c751ZJNfg/ANzjn~~A~~c7532hx~~bco9EH~~HO38U5f4vSPgQxC~~GS~~cESMF~~Y~~+xOAL8M4A~~7O~~uR/ATQD~~+~~w9hREVrA~~G~~HsRgHcBe~~C2AMIA~~lAB+XvD8N4~~N8~~Bf~~ABABMA~~xAF8Z8Jx~~HA~~JwEwAG8ZpBjdTmPH8CH~~A~~bwVQ~~A~~jArwMoq9j1E63rP~~Arg~~OwA+wxhjPZ~~5~~bd4NqyCwCeNLoQR~~DEI~~JBxRowaPwrgy5zzCwDAOd/inH9QaeOWt+B3GWOP~~M~~8Yy~~j~~LFP~~M~~MY8rfem~~G~~GNf~~YIz~~FW56TL4jeDMbYn0JYtN/X8ly8~~r~~/X~~67lM~~9YyzIGLu/tf8yY+x/MM~~Z~~srfd+hTH2HcbYX7SOvcQYe6VkXG9ijD~~3d~~Cs1cZIz9mtoJ~~Y~~Ixd~~wxj7~~KmM~~sy~~Rg7w~~xh7XYdtf~~7wV~~A~~hI9~~L9dL3jvEGPtM~~a/w7jLH3~~M~~caeBeBe~~ALe~~IHpvWtnvCpYy~~xX~~215uJKMs~~c~~9JPVytObq~~[·...·truncated·by·diffoscope;·len:·8~~313~~6,·SHA:·~~f581~~3~~8f4d6~~f~~87a~~98f2d5c~~b61~~2~~048acd6~~7aa562~~862d5309732138~~ca4c~~ac8a23f~~9·...·]=	13811	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·82816,·SHA:·5cd23ded1ef05592dc0b42c7024bc795e29b019c7cbbee497c01f4ce5a515959·...·]=
13812	"	13812	"
13813	>	13813	>
13814	</div>	13814	</div>

13815	</div>	13815	</div>

13816	</div>	13816	</div>
Offset 13890, 15 lines modified		Offset 13890, 15 lines modified

13890	····<div·class="prompt"></div>	13890	····<div·class="prompt"></div>




13891	<div·class="output_png·output_subarea·">	13891	<div·class="output_png·output_subarea·">
13892	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmcAAAHoCAYAAAAMvEiBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsnXl8W+WZ73+vVlu~~WbXl37MR~~JC~~A6ENSEp~~N~~NCQjR1a~~Wko~~b1ik~~MU~~JbOUIYO01~~u~~YXrgzbYauFO5MO6W~~0U~~3pLI~~ZS~~yM8MW4iQl~~IRAnOCG~~L411~~eJ~~FuyLHmTLEt67x9HUrxoOZKOdHSk5/v55BP5~~LO9~~59Oosz3lWxjkHQR~~A~~EQR~~A~~EkR2o5B~~aAIAiCIAiCOAkpZwRBEARBEFkEKWcEQRAEQRBZBClnBEEQBEEQWQQpZwRBEARBEFkEKWcEQRAEQRBZBClnBCEhj~~LFxxtgpaT7GBsZYX5L7/p4x9oM0y~~PSf~~jLHvSz0ukRswxm5jjP01TWOv~~Y~~4y1zvj7NMbYp4~~y~~xMcbY/ek6Nxl~~jDzP~~GnpF6XII~~ASD~~kjFAZjjDPGTp2z7DHG2B9n~~/~~P0wY6wrqCj1Mca~~2pUm~~WJsbYnTOXcc6NnPPOdBwvW4j0oOWc38M5~~/~~9c0HOsxxthjM/6+kzHWHvxt3~~2a~~M1c3Z/j~~zG~~2K7~~g~~+kHG2LclkKGJMTbCGNOnOlac49~~z~~CGOtmjI0yxvYxxh~~b~~G2f4xxth08Ls~~6~~GWN7GGNrUzh+N2PskiT~~3~~XRK8NjXJHj+BY826B3D~~Od~~3POT5uxyT8B2ME5L+acPyXFuRnphYhzvpV~~z~~fme0fQgiFU~~g~~5I3~~I~~Kxtg3AN~~w~~K4B~~LOu~~RHAGgDb5ZWKkALG2~~AYAWw~~FcC6~~AcQBeA5~~2es~~rw~~TwNo~~BfA6g~~AcCq~~A~~d1M~~8~~5hIA6~~wBwAF9~~KZaw4~~x~~zEC+C8~~A3wRgA~~vB3AD~~w~~idt0WPM+rAP~~w~~VwMu~~M~~MZbgsd~~O~~uUGW~~YxQC~~OyC0~~EQ~~aQCKWdErvE5A~~O~~9wzjsAgHNu5Zw/HW3joLXgHxljhxhjL~~s~~bYN~~s~~ZYQXBdGW~~P~~sTcaYLWg5eTNkzWCM/RDCQ/vfg5a~~L~~fw8uD7/VM8ZKGWN/CO7fwxj7Z8aYKrju~~NsbY~~Xxl~~j~~Pw2O3cUYu3KGXLczxo4FXTOdjLG7~~xU4AY~~+x0xth7jDEHY6yVMfb1G~~NteE3QBhSwv58~~xYt4gx9nJQ/mHG2L~~8~~zxlY~~A~~+E8~~A~~a0MWm+~~C~~2s9yljLG7ghYuB~~2P~~s9ZkWruAc3c~~MY~~awse9z9EKhTX~~A~~Pgz5/wI59wL4F~~8[·...·truncated·by·diffoscope;·len:·86119,·SHA:·0~~492~~64452ac7f518~~6439a94~~a74375f0d83~~b445d049f~~02921cce1~~93a~~b1e8d~~6b6e~~·...·]AAAABJRU5ErkJggg==	13892	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·86079,·SHA:·eff6595c4ecdd5e8f5ea743ecf658f1edae32e0782b123cf1a050d53b15d0ca6·...·]AAAABJRU5ErkJggg==
13893	"	13893	"
13894	>	13894	>
13895	</div>	13895	</div>

13896	</div>	13896	</div>

13897	</div>	13897	</div>


Offset 13297, 35 lines modified		Offset 13297, 35 lines modified

13297	····<div·class="prompt"></div>	13297	····<div·class="prompt"></div>


13298	<div·class="output_subarea·output_stream·output_stdout·output_text">	13298	<div·class="output_subarea·output_stream·output_stdout·output_text">
13299	<pre>····························OLS·Regression·Results····························	13299	<pre>····························OLS·Regression·Results····························
13300	==============================================================================	13300	==============================================================================
13301	Dep.·Variable:······················y···R-squared:·······················0.986	13301	Dep.·Variable:······················y···R-squared:·······················0.987
13302	Model:····························OLS···Adj.·R-squared:··················0.985	13302	Model:····························OLS···Adj.·R-squared:··················0.987
13303	Method:·················Least·Squares···F-statistic:·····················1~~061~~.	13303	Method:·················Least·Squares···F-statistic:·····················1199.
13304	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········1.89e-42	13304	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········1.16e-43
13305	Time:························23:17:58···Log-Likelihood:·················3.~~8658~~	13305	Time:························07:43:46···Log-Likelihood:·················8.2729
13306	No.·Observations:··················50···AIC:····························~~0.2684~~	13306	No.·Observations:··················50···AIC:····························-8.546
13307	Df·Residuals:······················46···BIC:···························~~··7.~~917	13307	Df·Residuals:······················46···BIC:···························-0.8976
13308	Df·Model:···························3·········································	13308	Df·Model:···························3·········································
13309	Covariance·Type:············nonrobust·········································	13309	Covariance·Type:············nonrobust·········································
13310	==============================================================================	13310	==============================================================================
13311	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13311	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13312	------------------------------------------------------------------------------	13312	------------------------------------------------------------------------------
13313	const··········5.0~~216~~······0.080·····63.095······0.000·······4.861·······5.182	13313	const··········5.0303······0.073·····69.028······0.000·······4.884·······5.177
13314	x1·············0.4937······0.012·····40.~~221~~······0.000·······0.469·······0.518	13314	x1·············0.5037······0.011·····44.815······0.000·······0.481·······0.526
13315	x2·············0.~~4809~~······0.048·····~~·9~~.~~967~~······0.000·······0.~~384~~·······0.578	13315	x2·············0.5070······0.044·····11.475······0.000·······0.418·······0.596
13316	x3············-0.0~~188~~······0.001····-17.474······0.000······-0.021······-0.017	13316	x3············-0.0211······0.001····-21.409······0.000······-0.023······-0.019
13317	==============================================================================	13317	==============================================================================
13318	Omnibus:························0.~~007~~···Durbin-Watson:···················1.~~758~~	13318	Omnibus:························1.524···Durbin-Watson:···················2.635
13319	Prob(Omnibus):··················0.997···Jarque-Bera·(JB):················0.156	13319	Prob(Omnibus):··················0.467···Jarque-Bera·(JB):················1.153
13320	Skew:··························-0.~~004~~···Prob(JB):························0.~~925~~	13320	Skew:··························-0.116···Prob(JB):························0.562
13321	Kurtosis:·······················2.~~727~~···Cond.·No.·························221.	13321	Kurtosis:·······················2.293···Cond.·No.·························221.
13322	==============================================================================	13322	==============================================================================

13323	Warnings:	13323	Warnings:
13324	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.	13324	[1]·Standard·Errors·assume·that·the·covariance·matrix·of·the·errors·is·correctly·specified.
13325	</pre>	13325	</pre>
13326	</div>	13326	</div>
13327	</div>	13327	</div>
Offset 13360, 23 lines modified		Offset 13360, 23 lines modified

13360	<div·class="output_area">	13360	<div·class="output_area">

13361	····<div·class="prompt"></div>	13361	····<div·class="prompt"></div>


13362	<div·class="output_subarea·output_stream·output_stdout·output_text">	13362	<div·class="output_subarea·output_stream·output_stdout·output_text">
13363	<pre>[·4.5~~5078844~~··5.0169~~0379~~··5.44~~538304~~··5.8~~1001727~~··6.~~094~~05~~622~~··6.~~29296031~~	13363	<pre>[·4.50212995··4.99165165··5.4410721···5.82276099··6.11905964··6.32518228
13364	··6.4~~1514649~~··6.~~48060535~~··6.5~~176167~~3··6.5581~~0341~~··6.6~~323863~~1··6.7~~6420283~~	13364	··6.45000234··6.51459454··6.54877243··6.58619005··6.65881271··6.79166515
13365	··6.9~~6680686~~··7.240~~7912~~2··7.~~57399024~~··7.~~94347858~~··8.3~~1933775~~··8.~~66957167~~	13365	··6.99872007··7.28060242··7.62448678··8.00620447··8.39421446··8.75478543
13366	··8.96536~~353~~··9.18~~581029~~··9.32135703··9.~~375366~~74··9.363~~56784~~··9.31147003	13366	··9.05753774··9.28043474··9.41340328··9.45998877··9.43677262··9.37064799
13367	··9.25017311··9.21125736··9.22~~159~~503··9.298~~93528~~··9.4~~489880~~2··9.66~~448~~583	13367	··9.2944013···9.24132554··9.2397503···9.30838733··9.45325622··9.6666954
13368	··9.92~~6377~~96·10.20~~6960~~3··10.47~~442712~~·10.69~~8096~~62·10.8~~534~~5~~19·~~·10.9~~2616898~~	13368	··9.92862095·10.2098263··10.47678078·10.69713864·10.84505336·10.90542413
13369	·10.~~9144~~7~~024~~·10.829~~41643~~·10.6~~930~~8944·10.5~~349~~643··10.~~38706655~~·10.~~27871047~~	13369	·10.87637693·10.76957235·10.60828985·10.423603···10.24927414·10.11620736
13370	·10.~~2316829·~~·10.25~~666366~~·10.~~35147092~~·10.50141~~967~~·10.~~68173442~~·10.~~8616~~1972	13370	·10.04737107·10.05402427·10.13386683·10.2714173··10.44055656·10.60881904
13371	·11.~~0093198·~~·11.09733595]	13371	·10.74272676·10.81328967]
13372	</pre>	13372	</pre>
13373	</div>	13373	</div>
13374	</div>	13374	</div>

13375	</div>	13375	</div>
13376	</div>	13376	</div>

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13410	<div·class="output_area">	13410	<div·class="output_area">

13411	····<div·class="prompt"></div>	13411	····<div·class="prompt"></div>


13412	<div·class="output_subarea·output_stream·output_stdout·output_text">	13412	<div·class="output_subarea·output_stream·output_stdout·output_text">
13413	<pre>[11.~~09734119~~·10.~~970561~~51·10.~~735~~8~~5619~~·10.~~436~~2~~0319~~·10.~~128~~17~~66·~~··9.~~868~~09538	13413	<pre>[10.78548971·10.62339258·10.34688037·10.00126184··9.64617928··9.34100596
13414	··9.~~69823446~~··9.63~~64744~~···9.~~6719236·~~··9.~~76758491~~]	13414	··9.13030951··9.0329402···9.03741575··9.10473255]
13415	</pre>	13415	</pre>
13416	</div>	13416	</div>
13417	</div>	13417	</div>

13418	</div>	13418	</div>
13419	</div>	13419	</div>

Offset 13457, 15 lines modified		Offset 13457, 15 lines modified

13457	····<div·class="prompt"></div>	13457	····<div·class="prompt"></div>




13458	<div·class="output_png·output_subarea·">	13458	<div·class="output_png·output_subarea·">
13459	<img·src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xdc1dX/wPHX~~uchy~~4sAB~~ODDT3IuU~~NFNz~~5cjVMC3~~bmaXZ0t~~SWZr/SxB~~zf~~LM3KmZYb04~~a7ZS4Q~~zT1KTTE~~FURB~~kw/n9cYFkXLlyL9z1fj4e~~P~~sQP~~n~~3vv++PV9z28~~P+e8j9JaI4QQwv~~EZbB2~~AE~~EII65C~~E~~Lo~~Q~~QTk~~IS~~uhB~~C~~OAlJ6EI~~I~~4SQ~~koQ~~shhJOQ~~hC~~6EEE5C~~Er~~oQ~~Q~~jgJSehC~~COEkJKE~~LIYSTKFWS~~L1a~~lShVdp06~~dk~~nxJIYRweOHh~~4Ze1~~1r6FnVeiC~~b~~1On~~T~~qEhYW~~V5E~~sKI~~YTD~~U0qdNec8Kb~~kIIY~~STkIQu~~hB~~BOQ~~h~~K6EE~~I~~4iRKtoRckLS2N8+fPk5ycbOtQhBm8~~vLw~~ICAjA~~3d~~3d1qEIIf~~K~~weUI/f/485cqVo06dOiilb~~B~~2OuAm~~t~~NTExMZw/f57A~~wEBb~~hyOEyMPmCT05OVmSuYNQSlG5cmWio6Nt~~H~~YpwUKE~~R~~kYRsPM6F2CT8fLwZ06M~~B/V~~v62zosp2~~H~~zhA5I~~Mnc~~g8l6Jg~~pi~~TqEMjIhm/5iBJaR~~kA~~RMYmMX~~7NQQ~~BJ6l~~Y~~iN0WFEBbJTtSRsU~~l~~o/kv~~UoR~~GRuc4L2Xg8J~~5lnS~~0rLIGTj8RKM1rlJQ~~g~~fc3Nxo0aIFjRs3pnnz5~~nz~~88cdkZmbe9DFn~~z~~pxh2bJl~~J~~RShEP~~b~~L3ER9ITapwMebOi5un~~V~~2UXG5FcdTgvL2~~9~~2b9/PwBRUVE~~M~~GTKEa9eu8d~~5~~775l~~8~~THZCHzJkiEWvLVyHs9aPzU3Ufj7eRBZwrp+Pd7HE5YocaoR~~u~~7o92lqhatSrz5s1j~~9~~uzZaK05c+Y~~M~~HTp0oFWrV~~r~~Rq1Yo~~//vgDg~~HHjxvHbb7/RokULZsyYYfI8IaBk/u3aiqmEnPf4mB4N8H~~Z~~3y3XM292NMT0aFFtsrqbQEbpSaj7Q~~B~~4jSWj~~fJO~~vYQMBFoCLTRWpdIg5ab/W~~h~~nzZFO3bp1ycjIICoqiqp~~V~~q7J582a~~8~~vLw4efIkgwc~~P~~JiwsjClTpjBt~~2j~~Q2bNg~~AQGJi~~YoHnCQEl92/XFsb~~0~~aJDrZicU~~n~~Kj7t/SnzD+nuRIyE0NcHKU9~~3~~WjiV4Ha50sbTzAYYOBA6N27JM~~N~~3KuaUXBYCs4HF~~[·...·truncated·by·diffoscope;·len:·17248,·SHA:·~~8b61bfdbe579~~83d01~~e55b7~~6~~ed58d~~b558b2~~dac~~7~~89bd~~1~~1a9~~5~~899b2f3~~de7b~~a3aa8~~c·...·]	13459	<img·src="data:image/png;base64,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[·...·truncated·by·diffoscope;·len:·17648,·SHA:·d4820814d8fda61bed4e6a8efab00b2be77e3c2135d5af37aca4dbe11b0f02dc·...·]
13460	"	13460	"
13461	>	13461	>
13462	</div>	13462	</div>

13463	</div>	13463	</div>

13464	</div>	13464	</div>
Offset 13532, 18 lines modified		Offset 13532, 18 lines modified

13532	····<div·class="prompt·output_prompt">Out[8]:</div>	13532	····<div·class="prompt·output_prompt">Out[8]:</div>




13533	<div·class="output_text·output_subarea·output_execute_result">	13533	<div·class="output_text·output_subarea·output_execute_result">
13534	<pre>Intercept···········5.0~~21576~~	13534	<pre>Intercept···········5.030267
13535	x1··················0.493690	13535	x1··················0.503670
13536	np.sin(x1)··········0.~~480~~906	13536	np.sin(x1)··········0.506986
13537	I((x1·-·5)·**·2)···-0.0~~18832~~	13537	I((x1·-·5)·**·2)···-0.021125
13538	dtype:·float64</pre>	13538	dtype:·float64</pre>
13539	</div>	13539	</div>

13540	</div>	13540	</div>

13541	</div>	13541	</div>
13542	</div>	13542	</div>
Offset 13577, 24 lines modified		Offset 13577, 24 lines modified

13577	····<div·class="prompt·output_prompt">Out[9]:</div>	13577	····<div·class="prompt·output_prompt">Out[9]:</div>




13578	<div·class="output_text·output_subarea·output_execute_result">	13578	<div·class="output_text·output_subarea·output_execute_result">
13579	<pre>0····11.~~097341~~	13579	<pre>0····10.785490
13580	1····10.~~970562~~	13580	1····10.623393
13581	2····10.~~735~~856	13581	2····10.346880
13582	3····10.~~436~~203	13582	3····10.001262
13583	4····10.~~128~~177	13583	4·····9.646179
13584	5·····9.~~868~~095	13584	5·····9.341006
13585	6·····9.~~698234~~	13585	6·····9.130310
13586	7·····9.63~~6474~~	13586	7·····9.032940
Max diff block lines reached; 36/43895 bytes (0.08%) of diff not shown.


Offset 13357, 16 lines modified		Offset 13357, 16 lines modified

13357	<div·class="output_subarea·output_stream·output_stdout·output_text">	13357	<div·class="output_subarea·output_stream·output_stdout·output_text">
13358	<pre>·························QuantReg·Regression·Results··························	13358	<pre>·························QuantReg·Regression·Results··························
13359	==============================================================================	13359	==============================================================================
13360	Dep.·Variable:················foodexp···Pseudo·R-squared:···············0.6206	13360	Dep.·Variable:················foodexp···Pseudo·R-squared:···············0.6206
13361	Model:·······················QuantReg···Bandwidth:·······················64.51	13361	Model:·······················QuantReg···Bandwidth:·······················64.51
13362	Method:·················Least·Squares···Sparsity:························209.3	13362	Method:·················Least·Squares···Sparsity:························209.3
13363	Date:················~~Wed~~,·10·Jun·2020···No.·Observations:··················235	13363	Date:················Fri,·12·Jun·2020···No.·Observations:··················235
13364	Time:························23:14:17···Df·Residuals:······················233	13364	Time:························07:41:53···Df·Residuals:······················233
13365	········································Df·Model:····························1	13365	········································Df·Model:····························1
13366	==============================================================================	13366	==============================================================================
13367	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13367	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13368	------------------------------------------------------------------------------	13368	------------------------------------------------------------------------------
13369	Intercept·····81.4823·····14.634······5.568······0.000······52.649·····110.315	13369	Intercept·····81.4823·····14.634······5.568······0.000······52.649·····110.315
13370	income·········0.5602······0.013·····42.516······0.000·······0.534·······0.586	13370	income·········0.5602······0.013·····42.516······0.000·······0.534·······0.586
13371	==============================================================================	13371	==============================================================================


Offset 13274, 16 lines modified		Offset 13274, 16 lines modified

13274	<div·class="output_subarea·output_stream·output_stdout·output_text">	13274	<div·class="output_subarea·output_stream·output_stdout·output_text">
13275	<pre>····························OLS·Regression·Results····························	13275	<pre>····························OLS·Regression·Results····························
13276	==============================================================================	13276	==============================================================================
13277	Dep.·Variable:················Lottery···R-squared:·······················0.348	13277	Dep.·Variable:················Lottery···R-squared:·······················0.348
13278	Model:····························OLS···Adj.·R-squared:··················0.333	13278	Model:····························OLS···Adj.·R-squared:··················0.333
13279	Method:·················Least·Squares···F-statistic:·····················22.20	13279	Method:·················Least·Squares···F-statistic:·····················22.20
13280	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········1.90e-08	13280	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········1.90e-08
13281	Time:························23:11:46···Log-Likelihood:················-379.82	13281	Time:························07:45:14···Log-Likelihood:················-379.82
13282	No.·Observations:··················86···AIC:·····························765.6	13282	No.·Observations:··················86···AIC:·····························765.6
13283	Df·Residuals:······················83···BIC:·····························773.0	13283	Df·Residuals:······················83···BIC:·····························773.0
13284	Df·Model:···························2·········································	13284	Df·Model:···························2·········································
13285	Covariance·Type:············nonrobust·········································	13285	Covariance·Type:············nonrobust·········································
13286	===================================================================================	13286	===================================================================================
13287	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13287	······················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13288	-----------------------------------------------------------------------------------	13288	-----------------------------------------------------------------------------------


Offset 14764, 16 lines modified		Offset 14764, 16 lines modified

14764	<div·class="output_subarea·output_stream·output_stdout·output_text">	14764	<div·class="output_subarea·output_stream·output_stdout·output_text">
14765	<pre>····························OLS·Regression·Results····························	14765	<pre>····························OLS·Regression·Results····························
14766	==============================================================================	14766	==============================================================================
14767	Dep.·Variable:···············prestige···R-squared:·······················0.828	14767	Dep.·Variable:···············prestige···R-squared:·······················0.828
14768	Model:····························OLS···Adj.·R-squared:··················0.820	14768	Model:····························OLS···Adj.·R-squared:··················0.820
14769	Method:·················Least·Squares···F-statistic:·····················101.2	14769	Method:·················Least·Squares···F-statistic:·····················101.2
14770	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········8.65e-17	14770	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········8.65e-17
14771	Time:························23:21:45···Log-Likelihood:················-178.98	14771	Time:························07:40:51···Log-Likelihood:················-178.98
14772	No.·Observations:··················45···AIC:·····························364.0	14772	No.·Observations:··················45···AIC:·····························364.0
14773	Df·Residuals:······················42···BIC:·····························369.4	14773	Df·Residuals:······················42···BIC:·····························369.4
14774	Df·Model:···························2·········································	14774	Df·Model:···························2·········································
14775	Covariance·Type:············nonrobust·········································	14775	Covariance·Type:············nonrobust·········································
14776	==============================================================================	14776	==============================================================================
14777	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	14777	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
14778	------------------------------------------------------------------------------	14778	------------------------------------------------------------------------------
Offset 15168, 16 lines modified		Offset 15168, 16 lines modified
15168	==============================================================================	15168	==============================================================================
15169	Dep.·Variable:···············prestige···No.·Observations:···················45	15169	Dep.·Variable:···············prestige···No.·Observations:···················45
15170	Model:····························RLM···Df·Residuals:·······················42	15170	Model:····························RLM···Df·Residuals:·······················42
15171	Method:··························IRLS···Df·Model:····························2	15171	Method:··························IRLS···Df·Model:····························2
15172	Norm:··························HuberT·········································	15172	Norm:··························HuberT·········································
15173	Scale·Est.:·······················mad·········································	15173	Scale·Est.:·······················mad·········································
15174	Cov·Type:··························H1·········································	15174	Cov·Type:··························H1·········································
15175	Date:················~~Wed~~,·10·Jun·2020·········································	15175	Date:················Fri,·12·Jun·2020·········································
15176	Time:························23:21:51·········································	15176	Time:························07:40:53·········································
15177	No.·Iterations:····················18·········································	15177	No.·Iterations:····················18·········································
15178	==============================================================================	15178	==============================================================================
15179	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	15179	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
15180	------------------------------------------------------------------------------	15180	------------------------------------------------------------------------------
15181	Intercept·····-7.1107······3.879·····-1.833······0.067·····-14.713·······0.492	15181	Intercept·····-7.1107······3.879·····-1.833······0.067·····-14.713·······0.492
15182	income·········0.7015······0.109······6.456······0.000·······0.489·······0.914	15182	income·········0.7015······0.109······6.456······0.000·······0.489·······0.914
15183	education······0.4854······0.089······5.441······0.000·······0.311·······0.660	15183	education······0.4854······0.089······5.441······0.000·······0.311·······0.660


Offset 13454, 15 lines modified		Offset 13454, 15 lines modified
13454	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>	13454	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>

13455	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>	13455	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>
13456	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(	13456	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(
13457	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)	13457	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)
13458	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·	13458	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·

13459	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xac842c50>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused	13459	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xac82cc50>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused

13460	During·handling·of·the·above·exception,·another·exception·occurred:	13460	During·handling·of·the·above·exception,·another·exception·occurred:

13461	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)	13461	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)
13462	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13462	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13463	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>	13463	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>
13464	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout	13464	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout
Offset 13474, 15 lines modified		Offset 13474, 15 lines modified
13474	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>	13474	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>

13475	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>	13475	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>
13476	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13476	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13477	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>	13477	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>
13478	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·	13478	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·

13479	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac842c50>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))	13479	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac82cc50>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))

13480	During·handling·of·the·above·exception,·another·exception·occurred:	13480	During·handling·of·the·above·exception,·another·exception·occurred:

13481	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)	13481	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)
13482	<span·class="ansi-green-fg"><ipython-input-3-adacc4910a58></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>	13482	<span·class="ansi-green-fg"><ipython-input-3-adacc4910a58></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>
13483	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·	13483	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·
13484	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>	13484	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>
Offset 13521, 15 lines modified		Offset 13521, 15 lines modified
13521	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13521	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13522	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·	13522	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·
13523	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13523	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13524	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>	13524	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>
13525	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·	13525	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·
13526	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13526	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>

13527	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac842c50>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>	13527	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://staff.feweb.vu.nl/koopman/projects/ckbook/OxCodeAll.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xac82cc50>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>
13528	</div>	13528	</div>
13529	</div>	13529	</div>

13530	</div>	13530	</div>
13531	</div>	13531	</div>

13532	</div>	13532	</div>


Offset 13360, 15 lines modified		Offset 13360, 15 lines modified
13360	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>	13360	<span·class="ansi-green-intense-fg·ansi-bold">····182</span>·········self<span·class="ansi-blue-fg">.</span>_prepare_conn<span·class="ansi-blue-fg">(</span>conn<span·class="ansi-blue-fg">)</span>

13361	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>	13361	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/connection.py</span>·in·<span·class="ansi-cyan-fg">_new_conn</span><span·class="ansi-blue-fg">(self)</span>
13362	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(	13362	<span·class="ansi-green-intense-fg·ansi-bold">····167</span>·············raise·NewConnectionError(
13363	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)	13363	<span·class="ansi-green-fg">-->·168</span><span·class="ansi-red-fg">·················self,·"Failed·to·establish·a·new·connection:·%s"·%·e)
13364	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·	13364	</span><span·class="ansi-green-intense-fg·ansi-bold">····169</span>·

13365	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xa~~c7888f~~0>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused	13365	<span·class="ansi-red-fg">NewConnectionError</span>:·<urllib3.connection.HTTPConnection·object·at·0xabec9a30>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused

13366	During·handling·of·the·above·exception,·another·exception·occurred:	13366	During·handling·of·the·above·exception,·another·exception·occurred:

13367	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)	13367	<span·class="ansi-red-fg">MaxRetryError</span>·····························Traceback·(most·recent·call·last)
13368	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13368	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13369	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>	13369	<span·class="ansi-green-intense-fg·ansi-bold">····448</span>·····················retries<span·class="ansi-blue-fg">=</span>self<span·class="ansi-blue-fg">.</span>max_retries<span·class="ansi-blue-fg">,</span>
13370	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout	13370	<span·class="ansi-green-fg">-->·449</span><span·class="ansi-red-fg">·····················</span>timeout<span·class="ansi-blue-fg">=</span>timeout
Offset 13380, 15 lines modified		Offset 13380, 15 lines modified
13380	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>	13380	</span><span·class="ansi-green-intense-fg·ansi-bold">····639</span>·············retries<span·class="ansi-blue-fg">.</span>sleep<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span>

13381	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>	13381	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/urllib3/util/retry.py</span>·in·<span·class="ansi-cyan-fg">increment</span><span·class="ansi-blue-fg">(self,·method,·url,·response,·error,·_pool,·_stacktrace)</span>
13382	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13382	<span·class="ansi-green-intense-fg·ansi-bold">····397</span>·········<span·class="ansi-green-fg">if</span>·new_retry<span·class="ansi-blue-fg">.</span>is_exhausted<span·class="ansi-blue-fg">(</span><span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13383	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>	13383	<span·class="ansi-green-fg">-->·398</span><span·class="ansi-red-fg">·············</span><span·class="ansi-green-fg">raise</span>·MaxRetryError<span·class="ansi-blue-fg">(</span>_pool<span·class="ansi-blue-fg">,</span>·url<span·class="ansi-blue-fg">,</span>·error·<span·class="ansi-green-fg">or</span>·ResponseError<span·class="ansi-blue-fg">(</span>cause<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">)</span>
13384	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·	13384	<span·class="ansi-green-intense-fg·ansi-bold">····399</span>·

13385	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xa~~c7888f~~0>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))	13385	<span·class="ansi-red-fg">MaxRetryError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xabec9a30>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))

13386	During·handling·of·the·above·exception,·another·exception·occurred:	13386	During·handling·of·the·above·exception,·another·exception·occurred:

13387	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)	13387	<span·class="ansi-red-fg">ProxyError</span>································Traceback·(most·recent·call·last)
13388	<span·class="ansi-green-fg"><ipython-input-3-074aec8a1161></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>	13388	<span·class="ansi-green-fg"><ipython-input-3-074aec8a1161></span>·in·<span·class="ansi-cyan-fg"><module></span><span·class="ansi-blue-fg">()</span>
13389	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·	13389	<span·class="ansi-green-intense-fg·ansi-bold">······4</span>·
13390	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>	13390	<span·class="ansi-green-intense-fg·ansi-bold">······5</span>·<span·class="ansi-red-fg">#·Download·the·dataset</span>
Offset 13427, 15 lines modified		Offset 13427, 15 lines modified
13427	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>	13427	<span·class="ansi-green-fg">/usr/lib/python3/dist-packages/requests/adapters.py</span>·in·<span·class="ansi-cyan-fg">send</span><span·class="ansi-blue-fg">(self,·request,·stream,·timeout,·verify,·cert,·proxies)</span>
13428	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·	13428	<span·class="ansi-green-intense-fg·ansi-bold">····508</span>·
13429	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13429	<span·class="ansi-green-intense-fg·ansi-bold">····509</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_ProxyError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>
13430	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>	13430	<span·class="ansi-green-fg">-->·510</span><span·class="ansi-red-fg">·················</span><span·class="ansi-green-fg">raise</span>·ProxyError<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">,</span>·request<span·class="ansi-blue-fg">=</span>request<span·class="ansi-blue-fg">)</span>
13431	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·	13431	<span·class="ansi-green-intense-fg·ansi-bold">····511</span>·
13432	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>	13432	<span·class="ansi-green-intense-fg·ansi-bold">····512</span>·············<span·class="ansi-green-fg">if</span>·isinstance<span·class="ansi-blue-fg">(</span>e<span·class="ansi-blue-fg">.</span>reason<span·class="ansi-blue-fg">,</span>·_SSLError<span·class="ansi-blue-fg">)</span><span·class="ansi-blue-fg">:</span>

13433	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xa~~c7888f~~0>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>	13433	<span·class="ansi-red-fg">ProxyError</span>:·HTTPConnectionPool(host='127.0.0.1',·port=9):·Max·retries·exceeded·with·url:·http://www.ssfpack.com/files/DK-data.zip·(Caused·by·ProxyError('Cannot·connect·to·proxy.',·NewConnectionError('<urllib3.connection.HTTPConnection·object·at·0xabec9a30>:·Failed·to·establish·a·new·connection:·[Errno·111]·Connection·refused')))</pre>
13434	</div>	13434	</div>
13435	</div>	13435	</div>

13436	</div>	13436	</div>
13437	</div>	13437	</div>

13438	</div>	13438	</div>


Offset 13431, 18 lines modified		Offset 13431, 14 lines modified
13431	<div·class="output_subarea·output_stream·output_stderr·output_text">	13431	<div·class="output_subarea·output_stream·output_stderr·output_text">
13432	<pre>/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1341:·FutureWarning:·Using·a·non-tuple·sequence·for·multidimensional·indexing·is·deprecated;·use·`arr[tuple(seq)]`·instead·of·`arr[seq]`.·In·the·future·this·will·be·interpreted·as·an·array·index,·`arr[np.array(seq)]`,·which·will·result·either·in·an·error·or·a·different·result.	13432	<pre>/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1341:·FutureWarning:·Using·a·non-tuple·sequence·for·multidimensional·indexing·is·deprecated;·use·`arr[tuple(seq)]`·instead·of·`arr[seq]`.·In·the·future·this·will·be·interpreted·as·an·array·index,·`arr[np.array(seq)]`,·which·will·result·either·in·an·error·or·a·different·result.
13433	··out_full[ind]·+=·zi	13433	··out_full[ind]·+=·zi
13434	/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1344:·FutureWarning:·Using·a·non-tuple·sequence·for·multidimensional·indexing·is·deprecated;·use·`arr[tuple(seq)]`·instead·of·`arr[seq]`.·In·the·future·this·will·be·interpreted·as·an·array·index,·`arr[np.array(seq)]`,·which·will·result·either·in·an·error·or·a·different·result.	13434	/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1344:·FutureWarning:·Using·a·non-tuple·sequence·for·multidimensional·indexing·is·deprecated;·use·`arr[tuple(seq)]`·instead·of·`arr[seq]`.·In·the·future·this·will·be·interpreted·as·an·array·index,·`arr[np.array(seq)]`,·which·will·result·either·in·an·error·or·a·different·result.
13435	··out·=·out_full[ind]	13435	··out·=·out_full[ind]
13436	/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1350:·FutureWarning:·Using·a·non-tuple·sequence·for·multidimensional·indexing·is·deprecated;·use·`arr[tuple(seq)]`·instead·of·`arr[seq]`.·In·the·future·this·will·be·interpreted·as·an·array·index,·`arr[np.array(seq)]`,·which·will·result·either·in·an·error·or·a·different·result.	13436	/usr/lib/python3/dist-packages/scipy/signal/signaltools.py:1350:·FutureWarning:·Using·a·non-tuple·sequence·for·multidimensional·indexing·is·deprecated;·use·`arr[tuple(seq)]`·instead·of·`arr[seq]`.·In·the·future·this·will·be·interpreted·as·an·array·index,·`arr[np.array(seq)]`,·which·will·result·either·in·an·error·or·a·different·result.
13437	··zf·=·out_full[ind]	13437	··zf·=·out_full[ind]
13438	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
13439	··if·issubdtype(paramsdtype,·float):
13440	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`complex`·to·`np.complexfloating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.complex128·==·np.dtype(complex).type`.
13441	··elif·issubdtype(paramsdtype,·complex):
13442	</pre>	13438	</pre>
13443	</div>	13439	</div>
13444	</div>	13440	</div>

13445	<div·class="output_area">	13441	<div·class="output_area">

13446	····<div·class="prompt"></div>	13442	····<div·class="prompt"></div>
Offset 13453, 14 lines modified		Offset 13449, 28 lines modified
13453	ar.L1.SUNACTIVITY·····1.390656	13449	ar.L1.SUNACTIVITY·····1.390656
13454	ar.L2.SUNACTIVITY····-0.688571	13450	ar.L2.SUNACTIVITY····-0.688571
13455	dtype:·float64	13451	dtype:·float64
13456	</pre>	13452	</pre>
13457	</div>	13453	</div>
13458	</div>	13454	</div>

		13455	<div·class="output_area">

		13456	····<div·class="prompt"></div>


		13457	<div·class="output_subarea·output_stream·output_stderr·output_text">
		13458	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
		13459	··if·issubdtype(paramsdtype,·float):
		13460	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`complex`·to·`np.complexfloating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.complex128·==·np.dtype(complex).type`.
		13461	··elif·issubdtype(paramsdtype,·complex):
		13462	</pre>
		13463	</div>
		13464	</div>

13459	</div>	13465	</div>
13460	</div>	13466	</div>

13461	</div>	13467	</div>
13462	<div·class="cell·border-box-sizing·code_cell·rendered">	13468	<div·class="cell·border-box-sizing·code_cell·rendered">
13463	<div·class="input">	13469	<div·class="input">
13464	<div·class="prompt·input_prompt">In [9]:</div>	13470	<div·class="prompt·input_prompt">In [9]:</div>
Offset 14415, 16 lines modified		Offset 14425, 14 lines modified

14415	····<div·class="prompt"></div>	14425	····<div·class="prompt"></div>


14416	<div·class="output_subarea·output_stream·output_stderr·output_text">	14426	<div·class="output_subarea·output_stream·output_stderr·output_text">
14417	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.	14427	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:646:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
14418	··if·issubdtype(paramsdtype,·float):	14428	··if·issubdtype(paramsdtype,·float):
14419	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`complex`·to·`np.complexfloating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.complex128·==·np.dtype(complex).type`.
14420	··elif·issubdtype(paramsdtype,·complex):
14421	</pre>	14429	</pre>
14422	</div>	14430	</div>
14423	</div>	14431	</div>

14424	<div·class="output_area">	14432	<div·class="output_area">

14425	····<div·class="prompt"></div>	14433	····<div·class="prompt"></div>
Offset 14479, 15 lines modified		Offset 14487, 17 lines modified

14479	<div·class="output_area">	14487	<div·class="output_area">

14480	····<div·class="prompt"></div>	14488	····<div·class="prompt"></div>


14481	<div·class="output_subarea·output_stream·output_stderr·output_text">	14489	<div·class="output_subarea·output_stream·output_stderr·output_text">
14482	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:~~577~~:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`~~float~~`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.fl~~oat64~~·==·np.dtype(~~float~~).type`.	14490	<pre>/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:650:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`complex`·to·`np.complexfloating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.complex128·==·np.dtype(complex).type`.
		14491	··elif·issubdtype(paramsdtype,·complex):
		14492	/build/statsmodels-0.8.0/.pybuild/cpython3_3.7_statsmodels/build/statsmodels/tsa/kalmanf/kalmanfilter.py:577:·FutureWarning:·Conversion·of·the·second·argument·of·issubdtype·from·`float`·to·`np.floating`·is·deprecated.·In·future,·it·will·be·treated·as·`np.float64·==·np.dtype(float).type`.
14483	··if·issubdtype(paramsdtype,·float):	14493	··if·issubdtype(paramsdtype,·float):
14484	</pre>	14494	</pre>
14485	</div>	14495	</div>
14486	</div>	14496	</div>

14487	</div>	14497	</div>
14488	</div>	14498	</div>


Offset 13372, 16 lines modified		Offset 13372, 16 lines modified

13372	<div·class="output_subarea·output_stream·output_stdout·output_text">	13372	<div·class="output_subarea·output_stream·output_stdout·output_text">
13373	<pre>······························ARMA·Model·Results······························	13373	<pre>······························ARMA·Model·Results······························
13374	==============================================================================	13374	==============================================================================
13375	Dep.·Variable:······················y···No.·Observations:··················250	13375	Dep.·Variable:······················y···No.·Observations:··················250
13376	Model:·····················ARMA(2,·2)···Log·Likelihood················-353.445	13376	Model:·····················ARMA(2,·2)···Log·Likelihood················-353.445
13377	Method:·······················css-mle···S.D.·of·innovations··············0.990	13377	Method:·······················css-mle···S.D.·of·innovations··············0.990
13378	Date:················~~Wed~~,·10·Jun·2020···AIC····························716.891	13378	Date:················Fri,·12·Jun·2020···AIC····························716.891
13379	Time:························23:15:38···BIC····························734.498	13379	Time:························07:44:26···BIC····························734.498
13380	Sample:····················01-31-1980···HQIC···························723.977	13380	Sample:····················01-31-1980···HQIC···························723.977
13381	·························-·10-31-2000·········································	13381	·························-·10-31-2000·········································
13382	==============================================================================	13382	==============================================================================
13383	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]	13383	·················coef····std·err··········z······P>\|z\|······[0.025······0.975]
13384	------------------------------------------------------------------------------	13384	------------------------------------------------------------------------------
13385	ar.L1.y········0.7904······0.134······5.878······0.000·······0.527·······1.054	13385	ar.L1.y········0.7904······0.134······5.878······0.000·······0.527·······1.054
13386	ar.L2.y·······-0.2314······0.113·····-2.044······0.042······-0.453······-0.009	13386	ar.L2.y·······-0.2314······0.113·····-2.044······0.042······-0.453······-0.009


Offset 13315, 16 lines modified		Offset 13315, 16 lines modified

13315	<div·class="output_subarea·output_stream·output_stdout·output_text">	13315	<div·class="output_subarea·output_stream·output_stdout·output_text">
13316	<pre>····························WLS·Regression·Results····························	13316	<pre>····························WLS·Regression·Results····························
13317	==============================================================================	13317	==============================================================================
13318	Dep.·Variable:······················y···R-squared:·······················0.910	13318	Dep.·Variable:······················y···R-squared:·······················0.910
13319	Model:····························WLS···Adj.·R-squared:··················0.909	13319	Model:····························WLS···Adj.·R-squared:··················0.909
13320	Method:·················Least·Squares···F-statistic:·····················487.9	13320	Method:·················Least·Squares···F-statistic:·····················487.9
13321	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········8.52e-27	13321	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········8.52e-27
13322	Time:························23:12:26···Log-Likelihood:················-57.048	13322	Time:························07:39:35···Log-Likelihood:················-57.048
13323	No.·Observations:··················50···AIC:·····························118.1	13323	No.·Observations:··················50···AIC:·····························118.1
13324	Df·Residuals:······················48···BIC:·····························121.9	13324	Df·Residuals:······················48···BIC:·····························121.9
13325	Df·Model:···························1·········································	13325	Df·Model:···························1·········································
13326	Covariance·Type:············nonrobust·········································	13326	Covariance·Type:············nonrobust·········································
13327	==============================================================================	13327	==============================================================================
13328	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13328	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13329	------------------------------------------------------------------------------	13329	------------------------------------------------------------------------------
Offset 13573, 16 lines modified		Offset 13573, 16 lines modified

13573	<div·class="output_subarea·output_stream·output_stdout·output_text">	13573	<div·class="output_subarea·output_stream·output_stdout·output_text">
13574	<pre>····························WLS·Regression·Results····························	13574	<pre>····························WLS·Regression·Results····························
13575	==============================================================================	13575	==============================================================================
13576	Dep.·Variable:······················y···R-squared:·······················0.914	13576	Dep.·Variable:······················y···R-squared:·······················0.914
13577	Model:····························WLS···Adj.·R-squared:··················0.912	13577	Model:····························WLS···Adj.·R-squared:··················0.912
13578	Method:·················Least·Squares···F-statistic:·····················507.1	13578	Method:·················Least·Squares···F-statistic:·····················507.1
13579	Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········3.65e-27	13579	Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········3.65e-27
13580	Time:························23:12:36···Log-Likelihood:················-55.777	13580	Time:························07:39:38···Log-Likelihood:················-55.777
13581	No.·Observations:··················50···AIC:·····························115.6	13581	No.·Observations:··················50···AIC:·····························115.6
13582	Df·Residuals:······················48···BIC:·····························119.4	13582	Df·Residuals:······················48···BIC:·····························119.4
13583	Df·Model:···························1·········································	13583	Df·Model:···························1·········································
13584	Covariance·Type:············nonrobust·········································	13584	Covariance·Type:············nonrobust·········································
13585	==============================================================================	13585	==============================================================================
13586	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]	13586	·················coef····std·err··········t······P>\|t\|······[0.025······0.975]
13587	------------------------------------------------------------------------------	13587	------------------------------------------------------------------------------


Offset 105, 16 lines modified		Offset 105, 16 lines modified
105	<span·class="go">===================================================================================</span>	105	<span·class="go">===================================================================================</span>
106	<span·class="go">Dep.·Variable:···························y···No.·Observations:··················236</span>	106	<span·class="go">Dep.·Variable:···························y···No.·Observations:··················236</span>
107	<span·class="go">Model:·································GEE···No.·clusters:·······················59</span>	107	<span·class="go">Model:·································GEE···No.·clusters:·······················59</span>
108	<span·class="go">Method:························Generalized···Min.·cluster·size:···················4</span>	108	<span·class="go">Method:························Generalized···Min.·cluster·size:···················4</span>
109	<span·class="go">······················Estimating·Equations···Max.·cluster·size:···················4</span>	109	<span·class="go">······················Estimating·Equations···Max.·cluster·size:···················4</span>
110	<span·class="go">Family:····························Poisson···Mean·cluster·size:·················4.0</span>	110	<span·class="go">Family:····························Poisson···Mean·cluster·size:·················4.0</span>
111	<span·class="go">Dependence·structure:·········Exchangeable···Num.·iterations:····················51</span>	111	<span·class="go">Dependence·structure:·········Exchangeable···Num.·iterations:····················51</span>
112	<span·class="go">Date:·····················~~Wed~~,·10·Jun·2020···Scale:···························1.000</span>	112	<span·class="go">Date:·····················Fri,·12·Jun·2020···Scale:···························1.000</span>
113	<span·class="go">Covariance·type:····················robust···Time:·························23:37:31</span>	113	<span·class="go">Covariance·type:····················robust···Time:·························07:48:15</span>
114	<span·class="go">====================================================================================</span>	114	<span·class="go">====================================================================================</span>
115	<span·class="go">·······················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>	115	<span·class="go">·······················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>
116	<span·class="gt">------------------------------------------------------------------------------------</span>	116	<span·class="gt">------------------------------------------------------------------------------------</span>
117	<span·class="n">Intercept</span>············<span·class="mf">0.5730</span>······<span·class="mf">0.361</span>······<span·class="mf">1.589</span>······<span·class="mf">0.112</span>······<span·class="o">-</span><span·class="mf">0.134</span>·······<span·class="mf">1.280</span>	117	<span·class="n">Intercept</span>············<span·class="mf">0.5730</span>······<span·class="mf">0.361</span>······<span·class="mf">1.589</span>······<span·class="mf">0.112</span>······<span·class="o">-</span><span·class="mf">0.134</span>·······<span·class="mf">1.280</span>
118	<span·class="n">trt</span><span·class="p">[</span><span·class="n">T</span><span·class="o">.</span><span·class="n">progabide</span><span·class="p">]</span>····<span·class="o">-</span><span·class="mf">0.1519</span>······<span·class="mf">0.171</span>·····<span·class="o">-</span><span·class="mf">0.888</span>······<span·class="mf">0.375</span>······<span·class="o">-</span><span·class="mf">0.487</span>·······<span·class="mf">0.183</span>	118	<span·class="n">trt</span><span·class="p">[</span><span·class="n">T</span><span·class="o">.</span><span·class="n">progabide</span><span·class="p">]</span>····<span·class="o">-</span><span·class="mf">0.1519</span>······<span·class="mf">0.171</span>·····<span·class="o">-</span><span·class="mf">0.888</span>······<span·class="mf">0.375</span>······<span·class="o">-</span><span·class="mf">0.487</span>·······<span·class="mf">0.183</span>
119	<span·class="n">age</span>··················<span·class="mf">0.0223</span>······<span·class="mf">0.011</span>······<span·class="mf">1.960</span>······<span·class="mf">0.050</span>····<span·class="mf">2.11e-06</span>·······<span·class="mf">0.045</span>	119	<span·class="n">age</span>··················<span·class="mf">0.0223</span>······<span·class="mf">0.011</span>······<span·class="mf">1.960</span>······<span·class="mf">0.050</span>····<span·class="mf">2.11e-06</span>·······<span·class="mf">0.045</span>
120	<span·class="n">base</span>·················<span·class="mf">0.0226</span>······<span·class="mf">0.001</span>·····<span·class="mf">18.451</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.020</span>·······<span·class="mf">0.025</span>	120	<span·class="n">base</span>·················<span·class="mf">0.0226</span>······<span·class="mf">0.001</span>·····<span·class="mf">18.451</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.020</span>·······<span·class="mf">0.025</span>


Offset 95, 16 lines modified		Offset 95, 16 lines modified
95	<span·class="go">···················Generalized·Linear·Model·Regression·Results···················</span>	95	<span·class="go">···················Generalized·Linear·Model·Regression·Results···················</span>
96	<span·class="go">=================================================================================</span>	96	<span·class="go">=================================================================================</span>
97	<span·class="go">Dep.·Variable:······················y···No.·Observations:······················32</span>	97	<span·class="go">Dep.·Variable:······················y···No.·Observations:······················32</span>
98	<span·class="go">Model:····························GLM···Df·Residuals:··························24</span>	98	<span·class="go">Model:····························GLM···Df·Residuals:··························24</span>
99	<span·class="go">Model·Family:···················Gamma···Df·Model:·······························7</span>	99	<span·class="go">Model·Family:···················Gamma···Df·Model:·······························7</span>
100	<span·class="go">Link·Function:··········inverse_power···Scale:··············0.0035842831734931545</span>	100	<span·class="go">Link·Function:··········inverse_power···Scale:··············0.0035842831734931545</span>
101	<span·class="go">Method:··························IRLS···Log-Likelihood:···················-83.017</span>	101	<span·class="go">Method:··························IRLS···Log-Likelihood:···················-83.017</span>
102	<span·class="go">Date:················~~Thu~~,·11·Jun·2020···Deviance:························0.087389</span>	102	<span·class="go">Date:················Fri,·12·Jun·2020···Deviance:························0.087389</span>
103	<span·class="go">Time:························00:26:03···Pearson·chi2:······················0.0860</span>	103	<span·class="go">Time:························08:08:07···Pearson·chi2:······················0.0860</span>
104	<span·class="go">No.·Iterations:·····················4············································</span>	104	<span·class="go">No.·Iterations:·····················4············································</span>
105	<span·class="go">==============================================================================</span>	105	<span·class="go">==============================================================================</span>
106	<span·class="go">·················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>	106	<span·class="go">·················coef····std·err··········z······P>\|z\|······[0.025······0.975]</span>
107	<span·class="gt">------------------------------------------------------------------------------</span>	107	<span·class="gt">------------------------------------------------------------------------------</span>
108	<span·class="n">const</span>·········<span·class="o">-</span><span·class="mf">0.0178</span>······<span·class="mf">0.011</span>·····<span·class="o">-</span><span·class="mf">1.548</span>······<span·class="mf">0.122</span>······<span·class="o">-</span><span·class="mf">0.040</span>·······<span·class="mf">0.005</span>	108	<span·class="n">const</span>·········<span·class="o">-</span><span·class="mf">0.0178</span>······<span·class="mf">0.011</span>·····<span·class="o">-</span><span·class="mf">1.548</span>······<span·class="mf">0.122</span>······<span·class="o">-</span><span·class="mf">0.040</span>·······<span·class="mf">0.005</span>
109	<span·class="n">x1</span>··········<span·class="mf">4.962e-05</span>···<span·class="mf">1.62e-05</span>······<span·class="mf">3.060</span>······<span·class="mf">0.002</span>····<span·class="mf">1.78e-05</span>····<span·class="mf">8.14e-05</span>	109	<span·class="n">x1</span>··········<span·class="mf">4.962e-05</span>···<span·class="mf">1.62e-05</span>······<span·class="mf">3.060</span>······<span·class="mf">0.002</span>····<span·class="mf">1.78e-05</span>····<span·class="mf">8.14e-05</span>
110	<span·class="n">x2</span>·············<span·class="mf">0.0020</span>······<span·class="mf">0.001</span>······<span·class="mf">3.824</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.001</span>·······<span·class="mf">0.003</span>	110	<span·class="n">x2</span>·············<span·class="mf">0.0020</span>······<span·class="mf">0.001</span>······<span·class="mf">3.824</span>······<span·class="mf">0.000</span>·······<span·class="mf">0.001</span>·······<span·class="mf">0.003</span>


Offset 99, 16 lines modified		Offset 99, 16 lines modified
99	<span·class="go">#·Inspect·the·results</span>	99	<span·class="go">#·Inspect·the·results</span>
100	<span·class="gp">In·[6]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>	100	<span·class="gp">In·[6]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>
101	<span·class="go">····························OLS·Regression·Results····························</span>	101	<span·class="go">····························OLS·Regression·Results····························</span>
102	<span·class="go">==============================================================================</span>	102	<span·class="go">==============================================================================</span>
103	<span·class="go">Dep.·Variable:················Lottery···R-squared:·······················0.348</span>	103	<span·class="go">Dep.·Variable:················Lottery···R-squared:·······················0.348</span>
104	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.333</span>	104	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.333</span>
105	<span·class="go">Method:·················Least·Squares···F-statistic:·····················22.20</span>	105	<span·class="go">Method:·················Least·Squares···F-statistic:·····················22.20</span>
106	<span·class="go">Date:················~~Thu~~,·11·Jun·2020···Prob·(F-statistic):···········1.90e-08</span>	106	<span·class="go">Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········1.90e-08</span>
107	<span·class="go">Time:························00:26:17···Log-Likelihood:················-379.82</span>	107	<span·class="go">Time:························08:08:14···Log-Likelihood:················-379.82</span>
108	<span·class="go">No.·Observations:··················86···AIC:·····························765.6</span>	108	<span·class="go">No.·Observations:··················86···AIC:·····························765.6</span>
109	<span·class="go">Df·Residuals:······················83···BIC:·····························773.0</span>	109	<span·class="go">Df·Residuals:······················83···BIC:·····························773.0</span>
110	<span·class="go">Df·Model:···························2·········································</span>	110	<span·class="go">Df·Model:···························2·········································</span>
111	<span·class="go">Covariance·Type:············nonrobust·········································</span>	111	<span·class="go">Covariance·Type:············nonrobust·········································</span>
112	<span·class="go">===================================================================================</span>	112	<span·class="go">===================================================================================</span>
113	<span·class="go">······················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>	113	<span·class="go">······················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>
114	<span·class="gt">-----------------------------------------------------------------------------------</span>	114	<span·class="gt">-----------------------------------------------------------------------------------</span>
Offset 150, 16 lines modified		Offset 150, 16 lines modified
150	<span·class="go">#·Inspect·the·results</span>	150	<span·class="go">#·Inspect·the·results</span>
151	<span·class="gp">In·[16]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>	151	<span·class="gp">In·[16]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">results</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>
152	<span·class="go">····························OLS·Regression·Results····························</span>	152	<span·class="go">····························OLS·Regression·Results····························</span>
153	<span·class="go">==============================================================================</span>	153	<span·class="go">==============================================================================</span>
154	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.260</span>	154	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.260</span>
155	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.245</span>	155	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.245</span>
156	<span·class="go">Method:·················Least·Squares···F-statistic:·····················17.06</span>	156	<span·class="go">Method:·················Least·Squares···F-statistic:·····················17.06</span>
157	<span·class="go">Date:················~~Thu~~,·11·Jun·2020···Prob·(F-statistic):···········4.49e-07</span>	157	<span·class="go">Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········4.49e-07</span>
158	<span·class="go">Time:························00:26:17···Log-Likelihood:················-23.039</span>	158	<span·class="go">Time:························08:08:15···Log-Likelihood:················-23.039</span>
159	<span·class="go">No.·Observations:·················100···AIC:·····························52.08</span>	159	<span·class="go">No.·Observations:·················100···AIC:·····························52.08</span>
160	<span·class="go">Df·Residuals:······················97···BIC:·····························59.89</span>	160	<span·class="go">Df·Residuals:······················97···BIC:·····························59.89</span>
161	<span·class="go">Df·Model:···························2·········································</span>	161	<span·class="go">Df·Model:···························2·········································</span>
162	<span·class="go">Covariance·Type:············nonrobust·········································</span>	162	<span·class="go">Covariance·Type:············nonrobust·········································</span>
163	<span·class="go">==============================================================================</span>	163	<span·class="go">==============================================================================</span>
164	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>	164	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>
165	<span·class="gt">------------------------------------------------------------------------------</span>	165	<span·class="gt">------------------------------------------------------------------------------</span>


Offset 98, 16 lines modified		Offset 98, 16 lines modified

98	<span·class="gp">In·[7]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">res</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>	98	<span·class="gp">In·[7]:·</span><span·class="k">print</span><span·class="p">(</span><span·class="n">res</span><span·class="o">.</span><span·class="n">summary</span><span·class="p">())</span>
99	<span·class="go">····························OLS·Regression·Results····························</span>	99	<span·class="go">····························OLS·Regression·Results····························</span>
100	<span·class="go">==============================================================================</span>	100	<span·class="go">==============================================================================</span>
101	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.416</span>	101	<span·class="go">Dep.·Variable:······················y···R-squared:·······················0.416</span>
102	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.353</span>	102	<span·class="go">Model:····························OLS···Adj.·R-squared:··················0.353</span>
103	<span·class="go">Method:·················Least·Squares···F-statistic:·····················6.646</span>	103	<span·class="go">Method:·················Least·Squares···F-statistic:·····················6.646</span>
104	<span·class="go">Date:················~~Thu~~,·11·Jun·2020···Prob·(F-statistic):············0.00157</span>	104	<span·class="go">Date:················Fri,·12·Jun·2020···Prob·(F-statistic):············0.00157</span>
105	<span·class="go">Time:························00:26:33···Log-Likelihood:················-12.978</span>	105	<span·class="go">Time:························08:08:23···Log-Likelihood:················-12.978</span>
106	<span·class="go">No.·Observations:··················32···AIC:·····························33.96</span>	106	<span·class="go">No.·Observations:··················32···AIC:·····························33.96</span>
107	<span·class="go">Df·Residuals:······················28···BIC:·····························39.82</span>	107	<span·class="go">Df·Residuals:······················28···BIC:·····························39.82</span>
108	<span·class="go">Df·Model:···························3·········································</span>	108	<span·class="go">Df·Model:···························3·········································</span>
109	<span·class="go">Covariance·Type:············nonrobust·········································</span>	109	<span·class="go">Covariance·Type:············nonrobust·········································</span>
110	<span·class="go">==============================================================================</span>	110	<span·class="go">==============================================================================</span>
111	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>	111	<span·class="go">·················coef····std·err··········t······P>\|t\|······[0.025······0.975]</span>
112	<span·class="gt">------------------------------------------------------------------------------</span>	112	<span·class="gt">------------------------------------------------------------------------------</span>


Offset 658, 20 lines modified		Offset 658, 20 lines modified
00002910:·2020·2020·2020·2020·2020·2020·2020·2020··················		00002910:·2020·2020·2020·2020·2020·2020·2020·2020··················
00002920:·2020·2020·5641·523c·2f73·7061·6e3e·0a3c······VAR</span>.<		00002920:·2020·2020·5641·523c·2f73·7061·6e3e·0a3c······VAR</span>.<
00002930:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">		00002930:·7370·616e·2063·6c61·7373·3d22·676f·223e··span·class="go">
00002940:·4d65·7468·6f64·3a20·2020·2020·2020·2020··Method:·········		00002940:·4d65·7468·6f64·3a20·2020·2020·2020·2020··Method:·········
00002950:·2020·2020·2020·2020·2020·2020·2020·204f·················O		00002950:·2020·2020·2020·2020·2020·2020·2020·204f·················O
00002960:·4c53·3c2f·7370·616e·3e0a·3c73·7061·6e20··LS</span>.<span·		00002960:·4c53·3c2f·7370·616e·3e0a·3c73·7061·6e20··LS</span>.<span·
00002970:·636c·6173·733d·2267·6f22·3e44·6174·653a··class="go">Date:		00002970:·636c·6173·733d·2267·6f22·3e44·6174·653a··class="go">Date:
00002980:·2020·2020·2020·2020·2020·2054·~~6875~~·2c20·············~~Thu~~,·		00002980:·2020·2020·2020·2020·2020·2046·7269·2c20·············Fri,·
00002990:·3131·2c20·4a75·6e2c·2032·3032·303c·2f73··11,·Jun,·2020</s		00002990:·3132·2c20·4a75·6e2c·2032·3032·303c·2f73··12,·Jun,·2020</s
000029a0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class		000029a0:·7061·6e3e·0a3c·7370·616e·2063·6c61·7373··pan>.<span·class
000029b0:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····		000029b0:·3d22·676f·223e·5469·6d65·3a20·2020·2020··="go">Time:·····
000029c0:·2020·2020·2020·2020·2020·2020·2020·2020··················		000029c0:·2020·2020·2020·2020·2020·2020·2020·2020··················
000029d0:·3030·3a32·383a·3133·3c2f·7370·616e·3e0a··00:28:13</span>.		000029d0:·3038·3a31·303a·3136·3c2f·7370·616e·3e0a··08:10:16</span>.
000029e0:·3c73·7061·6e20·636c·6173·733d·2267·7422··<span·class="gt"		000029e0:·3c73·7061·6e20·636c·6173·733d·2267·7422··<span·class="gt"
000029f0:·3e2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··>---------------		000029f0:·3e2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··>---------------
00002a00:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------		00002a00:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------
00002a10:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------		00002a10:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------
00002a20:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------		00002a20:·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d·2d2d··----------------
00002a30:·2d2d·2d2d·2d3c·2f73·7061·6e3e·0a3c·7370··-----</span>.<sp		00002a30:·2d2d·2d2d·2d3c·2f73·7061·6e3e·0a3c·7370··-----</span>.<sp
00002a40:·616e·2063·6c61·7373·3d22·6e22·3e4e·6f3c··an·class="n">No<		00002a40:·616e·2063·6c61·7373·3d22·6e22·3e4e·6f3c··an·class="n">No<


Offset 1, 13 lines modified		Offset 1, 13 lines modified
1	Package:·python-statsmodels	1	Package:·python-statsmodels
2	Source:·statsmodels	2	Source:·statsmodels
3	Version:·0.8.0-9	3	Version:·0.8.0-9
4	Architecture:·all	4	Architecture:·all
5	Maintainer:·Debian·Science·Maintainers·<debian-science-maintainers@lists.alioth.debian.org>	5	Maintainer:·Debian·Science·Maintainers·<debian-science-maintainers@lists.alioth.debian.org>
6	Installed-Size:·19511	6	Installed-Size:·19508
7	Depends:·python-numpy,·python:any·(<<·2.8),·python:any·(>=·2.7~),·python-scipy,·python-statsmodels-lib·(>=·0.8.0-9),·python-patsy,·python-pandas	7	Depends:·python-numpy,·python:any·(<<·2.8),·python:any·(>=·2.7~),·python-scipy,·python-statsmodels-lib·(>=·0.8.0-9),·python-patsy,·python-pandas
8	Recommends:·python-matplotlib,·python-joblib,·python-cvxopt	8	Recommends:·python-matplotlib,·python-joblib,·python-cvxopt
9	Suggests:·python-statsmodels-doc	9	Suggests:·python-statsmodels-doc
10	Breaks:·python-scikits-statsmodels,·python-scikits.statsmodels·(<<·0.4)	10	Breaks:·python-scikits-statsmodels,·python-scikits.statsmodels·(<<·0.4)
11	Replaces:·python-scikits-statsmodels,·python-scikits.statsmodels·(<<·0.4)	11	Replaces:·python-scikits-statsmodels,·python-scikits.statsmodels·(<<·0.4)
12	Provides:·python2.7-statsmodels	12	Provides:·python2.7-statsmodels
13	Section:·python	13	Section:·python


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943	-rw-r--r--···0·root·········(0)·root·········(0)······284·2019-02-13·21:33:00.000000·./usr/share/doc/python-statsmodels/TODO.Debian	943	-rw-r--r--···0·root·········(0)·root·········(0)······284·2019-02-13·21:33:00.000000·./usr/share/doc/python-statsmodels/TODO.Debian
944	-rw-r--r--···0·root·········(0)·root·········(0)·····6320·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels/changelog.Debian.gz	944	-rw-r--r--···0·root·········(0)·root·········(0)·····6320·2019-03-02·14:59:35.000000·./usr/share/doc/python-statsmodels/changelog.Debian.gz
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204	····················"output_type":·"stream",	204	····················"output_type":·"stream",
205	····················"text":·[	205	····················"text":·[
206	························"····························WLS·Regression·Results····························\n",	206	························"····························WLS·Regression·Results····························\n",
207	························"==============================================================================\n",	207	························"==============================================================================\n",
208	························"Dep.·Variable:······················y···R-squared:·······················0.400\n",	208	························"Dep.·Variable:······················y···R-squared:·······················0.400\n",
209	························"Model:····························WLS···Adj.·R-squared:··················0.367\n",	209	························"Model:····························WLS···Adj.·R-squared:··················0.367\n",
210	························"Method:·················Least·Squares···F-statistic:·····················193.5\n",	210	························"Method:·················Least·Squares···F-statistic:·····················193.5\n",
211	························"Date:················~~Wed~~,·10·Jun·2020···Prob·(F-statistic):···········4.52e-11\n",	211	························"Date:················Fri,·12·Jun·2020···Prob·(F-statistic):···········4.52e-11\n",
212	························"Time:························23:14:10···Log-Likelihood:················-119.06\n",	212	························"Time:························07:39:38···Log-Likelihood:················-119.06\n",
213	························"No.·Observations:··················20···AIC:·····························242.1\n",	213	························"No.·Observations:··················20···AIC:·····························242.1\n",
214	························"Df·Residuals:······················18···BIC:·····························244.1\n",	214	························"Df·Residuals:······················18···BIC:·····························244.1\n",
215	························"Df·Model:···························1·········································\n",	215	························"Df·Model:···························1·········································\n",
216	························"Covariance·Type:··········fixed·scale·········································\n",	216	························"Covariance·Type:··········fixed·scale·········································\n",
217	························"==============================================================================\n",	217	························"==============================================================================\n",
218	························"·················coef····std·err··········z······P>\|z\|······[0.025······0.975]\n",	218	························"·················coef····std·err··········z······P>\|z\|······[0.025······0.975]\n",
219	························"------------------------------------------------------------------------------\n",	219	························"------------------------------------------------------------------------------\n",