{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Voters" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import statsmodels.api as sm" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Replicate Figure 1\n", "\n", "We replicate Figure 1 by rewriting `Dofiles/Analysis/analysis_votechoice.do` in Python. This draws data from `Data/Analysis/analysis_indiv.dta` to run the following regression:\n", "\n", "> `reg int_act calsurv_dummy* calweekday* caldummy_pos caldistpos_dummy* dummy*, hascons cl(id_clust)`.\n", "\n", "The coefficients of `dummy*` are then used to generate the figure." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Load raw data and subset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " month_pre day_pre month_pos day_pos age id_resp day_elec \n", "0 9.0 11.0 10.0 27.0 56.0 3876.0 29.0 \\\n", "1 8.0 29.0 11.0 4.0 27.0 102.0 29.0 \n", "2 8.0 19.0 10.0 15.0 69.0 3866.0 29.0 \n", "3 9.0 13.0 10.0 19.0 27.0 1805.0 29.0 \n", "4 9.0 11.0 10.0 11.0 50.0 2698.0 29.0 \n", "... ... ... ... ... ... ... ... \n", "336249 9.0 2.0 NaN NaN 29.0 18989.0 14.0 \n", "336250 9.0 6.0 NaN NaN 43.0 16682.0 14.0 \n", "336251 9.0 5.0 NaN NaN 29.0 5249.0 14.0 \n", "336252 9.0 2.0 NaN NaN 46.0 9241.0 14.0 \n", "336253 9.0 3.0 9.0 15.0 53.0 87.0 14.0 \n", "\n", " month_elec year_elec year_pre ... new_int2 new_act2 winner_int \n", "0 9.0 2013.0 2013.0 ... 0.0 0.0 NaN \\\n", "1 9.0 2013.0 2013.0 ... 0.0 0.0 1.0 \n", "2 9.0 2013.0 2013.0 ... NaN 0.0 NaN \n", "3 9.0 2013.0 2013.0 ... NaN 0.0 NaN \n", "4 9.0 2013.0 2013.0 ... 0.0 0.0 NaN \n", "... ... ... ... ... ... ... ... \n", "336249 9.0 2014.0 2014.0 ... NaN NaN NaN \n", "336250 9.0 2014.0 2014.0 ... NaN NaN NaN \n", "336251 9.0 2014.0 2014.0 ... NaN NaN NaN \n", "336252 9.0 2014.0 2014.0 ... 0.0 NaN 0.0 \n", "336253 9.0 2014.0 2014.0 ... 0.0 NaN 0.0 \n", "\n", " winner_act watch small_int small_act small_int2 small_act2 rcs \n", "0 1.0 NaN NaN 0.0 0.0 0.0 1.0 \n", "1 1.0 NaN 0.0 0.0 0.0 0.0 1.0 \n", "2 1.0 NaN NaN 0.0 NaN 0.0 1.0 \n", "3 1.0 NaN NaN 0.0 NaN 0.0 1.0 \n", "4 1.0 NaN NaN 0.0 0.0 0.0 1.0 \n", "... ... ... ... ... ... ... ... \n", "336249 NaN NaN NaN NaN NaN NaN 0.0 \n", "336250 NaN NaN NaN NaN NaN NaN 0.0 \n", "336251 NaN NaN NaN NaN NaN NaN 0.0 \n", "336252 NaN NaN 0.0 NaN 0.0 NaN 0.0 \n", "336253 NaN 1.0 1.0 NaN 1.0 NaN 0.0 \n", "\n", "[336254 rows x 93 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_indiv = pd.read_stata('../../datasets/voters/raw/Data/Analysis/analysis_indiv.dta')\n", "analysis_indiv" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The relevant columns are:\n", "- `survey`: name of survey\n", "- `id_surv`: survey ID (1 to 65)\n", "- `date_elec`: date of election\n", "- `dist_pre`: days before election when survey was taken\n", "- `weekday_pre`: day of the week when pre-election survey was taken\n", "- `dist_pos`: days after election when survey was taken\n", "- `int_act`: dummy whether respondent's intended vote in pre-election survey matches actual vote" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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surveyid_survdate_elecdist_preweekday_predist_posint_act
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200916 rows × 7 columns

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" ], "text/plain": [ " survey id_surv date_elec dist_pre weekday_pre dist_pos int_act\n", "1 AUTNES 1 2013-09-29 31 4 36.0 1\n", "2 AUTNES 1 2013-09-29 41 1 16.0 0\n", "3 AUTNES 1 2013-09-29 16 5 20.0 0\n", "5 AUTNES 1 2013-09-29 23 5 35.0 0\n", "6 AUTNES 1 2013-09-29 33 2 17.0 1\n", "... ... ... ... ... ... ... ...\n", "334785 SNES 65 2014-09-14 12 2 1.0 1\n", "334786 SNES 65 2014-09-14 10 4 1.0 1\n", "334787 SNES 65 2014-09-14 11 3 1.0 1\n", "334788 SNES 65 2014-09-14 12 2 1.0 0\n", "334789 SNES 65 2014-09-14 12 2 1.0 1\n", "\n", "[200916 rows x 7 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = analysis_indiv.copy()\n", "df = df[['survey', 'id_surv', 'date_elec', 'dist_pre', 'weekday_pre', 'dist_pos', 'int_act']]\n", "df = df[(df['dist_pre'] <= 60) & (df['dist_pre'] > 0)]\n", "df.dropna(subset=['int_act'], inplace=True)\n", "\n", "df['id_surv'] = df['id_surv'].astype(int)\n", "df['dist_pre'] = df['dist_pre'].astype(int)\n", "df['weekday_pre'] = df['weekday_pre'].astype(int)\n", "df['int_act'] = df['int_act'].astype(int)\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('1952-11-04 00:00:00')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['date_elec'].min()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare for regression\n", "\n", "Categorical variables must be converted to dummies and then centered on `dist_pre=1`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 False\n", "2 False\n", "3 False\n", "5 False\n", "6 False\n", " ... \n", "334785 False\n", "334786 False\n", "334787 False\n", "334788 False\n", "334789 False\n", "Name: dist_pre, Length: 200916, dtype: bool" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_ref = df['dist_pre'] == 1\n", "sample_ref" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "dummy = pd.get_dummies(df['dist_pre'], prefix='dummy', dtype=int)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "surv_dummy = pd.get_dummies(df['id_surv'], prefix='surv', dtype=int)\n", "surv_mean = surv_dummy.loc[sample_ref, :].mean()\n", "calsurv_dummy = surv_dummy.sub(surv_mean)\n", "calsurv_dummy = calsurv_dummy.iloc[:, 1:]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "weekday_dummy = pd.get_dummies(df['weekday_pre'], prefix='weekday', dtype=int)\n", "weekday_mean = weekday_dummy.loc[sample_ref, :].mean()\n", "calweekday_dummy = weekday_dummy.sub(weekday_mean)\n", "calweekday_dummy = calweekday_dummy.iloc[:, 1:]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "distpos_dummy = pd.get_dummies(df['dist_pos'], prefix='dist', dummy_na=True, dtype=int)\n", "distpos_mean = distpos_dummy.loc[sample_ref, :].mean()\n", "caldistpos_dummy = distpos_dummy.sub(distpos_mean)\n", "caldistpos_dummy = caldistpos_dummy.iloc[:, 1:]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "X = pd.concat([calsurv_dummy, calweekday_dummy, caldistpos_dummy, dummy], axis=1)\n", "y = df['int_act']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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surv_2surv_3surv_4surv_5surv_6surv_7surv_8surv_9surv_10surv_11...dummy_51dummy_52dummy_53dummy_54dummy_55dummy_56dummy_57dummy_58dummy_59dummy_60
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200916 rows × 248 columns

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" ], "text/plain": [ " surv_2 surv_3 surv_4 surv_5 surv_6 surv_7 surv_8 \n", "1 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \\\n", "2 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "3 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "5 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "6 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "... ... ... ... ... ... ... ... \n", "334785 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "334786 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "334787 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "334788 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "334789 -0.016224 -0.13849 -0.155655 -0.141077 -0.014108 -0.01881 -0.013402 \n", "\n", " surv_9 surv_10 surv_11 ... dummy_51 dummy_52 dummy_53 \n", "1 -0.014108 -0.015989 -0.012932 ... 0 0 0 \\\n", "2 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "3 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "5 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "6 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "... ... ... ... ... ... ... ... \n", "334785 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "334786 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "334787 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "334788 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "334789 -0.014108 -0.015989 -0.012932 ... 0 0 0 \n", "\n", " dummy_54 dummy_55 dummy_56 dummy_57 dummy_58 dummy_59 dummy_60 \n", "1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "5 0 0 0 0 0 0 0 \n", "6 0 0 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... \n", "334785 0 0 0 0 0 0 0 \n", "334786 0 0 0 0 0 0 0 \n", "334787 0 0 0 0 0 0 0 \n", "334788 0 0 0 0 0 0 0 \n", "334789 0 0 0 0 0 0 0 \n", "\n", "[200916 rows x 248 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Perform regression" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df['survey'], _ = pd.factorize(df['survey'])\n", "df['date_elec'], _ = pd.factorize(df['date_elec'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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surveyid_survdate_elecdist_preweekday_predist_posint_act
101031436.01
201041116.00
301016520.00
501023535.00
601033217.01
........................
3347851065451221.01
3347861065451041.01
3347871065451131.01
3347881065451221.00
3347891065451221.01
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200916 rows × 7 columns

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" ], "text/plain": [ " survey id_surv date_elec dist_pre weekday_pre dist_pos int_act\n", "1 0 1 0 31 4 36.0 1\n", "2 0 1 0 41 1 16.0 0\n", "3 0 1 0 16 5 20.0 0\n", "5 0 1 0 23 5 35.0 0\n", "6 0 1 0 33 2 17.0 1\n", "... ... ... ... ... ... ... ...\n", "334785 10 65 45 12 2 1.0 1\n", "334786 10 65 45 10 4 1.0 1\n", "334787 10 65 45 11 3 1.0 1\n", "334788 10 65 45 12 2 1.0 0\n", "334789 10 65 45 12 2 1.0 1\n", "\n", "[200916 rows x 7 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: int_act R-squared: 0.072
Model: OLS Adj. R-squared: 0.071
Method: Least Squares F-statistic: nan
Date: Thu, 25 May 2023 Prob (F-statistic): nan
Time: 12:14:17 Log-Likelihood: -99100.
No. Observations: 200916 AIC: 1.987e+05
Df Residuals: 200668 BIC: 2.012e+05
Df Model: 247
Covariance Type: cluster
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coef std err z P>|z| [0.025 0.975]
surv_2 0.1311 0.009 14.673 0.000 0.114 0.149
surv_3 0.1050 0.012 9.114 0.000 0.082 0.128
surv_4 0.1484 0.007 22.369 0.000 0.135 0.161
surv_5 0.1993 0.006 34.835 0.000 0.188 0.211
surv_6 0.0018 0.006 0.297 0.766 -0.010 0.014
surv_7 -0.0542 0.003 -18.761 0.000 -0.060 -0.049
surv_8 -0.0879 0.004 -23.885 0.000 -0.095 -0.081
surv_9 -0.0424 0.003 -12.378 0.000 -0.049 -0.036
surv_10 -0.0261 0.002 -14.318 0.000 -0.030 -0.023
surv_11 0.0186 0.001 17.571 0.000 0.017 0.021
surv_12 -0.0234 0.010 -2.461 0.014 -0.042 -0.005
surv_13 -0.0117 0.003 -3.555 0.000 -0.018 -0.005
surv_14 -0.0210 0.002 -8.975 0.000 -0.026 -0.016
surv_15 -0.0609 0.031 -1.994 0.046 -0.121 -0.001
surv_16 0.0967 0.003 28.467 0.000 0.090 0.103
surv_17 -0.0949 0.003 -36.232 0.000 -0.100 -0.090
surv_18 -0.0192 0.003 -7.282 0.000 -0.024 -0.014
surv_19 -0.0338 0.002 -14.979 0.000 -0.038 -0.029
surv_20 -0.0119 0.002 -5.220 0.000 -0.016 -0.007
surv_21 0.2008 0.004 55.723 0.000 0.194 0.208
surv_22 0.2633 0.003 98.844 0.000 0.258 0.269
surv_23 0.2211 0.005 43.026 0.000 0.211 0.231
surv_24 -0.0978 0.003 -31.933 0.000 -0.104 -0.092
surv_25 -0.0339 0.003 -11.092 0.000 -0.040 -0.028
surv_26 -0.0677 0.002 -35.625 0.000 -0.071 -0.064
surv_27 0.0089 0.002 4.661 0.000 0.005 0.013
surv_28 -0.2647 0.093 -2.845 0.004 -0.447 -0.082
surv_29 -0.2490 0.093 -2.676 0.007 -0.431 -0.067
surv_30 0.2299 0.001 177.669 0.000 0.227 0.232
surv_31 0.1351 0.002 66.140 0.000 0.131 0.139
surv_32 0.1409 0.001 115.053 0.000 0.139 0.143
surv_33 0.1773 0.001 166.216 0.000 0.175 0.179
surv_34 0.2325 0.003 66.853 0.000 0.226 0.239
surv_35 0.2477 0.003 86.169 0.000 0.242 0.253
surv_36 0.2003 0.002 125.179 0.000 0.197 0.203
surv_37 0.1971 0.002 114.849 0.000 0.194 0.200
surv_38 0.0105 0.002 5.665 0.000 0.007 0.014
surv_39 0.1289 0.004 30.273 0.000 0.121 0.137
surv_40 0.0177 0.002 10.193 0.000 0.014 0.021
surv_41 -0.0403 0.010 -4.097 0.000 -0.060 -0.021
surv_42 -0.1246 0.003 -42.495 0.000 -0.130 -0.119
surv_43 0.0563 0.009 6.124 0.000 0.038 0.074
surv_44 0.1851 0.002 79.500 0.000 0.181 0.190
surv_45 0.2186 0.002 144.724 0.000 0.216 0.222
surv_46 0.1201 0.002 70.394 0.000 0.117 0.123
surv_47 0.2410 0.004 67.671 0.000 0.234 0.248
surv_48 0.1943 0.004 53.354 0.000 0.187 0.201
surv_49 0.1328 0.004 33.429 0.000 0.125 0.141
surv_50 0.2204 0.003 81.206 0.000 0.215 0.226
surv_51 0.1825 0.006 31.031 0.000 0.171 0.194
surv_52 0.1958 0.004 49.681 0.000 0.188 0.204
surv_53 0.1969 0.006 33.731 0.000 0.185 0.208
surv_54 -0.1226 0.006 -21.129 0.000 -0.134 -0.111
surv_55 -0.1446 0.006 -24.102 0.000 -0.156 -0.133
surv_56 0.0475 0.006 7.594 0.000 0.035 0.060
surv_57 -0.0168 0.002 -7.466 0.000 -0.021 -0.012
surv_58 0.2192 0.004 61.165 0.000 0.212 0.226
surv_59 0.1690 0.004 38.620 0.000 0.160 0.178
surv_60 0.1847 0.003 53.875 0.000 0.178 0.191
surv_61 0.1604 0.003 46.577 0.000 0.154 0.167
surv_62 -0.0823 0.003 -30.603 0.000 -0.088 -0.077
surv_63 -0.0256 0.003 -9.385 0.000 -0.031 -0.020
surv_64 0.0493 0.015 3.376 0.001 0.021 0.078
surv_65 0.0542 0.015 3.689 0.000 0.025 0.083
weekday_1 0.0025 0.004 0.596 0.551 -0.006 0.011
weekday_2 0.0080 0.005 1.663 0.096 -0.001 0.017
weekday_3 0.0074 0.004 2.082 0.037 0.000 0.014
weekday_4 0.0083 0.005 1.780 0.075 -0.001 0.018
weekday_5 0.0052 0.006 0.833 0.405 -0.007 0.018
weekday_6 0.0101 0.005 2.072 0.038 0.001 0.020
dist_2.0 -0.0024 0.003 -0.873 0.383 -0.008 0.003
dist_3.0 -0.0051 0.001 -3.416 0.001 -0.008 -0.002
dist_4.0 -0.0050 0.001 -4.585 0.000 -0.007 -0.003
dist_5.0 -0.0043 0.005 -0.832 0.406 -0.014 0.006
dist_6.0 -0.0116 0.006 -1.930 0.054 -0.023 0.000
dist_7.0 -0.0195 0.006 -3.334 0.001 -0.031 -0.008
dist_8.0 -0.0220 0.005 -4.088 0.000 -0.032 -0.011
dist_9.0 -0.0068 0.005 -1.429 0.153 -0.016 0.003
dist_10.0 -0.0092 0.011 -0.826 0.409 -0.031 0.013
dist_11.0 -0.0194 0.008 -2.380 0.017 -0.035 -0.003
dist_12.0 -0.0071 0.009 -0.810 0.418 -0.024 0.010
dist_13.0 -0.0144 0.006 -2.251 0.024 -0.027 -0.002
dist_14.0 -0.0200 0.008 -2.583 0.010 -0.035 -0.005
dist_15.0 -0.0083 0.009 -0.883 0.377 -0.027 0.010
dist_16.0 -0.0238 0.012 -1.992 0.046 -0.047 -0.000
dist_17.0 -0.0176 0.014 -1.279 0.201 -0.045 0.009
dist_18.0 -0.0292 0.019 -1.563 0.118 -0.066 0.007
dist_19.0 -0.0120 0.015 -0.816 0.414 -0.041 0.017
dist_20.0 -0.0260 0.007 -3.655 0.000 -0.040 -0.012
dist_21.0 -0.0311 0.009 -3.521 0.000 -0.048 -0.014
dist_22.0 -0.0249 0.012 -2.108 0.035 -0.048 -0.002
dist_23.0 -0.0046 0.008 -0.562 0.574 -0.021 0.012
dist_24.0 -0.0237 0.006 -3.876 0.000 -0.036 -0.012
dist_25.0 -0.0123 0.009 -1.325 0.185 -0.030 0.006
dist_26.0 -0.0198 0.013 -1.560 0.119 -0.045 0.005
dist_27.0 -0.0162 0.010 -1.703 0.088 -0.035 0.002
dist_28.0 -0.0140 0.009 -1.539 0.124 -0.032 0.004
dist_29.0 -0.0247 0.013 -1.833 0.067 -0.051 0.002
dist_30.0 -0.0302 0.013 -2.399 0.016 -0.055 -0.006
dist_31.0 -0.0199 0.017 -1.153 0.249 -0.054 0.014
dist_32.0 -0.0251 0.013 -1.906 0.057 -0.051 0.001
dist_33.0 -0.0094 0.009 -1.048 0.295 -0.027 0.008
dist_34.0 -0.0554 0.023 -2.416 0.016 -0.100 -0.010
dist_35.0 -0.0379 0.019 -1.985 0.047 -0.075 -0.000
dist_36.0 -0.0471 0.017 -2.854 0.004 -0.079 -0.015
dist_37.0 -0.0276 0.024 -1.153 0.249 -0.074 0.019
dist_38.0 -0.0425 0.013 -3.339 0.001 -0.067 -0.018
dist_39.0 -0.0471 0.018 -2.643 0.008 -0.082 -0.012
dist_40.0 -0.0255 0.024 -1.072 0.284 -0.072 0.021
dist_41.0 -0.0346 0.014 -2.469 0.014 -0.062 -0.007
dist_42.0 -0.0506 0.012 -4.279 0.000 -0.074 -0.027
dist_43.0 -0.0426 0.017 -2.580 0.010 -0.075 -0.010
dist_44.0 -0.0263 0.011 -2.338 0.019 -0.048 -0.004
dist_45.0 -0.0199 0.030 -0.656 0.512 -0.079 0.039
dist_46.0 -0.0389 0.015 -2.518 0.012 -0.069 -0.009
dist_47.0 -0.0451 0.025 -1.822 0.068 -0.094 0.003
dist_48.0 -0.0346 0.022 -1.550 0.121 -0.078 0.009
dist_49.0 -0.0282 0.014 -2.061 0.039 -0.055 -0.001
dist_50.0 -0.0163 0.008 -2.034 0.042 -0.032 -0.001
dist_51.0 -0.0790 0.021 -3.707 0.000 -0.121 -0.037
dist_52.0 -0.0265 0.010 -2.649 0.008 -0.046 -0.007
dist_53.0 -0.0372 0.017 -2.183 0.029 -0.071 -0.004
dist_54.0 -0.0215 0.015 -1.427 0.153 -0.051 0.008
dist_55.0 -0.0613 0.054 -1.145 0.252 -0.166 0.044
dist_56.0 -0.0376 0.008 -4.784 0.000 -0.053 -0.022
dist_57.0 -0.0619 0.034 -1.799 0.072 -0.129 0.006
dist_58.0 -0.0202 0.027 -0.754 0.451 -0.073 0.032
dist_59.0 -0.0844 0.035 -2.407 0.016 -0.153 -0.016
dist_60.0 0.0057 0.038 0.149 0.881 -0.069 0.080
dist_61.0 0.0018 0.012 0.158 0.874 -0.021 0.025
dist_62.0 -0.0517 0.037 -1.403 0.161 -0.124 0.021
dist_63.0 -0.0799 0.020 -4.021 0.000 -0.119 -0.041
dist_64.0 -0.0292 0.024 -1.203 0.229 -0.077 0.018
dist_65.0 -0.0494 0.018 -2.805 0.005 -0.084 -0.015
dist_66.0 -0.0006 0.051 -0.011 0.991 -0.100 0.099
dist_67.0 -0.0272 0.016 -1.715 0.086 -0.058 0.004
dist_68.0 -0.0528 0.028 -1.908 0.056 -0.107 0.001
dist_69.0 -0.0362 0.032 -1.115 0.265 -0.100 0.027
dist_70.0 -0.0683 0.013 -5.180 0.000 -0.094 -0.042
dist_71.0 -0.0148 0.016 -0.926 0.354 -0.046 0.017
dist_72.0 -0.0827 0.023 -3.615 0.000 -0.128 -0.038
dist_73.0 -0.0828 0.014 -5.895 0.000 -0.110 -0.055
dist_74.0 -0.0559 0.024 -2.358 0.018 -0.102 -0.009
dist_75.0 -0.0347 0.018 -1.931 0.053 -0.070 0.001
dist_76.0 -0.0637 0.016 -3.869 0.000 -0.096 -0.031
dist_77.0 -0.0799 0.032 -2.503 0.012 -0.143 -0.017
dist_78.0 -0.0154 0.022 -0.694 0.488 -0.059 0.028
dist_79.0 -0.0827 0.028 -2.904 0.004 -0.139 -0.027
dist_80.0 -0.0561 0.022 -2.582 0.010 -0.099 -0.014
dist_81.0 -0.0746 0.019 -3.901 0.000 -0.112 -0.037
dist_82.0 -0.0487 0.016 -3.139 0.002 -0.079 -0.018
dist_83.0 -0.0284 0.032 -0.890 0.374 -0.091 0.034
dist_84.0 -0.0265 0.013 -1.978 0.048 -0.053 -0.000
dist_85.0 0.0141 0.020 0.706 0.480 -0.025 0.053
dist_86.0 0.0051 0.083 0.062 0.951 -0.157 0.167
dist_87.0 0.0089 0.033 0.271 0.786 -0.056 0.073
dist_88.0 -0.0980 0.069 -1.417 0.156 -0.234 0.038
dist_89.0 -0.1763 0.093 -1.905 0.057 -0.358 0.005
dist_90.0 -0.1926 0.007 -26.783 0.000 -0.207 -0.179
dist_91.0 -0.3617 0.006 -64.617 0.000 -0.373 -0.351
dist_93.0 0.0271 0.352 0.077 0.939 -0.662 0.716
dist_94.0 -0.0790 0.127 -0.623 0.533 -0.328 0.170
dist_95.0 -0.1500 0.015 -9.831 0.000 -0.180 -0.120
dist_96.0 -0.0238 0.016 -1.455 0.146 -0.056 0.008
dist_97.0 -0.0485 0.011 -4.443 0.000 -0.070 -0.027
dist_98.0 0.0241 0.023 1.062 0.288 -0.020 0.069
dist_99.0 0.0249 0.020 1.230 0.219 -0.015 0.065
dist_100.0 -0.0104 0.005 -2.227 0.026 -0.019 -0.001
dist_101.0 -0.1039 0.029 -3.567 0.000 -0.161 -0.047
dist_102.0 0.0491 0.007 6.817 0.000 0.035 0.063
dist_103.0 -0.1675 0.007 -22.419 0.000 -0.182 -0.153
dist_104.0 0.0140 0.004 3.731 0.000 0.007 0.021
dist_106.0 -0.0234 0.006 -4.138 0.000 -0.034 -0.012
dist_107.0 -0.1331 0.008 -16.402 0.000 -0.149 -0.117
dist_108.0 -0.1094 0.061 -1.781 0.075 -0.230 0.011
dist_109.0 -0.0756 0.007 -10.644 0.000 -0.090 -0.062
dist_110.0 -0.3173 0.015 -21.545 0.000 -0.346 -0.288
dist_111.0 -0.3234 0.009 -37.120 0.000 -0.340 -0.306
dist_114.0 -0.0938 0.065 -1.441 0.149 -0.221 0.034
dist_115.0 -0.0748 0.018 -4.173 0.000 -0.110 -0.040
dist_116.0 0.0533 0.005 10.922 0.000 0.044 0.063
dist_117.0 0.0753 0.004 17.032 0.000 0.067 0.084
dist_118.0 -0.0971 0.005 -18.748 0.000 -0.107 -0.087
dist_119.0 -0.0479 0.007 -6.776 0.000 -0.062 -0.034
dist_121.0 -0.1373 0.008 -18.243 0.000 -0.152 -0.123
dist_122.0 -0.0528 0.007 -7.461 0.000 -0.067 -0.039
dist_123.0 -0.0993 0.009 -11.228 0.000 -0.117 -0.082
dist_nan 0.2368 0.093 2.546 0.011 0.055 0.419
dummy_1 0.8768 0.012 73.977 0.000 0.854 0.900
dummy_2 0.8739 0.020 44.474 0.000 0.835 0.912
dummy_3 0.8547 0.013 68.060 0.000 0.830 0.879
dummy_4 0.8528 0.013 66.303 0.000 0.828 0.878
dummy_5 0.8395 0.009 91.506 0.000 0.822 0.858
dummy_6 0.8389 0.012 68.270 0.000 0.815 0.863
dummy_7 0.8270 0.012 68.571 0.000 0.803 0.851
dummy_8 0.8193 0.006 127.483 0.000 0.807 0.832
dummy_9 0.8141 0.009 94.527 0.000 0.797 0.831
dummy_10 0.8186 0.009 95.389 0.000 0.802 0.835
dummy_11 0.8196 0.008 98.621 0.000 0.803 0.836
dummy_12 0.8093 0.011 74.134 0.000 0.788 0.831
dummy_13 0.7979 0.009 91.692 0.000 0.781 0.815
dummy_14 0.7920 0.006 136.831 0.000 0.781 0.803
dummy_15 0.8009 0.009 86.535 0.000 0.783 0.819
dummy_16 0.7855 0.008 95.460 0.000 0.769 0.802
dummy_17 0.7802 0.006 134.769 0.000 0.769 0.792
dummy_18 0.7793 0.005 159.336 0.000 0.770 0.789
dummy_19 0.7608 0.005 165.405 0.000 0.752 0.770
dummy_20 0.7666 0.008 90.479 0.000 0.750 0.783
dummy_21 0.7763 0.004 193.437 0.000 0.768 0.784
dummy_22 0.7685 0.009 83.916 0.000 0.751 0.786
dummy_23 0.7624 0.007 107.675 0.000 0.748 0.776
dummy_24 0.7524 0.009 88.352 0.000 0.736 0.769
dummy_25 0.7645 0.007 109.601 0.000 0.751 0.778
dummy_26 0.7619 0.008 101.205 0.000 0.747 0.777
dummy_27 0.7562 0.007 115.994 0.000 0.743 0.769
dummy_28 0.7550 0.012 64.369 0.000 0.732 0.778
dummy_29 0.7496 0.009 80.449 0.000 0.731 0.768
dummy_30 0.7489 0.009 80.411 0.000 0.731 0.767
dummy_31 0.7609 0.014 53.037 0.000 0.733 0.789
dummy_32 0.7553 0.010 77.474 0.000 0.736 0.774
dummy_33 0.7380 0.012 61.602 0.000 0.714 0.761
dummy_34 0.7390 0.014 53.602 0.000 0.712 0.766
dummy_35 0.7446 0.010 75.854 0.000 0.725 0.764
dummy_36 0.7504 0.015 48.912 0.000 0.720 0.780
dummy_37 0.7522 0.014 54.524 0.000 0.725 0.779
dummy_38 0.7519 0.008 92.959 0.000 0.736 0.768
dummy_39 0.7376 0.016 46.603 0.000 0.707 0.769
dummy_40 0.7522 0.013 56.892 0.000 0.726 0.778
dummy_41 0.7306 0.016 44.760 0.000 0.699 0.763
dummy_42 0.7442 0.016 46.127 0.000 0.713 0.776
dummy_43 0.7431 0.015 48.644 0.000 0.713 0.773
dummy_44 0.7448 0.012 64.457 0.000 0.722 0.767
dummy_45 0.7395 0.022 33.836 0.000 0.697 0.782
dummy_46 0.7380 0.020 36.567 0.000 0.698 0.778
dummy_47 0.7392 0.007 103.763 0.000 0.725 0.753
dummy_48 0.7392 0.006 118.057 0.000 0.727 0.751
dummy_49 0.7204 0.003 235.312 0.000 0.714 0.726
dummy_50 0.7360 0.014 51.464 0.000 0.708 0.764
dummy_51 0.7607 0.007 106.698 0.000 0.747 0.775
dummy_52 0.7470 0.018 41.705 0.000 0.712 0.782
dummy_53 0.7185 0.024 29.979 0.000 0.671 0.765
dummy_54 0.7162 0.026 27.416 0.000 0.665 0.767
dummy_55 0.7359 0.008 94.731 0.000 0.721 0.751
dummy_56 0.7562 0.013 57.553 0.000 0.730 0.782
dummy_57 0.7456 0.014 54.295 0.000 0.719 0.773
dummy_58 0.7367 0.019 38.880 0.000 0.700 0.774
dummy_59 0.7418 0.010 75.578 0.000 0.723 0.761
dummy_60 0.7061 0.013 53.249 0.000 0.680 0.732
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Omnibus: 32414.573 Durbin-Watson: 1.884
Prob(Omnibus): 0.000 Jarque-Bera (JB): 51459.998
Skew: -1.240 Prob(JB): 0.00
Kurtosis: 2.972 Cond. No. 182.


Notes:
[1] Standard Errors are robust to cluster correlation (cluster)" ], "text/latex": [ "\\begin{center}\n", "\\begin{tabular}{lclc}\n", "\\toprule\n", "\\textbf{Dep. Variable:} & int\\_act & \\textbf{ R-squared: } & 0.072 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.071 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & nan \\\\\n", "\\textbf{Date:} & Thu, 25 May 2023 & \\textbf{ Prob (F-statistic):} & nan \\\\\n", "\\textbf{Time:} & 12:14:17 & \\textbf{ Log-Likelihood: } & -99100. \\\\\n", "\\textbf{No. Observations:} & 200916 & \\textbf{ AIC: } & 1.987e+05 \\\\\n", "\\textbf{Df Residuals:} & 200668 & \\textbf{ BIC: } & 2.012e+05 \\\\\n", "\\textbf{Df Model:} & 247 & \\textbf{ } & \\\\\n", "\\textbf{Covariance Type:} & cluster & \\textbf{ } & \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lcccccc}\n", " & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", "\\midrule\n", "\\textbf{surv\\_2} & 0.1311 & 0.009 & 14.673 & 0.000 & 0.114 & 0.149 \\\\\n", "\\textbf{surv\\_3} & 0.1050 & 0.012 & 9.114 & 0.000 & 0.082 & 0.128 \\\\\n", "\\textbf{surv\\_4} & 0.1484 & 0.007 & 22.369 & 0.000 & 0.135 & 0.161 \\\\\n", "\\textbf{surv\\_5} & 0.1993 & 0.006 & 34.835 & 0.000 & 0.188 & 0.211 \\\\\n", "\\textbf{surv\\_6} & 0.0018 & 0.006 & 0.297 & 0.766 & -0.010 & 0.014 \\\\\n", "\\textbf{surv\\_7} & -0.0542 & 0.003 & -18.761 & 0.000 & -0.060 & -0.049 \\\\\n", "\\textbf{surv\\_8} & -0.0879 & 0.004 & -23.885 & 0.000 & -0.095 & -0.081 \\\\\n", "\\textbf{surv\\_9} & -0.0424 & 0.003 & -12.378 & 0.000 & -0.049 & -0.036 \\\\\n", "\\textbf{surv\\_10} & -0.0261 & 0.002 & -14.318 & 0.000 & -0.030 & -0.023 \\\\\n", "\\textbf{surv\\_11} & 0.0186 & 0.001 & 17.571 & 0.000 & 0.017 & 0.021 \\\\\n", "\\textbf{surv\\_12} & -0.0234 & 0.010 & -2.461 & 0.014 & -0.042 & -0.005 \\\\\n", "\\textbf{surv\\_13} & -0.0117 & 0.003 & -3.555 & 0.000 & -0.018 & -0.005 \\\\\n", "\\textbf{surv\\_14} & -0.0210 & 0.002 & -8.975 & 0.000 & -0.026 & -0.016 \\\\\n", "\\textbf{surv\\_15} & -0.0609 & 0.031 & -1.994 & 0.046 & -0.121 & -0.001 \\\\\n", "\\textbf{surv\\_16} & 0.0967 & 0.003 & 28.467 & 0.000 & 0.090 & 0.103 \\\\\n", "\\textbf{surv\\_17} & -0.0949 & 0.003 & -36.232 & 0.000 & -0.100 & -0.090 \\\\\n", "\\textbf{surv\\_18} & -0.0192 & 0.003 & -7.282 & 0.000 & -0.024 & -0.014 \\\\\n", "\\textbf{surv\\_19} & -0.0338 & 0.002 & -14.979 & 0.000 & -0.038 & -0.029 \\\\\n", "\\textbf{surv\\_20} & -0.0119 & 0.002 & -5.220 & 0.000 & -0.016 & -0.007 \\\\\n", "\\textbf{surv\\_21} & 0.2008 & 0.004 & 55.723 & 0.000 & 0.194 & 0.208 \\\\\n", "\\textbf{surv\\_22} & 0.2633 & 0.003 & 98.844 & 0.000 & 0.258 & 0.269 \\\\\n", "\\textbf{surv\\_23} & 0.2211 & 0.005 & 43.026 & 0.000 & 0.211 & 0.231 \\\\\n", "\\textbf{surv\\_24} & -0.0978 & 0.003 & -31.933 & 0.000 & -0.104 & -0.092 \\\\\n", "\\textbf{surv\\_25} & -0.0339 & 0.003 & -11.092 & 0.000 & -0.040 & -0.028 \\\\\n", "\\textbf{surv\\_26} & -0.0677 & 0.002 & -35.625 & 0.000 & -0.071 & -0.064 \\\\\n", "\\textbf{surv\\_27} & 0.0089 & 0.002 & 4.661 & 0.000 & 0.005 & 0.013 \\\\\n", "\\textbf{surv\\_28} & -0.2647 & 0.093 & -2.845 & 0.004 & -0.447 & -0.082 \\\\\n", "\\textbf{surv\\_29} & -0.2490 & 0.093 & -2.676 & 0.007 & -0.431 & -0.067 \\\\\n", "\\textbf{surv\\_30} & 0.2299 & 0.001 & 177.669 & 0.000 & 0.227 & 0.232 \\\\\n", "\\textbf{surv\\_31} & 0.1351 & 0.002 & 66.140 & 0.000 & 0.131 & 0.139 \\\\\n", "\\textbf{surv\\_32} & 0.1409 & 0.001 & 115.053 & 0.000 & 0.139 & 0.143 \\\\\n", "\\textbf{surv\\_33} & 0.1773 & 0.001 & 166.216 & 0.000 & 0.175 & 0.179 \\\\\n", "\\textbf{surv\\_34} & 0.2325 & 0.003 & 66.853 & 0.000 & 0.226 & 0.239 \\\\\n", "\\textbf{surv\\_35} & 0.2477 & 0.003 & 86.169 & 0.000 & 0.242 & 0.253 \\\\\n", "\\textbf{surv\\_36} & 0.2003 & 0.002 & 125.179 & 0.000 & 0.197 & 0.203 \\\\\n", "\\textbf{surv\\_37} & 0.1971 & 0.002 & 114.849 & 0.000 & 0.194 & 0.200 \\\\\n", "\\textbf{surv\\_38} & 0.0105 & 0.002 & 5.665 & 0.000 & 0.007 & 0.014 \\\\\n", "\\textbf{surv\\_39} & 0.1289 & 0.004 & 30.273 & 0.000 & 0.121 & 0.137 \\\\\n", "\\textbf{surv\\_40} & 0.0177 & 0.002 & 10.193 & 0.000 & 0.014 & 0.021 \\\\\n", "\\textbf{surv\\_41} & -0.0403 & 0.010 & -4.097 & 0.000 & -0.060 & -0.021 \\\\\n", "\\textbf{surv\\_42} & -0.1246 & 0.003 & -42.495 & 0.000 & -0.130 & -0.119 \\\\\n", "\\textbf{surv\\_43} & 0.0563 & 0.009 & 6.124 & 0.000 & 0.038 & 0.074 \\\\\n", "\\textbf{surv\\_44} & 0.1851 & 0.002 & 79.500 & 0.000 & 0.181 & 0.190 \\\\\n", "\\textbf{surv\\_45} & 0.2186 & 0.002 & 144.724 & 0.000 & 0.216 & 0.222 \\\\\n", "\\textbf{surv\\_46} & 0.1201 & 0.002 & 70.394 & 0.000 & 0.117 & 0.123 \\\\\n", "\\textbf{surv\\_47} & 0.2410 & 0.004 & 67.671 & 0.000 & 0.234 & 0.248 \\\\\n", "\\textbf{surv\\_48} & 0.1943 & 0.004 & 53.354 & 0.000 & 0.187 & 0.201 \\\\\n", "\\textbf{surv\\_49} & 0.1328 & 0.004 & 33.429 & 0.000 & 0.125 & 0.141 \\\\\n", "\\textbf{surv\\_50} & 0.2204 & 0.003 & 81.206 & 0.000 & 0.215 & 0.226 \\\\\n", "\\textbf{surv\\_51} & 0.1825 & 0.006 & 31.031 & 0.000 & 0.171 & 0.194 \\\\\n", "\\textbf{surv\\_52} & 0.1958 & 0.004 & 49.681 & 0.000 & 0.188 & 0.204 \\\\\n", "\\textbf{surv\\_53} & 0.1969 & 0.006 & 33.731 & 0.000 & 0.185 & 0.208 \\\\\n", "\\textbf{surv\\_54} & -0.1226 & 0.006 & -21.129 & 0.000 & -0.134 & -0.111 \\\\\n", "\\textbf{surv\\_55} & -0.1446 & 0.006 & -24.102 & 0.000 & -0.156 & -0.133 \\\\\n", "\\textbf{surv\\_56} & 0.0475 & 0.006 & 7.594 & 0.000 & 0.035 & 0.060 \\\\\n", "\\textbf{surv\\_57} & -0.0168 & 0.002 & -7.466 & 0.000 & -0.021 & -0.012 \\\\\n", "\\textbf{surv\\_58} & 0.2192 & 0.004 & 61.165 & 0.000 & 0.212 & 0.226 \\\\\n", "\\textbf{surv\\_59} & 0.1690 & 0.004 & 38.620 & 0.000 & 0.160 & 0.178 \\\\\n", "\\textbf{surv\\_60} & 0.1847 & 0.003 & 53.875 & 0.000 & 0.178 & 0.191 \\\\\n", "\\textbf{surv\\_61} & 0.1604 & 0.003 & 46.577 & 0.000 & 0.154 & 0.167 \\\\\n", "\\textbf{surv\\_62} & -0.0823 & 0.003 & -30.603 & 0.000 & -0.088 & -0.077 \\\\\n", "\\textbf{surv\\_63} & -0.0256 & 0.003 & -9.385 & 0.000 & -0.031 & -0.020 \\\\\n", "\\textbf{surv\\_64} & 0.0493 & 0.015 & 3.376 & 0.001 & 0.021 & 0.078 \\\\\n", "\\textbf{surv\\_65} & 0.0542 & 0.015 & 3.689 & 0.000 & 0.025 & 0.083 \\\\\n", "\\textbf{weekday\\_1} & 0.0025 & 0.004 & 0.596 & 0.551 & -0.006 & 0.011 \\\\\n", "\\textbf{weekday\\_2} & 0.0080 & 0.005 & 1.663 & 0.096 & -0.001 & 0.017 \\\\\n", "\\textbf{weekday\\_3} & 0.0074 & 0.004 & 2.082 & 0.037 & 0.000 & 0.014 \\\\\n", "\\textbf{weekday\\_4} & 0.0083 & 0.005 & 1.780 & 0.075 & -0.001 & 0.018 \\\\\n", "\\textbf{weekday\\_5} & 0.0052 & 0.006 & 0.833 & 0.405 & -0.007 & 0.018 \\\\\n", "\\textbf{weekday\\_6} & 0.0101 & 0.005 & 2.072 & 0.038 & 0.001 & 0.020 \\\\\n", "\\textbf{dist\\_2.0} & -0.0024 & 0.003 & -0.873 & 0.383 & -0.008 & 0.003 \\\\\n", "\\textbf{dist\\_3.0} & -0.0051 & 0.001 & -3.416 & 0.001 & -0.008 & -0.002 \\\\\n", "\\textbf{dist\\_4.0} & -0.0050 & 0.001 & -4.585 & 0.000 & -0.007 & -0.003 \\\\\n", "\\textbf{dist\\_5.0} & -0.0043 & 0.005 & -0.832 & 0.406 & -0.014 & 0.006 \\\\\n", "\\textbf{dist\\_6.0} & -0.0116 & 0.006 & -1.930 & 0.054 & -0.023 & 0.000 \\\\\n", "\\textbf{dist\\_7.0} & -0.0195 & 0.006 & -3.334 & 0.001 & -0.031 & -0.008 \\\\\n", "\\textbf{dist\\_8.0} & -0.0220 & 0.005 & -4.088 & 0.000 & -0.032 & -0.011 \\\\\n", "\\textbf{dist\\_9.0} & -0.0068 & 0.005 & -1.429 & 0.153 & -0.016 & 0.003 \\\\\n", "\\textbf{dist\\_10.0} & -0.0092 & 0.011 & -0.826 & 0.409 & -0.031 & 0.013 \\\\\n", "\\textbf{dist\\_11.0} & -0.0194 & 0.008 & -2.380 & 0.017 & -0.035 & -0.003 \\\\\n", "\\textbf{dist\\_12.0} & -0.0071 & 0.009 & -0.810 & 0.418 & -0.024 & 0.010 \\\\\n", "\\textbf{dist\\_13.0} & -0.0144 & 0.006 & -2.251 & 0.024 & -0.027 & -0.002 \\\\\n", "\\textbf{dist\\_14.0} & -0.0200 & 0.008 & -2.583 & 0.010 & -0.035 & -0.005 \\\\\n", "\\textbf{dist\\_15.0} & -0.0083 & 0.009 & -0.883 & 0.377 & -0.027 & 0.010 \\\\\n", "\\textbf{dist\\_16.0} & -0.0238 & 0.012 & -1.992 & 0.046 & -0.047 & -0.000 \\\\\n", "\\textbf{dist\\_17.0} & -0.0176 & 0.014 & -1.279 & 0.201 & -0.045 & 0.009 \\\\\n", "\\textbf{dist\\_18.0} & -0.0292 & 0.019 & -1.563 & 0.118 & -0.066 & 0.007 \\\\\n", "\\textbf{dist\\_19.0} & -0.0120 & 0.015 & -0.816 & 0.414 & -0.041 & 0.017 \\\\\n", "\\textbf{dist\\_20.0} & -0.0260 & 0.007 & -3.655 & 0.000 & -0.040 & -0.012 \\\\\n", "\\textbf{dist\\_21.0} & -0.0311 & 0.009 & -3.521 & 0.000 & -0.048 & -0.014 \\\\\n", "\\textbf{dist\\_22.0} & -0.0249 & 0.012 & -2.108 & 0.035 & -0.048 & -0.002 \\\\\n", "\\textbf{dist\\_23.0} & -0.0046 & 0.008 & -0.562 & 0.574 & -0.021 & 0.012 \\\\\n", "\\textbf{dist\\_24.0} & -0.0237 & 0.006 & -3.876 & 0.000 & -0.036 & -0.012 \\\\\n", "\\textbf{dist\\_25.0} & -0.0123 & 0.009 & -1.325 & 0.185 & -0.030 & 0.006 \\\\\n", "\\textbf{dist\\_26.0} & -0.0198 & 0.013 & -1.560 & 0.119 & -0.045 & 0.005 \\\\\n", "\\textbf{dist\\_27.0} & -0.0162 & 0.010 & -1.703 & 0.088 & -0.035 & 0.002 \\\\\n", "\\textbf{dist\\_28.0} & -0.0140 & 0.009 & -1.539 & 0.124 & -0.032 & 0.004 \\\\\n", "\\textbf{dist\\_29.0} & -0.0247 & 0.013 & -1.833 & 0.067 & -0.051 & 0.002 \\\\\n", "\\textbf{dist\\_30.0} & -0.0302 & 0.013 & -2.399 & 0.016 & -0.055 & -0.006 \\\\\n", "\\textbf{dist\\_31.0} & -0.0199 & 0.017 & -1.153 & 0.249 & -0.054 & 0.014 \\\\\n", "\\textbf{dist\\_32.0} & -0.0251 & 0.013 & -1.906 & 0.057 & -0.051 & 0.001 \\\\\n", "\\textbf{dist\\_33.0} & -0.0094 & 0.009 & -1.048 & 0.295 & -0.027 & 0.008 \\\\\n", "\\textbf{dist\\_34.0} & -0.0554 & 0.023 & -2.416 & 0.016 & -0.100 & -0.010 \\\\\n", "\\textbf{dist\\_35.0} & -0.0379 & 0.019 & -1.985 & 0.047 & -0.075 & -0.000 \\\\\n", "\\textbf{dist\\_36.0} & -0.0471 & 0.017 & -2.854 & 0.004 & -0.079 & -0.015 \\\\\n", "\\textbf{dist\\_37.0} & -0.0276 & 0.024 & -1.153 & 0.249 & -0.074 & 0.019 \\\\\n", "\\textbf{dist\\_38.0} & -0.0425 & 0.013 & -3.339 & 0.001 & -0.067 & -0.018 \\\\\n", "\\textbf{dist\\_39.0} & -0.0471 & 0.018 & -2.643 & 0.008 & -0.082 & -0.012 \\\\\n", "\\textbf{dist\\_40.0} & -0.0255 & 0.024 & -1.072 & 0.284 & -0.072 & 0.021 \\\\\n", "\\textbf{dist\\_41.0} & -0.0346 & 0.014 & -2.469 & 0.014 & -0.062 & -0.007 \\\\\n", "\\textbf{dist\\_42.0} & -0.0506 & 0.012 & -4.279 & 0.000 & -0.074 & -0.027 \\\\\n", "\\textbf{dist\\_43.0} & -0.0426 & 0.017 & -2.580 & 0.010 & -0.075 & -0.010 \\\\\n", "\\textbf{dist\\_44.0} & -0.0263 & 0.011 & -2.338 & 0.019 & -0.048 & -0.004 \\\\\n", "\\textbf{dist\\_45.0} & -0.0199 & 0.030 & -0.656 & 0.512 & -0.079 & 0.039 \\\\\n", "\\textbf{dist\\_46.0} & -0.0389 & 0.015 & -2.518 & 0.012 & -0.069 & -0.009 \\\\\n", "\\textbf{dist\\_47.0} & -0.0451 & 0.025 & -1.822 & 0.068 & -0.094 & 0.003 \\\\\n", "\\textbf{dist\\_48.0} & -0.0346 & 0.022 & -1.550 & 0.121 & -0.078 & 0.009 \\\\\n", "\\textbf{dist\\_49.0} & -0.0282 & 0.014 & -2.061 & 0.039 & -0.055 & -0.001 \\\\\n", "\\textbf{dist\\_50.0} & -0.0163 & 0.008 & -2.034 & 0.042 & -0.032 & -0.001 \\\\\n", "\\textbf{dist\\_51.0} & -0.0790 & 0.021 & -3.707 & 0.000 & -0.121 & -0.037 \\\\\n", "\\textbf{dist\\_52.0} & -0.0265 & 0.010 & -2.649 & 0.008 & -0.046 & -0.007 \\\\\n", "\\textbf{dist\\_53.0} & -0.0372 & 0.017 & -2.183 & 0.029 & -0.071 & -0.004 \\\\\n", "\\textbf{dist\\_54.0} & -0.0215 & 0.015 & -1.427 & 0.153 & -0.051 & 0.008 \\\\\n", "\\textbf{dist\\_55.0} & -0.0613 & 0.054 & -1.145 & 0.252 & -0.166 & 0.044 \\\\\n", "\\textbf{dist\\_56.0} & -0.0376 & 0.008 & -4.784 & 0.000 & -0.053 & -0.022 \\\\\n", "\\textbf{dist\\_57.0} & -0.0619 & 0.034 & -1.799 & 0.072 & -0.129 & 0.006 \\\\\n", "\\textbf{dist\\_58.0} & -0.0202 & 0.027 & -0.754 & 0.451 & -0.073 & 0.032 \\\\\n", "\\textbf{dist\\_59.0} & -0.0844 & 0.035 & -2.407 & 0.016 & -0.153 & -0.016 \\\\\n", "\\textbf{dist\\_60.0} & 0.0057 & 0.038 & 0.149 & 0.881 & -0.069 & 0.080 \\\\\n", "\\textbf{dist\\_61.0} & 0.0018 & 0.012 & 0.158 & 0.874 & -0.021 & 0.025 \\\\\n", "\\textbf{dist\\_62.0} & -0.0517 & 0.037 & -1.403 & 0.161 & -0.124 & 0.021 \\\\\n", "\\textbf{dist\\_63.0} & -0.0799 & 0.020 & -4.021 & 0.000 & -0.119 & -0.041 \\\\\n", "\\textbf{dist\\_64.0} & -0.0292 & 0.024 & -1.203 & 0.229 & -0.077 & 0.018 \\\\\n", "\\textbf{dist\\_65.0} & -0.0494 & 0.018 & -2.805 & 0.005 & -0.084 & -0.015 \\\\\n", "\\textbf{dist\\_66.0} & -0.0006 & 0.051 & -0.011 & 0.991 & -0.100 & 0.099 \\\\\n", "\\textbf{dist\\_67.0} & -0.0272 & 0.016 & -1.715 & 0.086 & -0.058 & 0.004 \\\\\n", "\\textbf{dist\\_68.0} & -0.0528 & 0.028 & -1.908 & 0.056 & -0.107 & 0.001 \\\\\n", "\\textbf{dist\\_69.0} & -0.0362 & 0.032 & -1.115 & 0.265 & -0.100 & 0.027 \\\\\n", "\\textbf{dist\\_70.0} & -0.0683 & 0.013 & -5.180 & 0.000 & -0.094 & -0.042 \\\\\n", "\\textbf{dist\\_71.0} & -0.0148 & 0.016 & -0.926 & 0.354 & -0.046 & 0.017 \\\\\n", "\\textbf{dist\\_72.0} & -0.0827 & 0.023 & -3.615 & 0.000 & -0.128 & -0.038 \\\\\n", "\\textbf{dist\\_73.0} & -0.0828 & 0.014 & -5.895 & 0.000 & -0.110 & -0.055 \\\\\n", "\\textbf{dist\\_74.0} & -0.0559 & 0.024 & -2.358 & 0.018 & -0.102 & -0.009 \\\\\n", "\\textbf{dist\\_75.0} & -0.0347 & 0.018 & -1.931 & 0.053 & -0.070 & 0.001 \\\\\n", "\\textbf{dist\\_76.0} & -0.0637 & 0.016 & -3.869 & 0.000 & -0.096 & -0.031 \\\\\n", "\\textbf{dist\\_77.0} & -0.0799 & 0.032 & -2.503 & 0.012 & -0.143 & -0.017 \\\\\n", "\\textbf{dist\\_78.0} & -0.0154 & 0.022 & -0.694 & 0.488 & -0.059 & 0.028 \\\\\n", "\\textbf{dist\\_79.0} & -0.0827 & 0.028 & -2.904 & 0.004 & -0.139 & -0.027 \\\\\n", "\\textbf{dist\\_80.0} & -0.0561 & 0.022 & -2.582 & 0.010 & -0.099 & -0.014 \\\\\n", "\\textbf{dist\\_81.0} & -0.0746 & 0.019 & -3.901 & 0.000 & -0.112 & -0.037 \\\\\n", "\\textbf{dist\\_82.0} & -0.0487 & 0.016 & -3.139 & 0.002 & -0.079 & -0.018 \\\\\n", "\\textbf{dist\\_83.0} & -0.0284 & 0.032 & -0.890 & 0.374 & -0.091 & 0.034 \\\\\n", "\\textbf{dist\\_84.0} & -0.0265 & 0.013 & -1.978 & 0.048 & -0.053 & -0.000 \\\\\n", "\\textbf{dist\\_85.0} & 0.0141 & 0.020 & 0.706 & 0.480 & -0.025 & 0.053 \\\\\n", "\\textbf{dist\\_86.0} & 0.0051 & 0.083 & 0.062 & 0.951 & -0.157 & 0.167 \\\\\n", "\\textbf{dist\\_87.0} & 0.0089 & 0.033 & 0.271 & 0.786 & -0.056 & 0.073 \\\\\n", "\\textbf{dist\\_88.0} & -0.0980 & 0.069 & -1.417 & 0.156 & -0.234 & 0.038 \\\\\n", "\\textbf{dist\\_89.0} & -0.1763 & 0.093 & -1.905 & 0.057 & -0.358 & 0.005 \\\\\n", "\\textbf{dist\\_90.0} & -0.1926 & 0.007 & -26.783 & 0.000 & -0.207 & -0.179 \\\\\n", "\\textbf{dist\\_91.0} & -0.3617 & 0.006 & -64.617 & 0.000 & -0.373 & -0.351 \\\\\n", "\\textbf{dist\\_93.0} & 0.0271 & 0.352 & 0.077 & 0.939 & -0.662 & 0.716 \\\\\n", "\\textbf{dist\\_94.0} & -0.0790 & 0.127 & -0.623 & 0.533 & -0.328 & 0.170 \\\\\n", "\\textbf{dist\\_95.0} & -0.1500 & 0.015 & -9.831 & 0.000 & -0.180 & -0.120 \\\\\n", "\\textbf{dist\\_96.0} & -0.0238 & 0.016 & -1.455 & 0.146 & -0.056 & 0.008 \\\\\n", "\\textbf{dist\\_97.0} & -0.0485 & 0.011 & -4.443 & 0.000 & -0.070 & -0.027 \\\\\n", "\\textbf{dist\\_98.0} & 0.0241 & 0.023 & 1.062 & 0.288 & -0.020 & 0.069 \\\\\n", "\\textbf{dist\\_99.0} & 0.0249 & 0.020 & 1.230 & 0.219 & -0.015 & 0.065 \\\\\n", "\\textbf{dist\\_100.0} & -0.0104 & 0.005 & -2.227 & 0.026 & -0.019 & -0.001 \\\\\n", "\\textbf{dist\\_101.0} & -0.1039 & 0.029 & -3.567 & 0.000 & -0.161 & -0.047 \\\\\n", "\\textbf{dist\\_102.0} & 0.0491 & 0.007 & 6.817 & 0.000 & 0.035 & 0.063 \\\\\n", "\\textbf{dist\\_103.0} & -0.1675 & 0.007 & -22.419 & 0.000 & -0.182 & -0.153 \\\\\n", "\\textbf{dist\\_104.0} & 0.0140 & 0.004 & 3.731 & 0.000 & 0.007 & 0.021 \\\\\n", "\\textbf{dist\\_106.0} & -0.0234 & 0.006 & -4.138 & 0.000 & -0.034 & -0.012 \\\\\n", "\\textbf{dist\\_107.0} & -0.1331 & 0.008 & -16.402 & 0.000 & -0.149 & -0.117 \\\\\n", "\\textbf{dist\\_108.0} & -0.1094 & 0.061 & -1.781 & 0.075 & -0.230 & 0.011 \\\\\n", "\\textbf{dist\\_109.0} & -0.0756 & 0.007 & -10.644 & 0.000 & -0.090 & -0.062 \\\\\n", "\\textbf{dist\\_110.0} & -0.3173 & 0.015 & -21.545 & 0.000 & -0.346 & -0.288 \\\\\n", "\\textbf{dist\\_111.0} & -0.3234 & 0.009 & -37.120 & 0.000 & -0.340 & -0.306 \\\\\n", "\\textbf{dist\\_114.0} & -0.0938 & 0.065 & -1.441 & 0.149 & -0.221 & 0.034 \\\\\n", "\\textbf{dist\\_115.0} & -0.0748 & 0.018 & -4.173 & 0.000 & -0.110 & -0.040 \\\\\n", "\\textbf{dist\\_116.0} & 0.0533 & 0.005 & 10.922 & 0.000 & 0.044 & 0.063 \\\\\n", "\\textbf{dist\\_117.0} & 0.0753 & 0.004 & 17.032 & 0.000 & 0.067 & 0.084 \\\\\n", "\\textbf{dist\\_118.0} & -0.0971 & 0.005 & -18.748 & 0.000 & -0.107 & -0.087 \\\\\n", "\\textbf{dist\\_119.0} & -0.0479 & 0.007 & -6.776 & 0.000 & -0.062 & -0.034 \\\\\n", "\\textbf{dist\\_121.0} & -0.1373 & 0.008 & -18.243 & 0.000 & -0.152 & -0.123 \\\\\n", "\\textbf{dist\\_122.0} & -0.0528 & 0.007 & -7.461 & 0.000 & -0.067 & -0.039 \\\\\n", "\\textbf{dist\\_123.0} & -0.0993 & 0.009 & -11.228 & 0.000 & -0.117 & -0.082 \\\\\n", "\\textbf{dist\\_nan} & 0.2368 & 0.093 & 2.546 & 0.011 & 0.055 & 0.419 \\\\\n", "\\textbf{dummy\\_1} & 0.8768 & 0.012 & 73.977 & 0.000 & 0.854 & 0.900 \\\\\n", "\\textbf{dummy\\_2} & 0.8739 & 0.020 & 44.474 & 0.000 & 0.835 & 0.912 \\\\\n", "\\textbf{dummy\\_3} & 0.8547 & 0.013 & 68.060 & 0.000 & 0.830 & 0.879 \\\\\n", "\\textbf{dummy\\_4} & 0.8528 & 0.013 & 66.303 & 0.000 & 0.828 & 0.878 \\\\\n", "\\textbf{dummy\\_5} & 0.8395 & 0.009 & 91.506 & 0.000 & 0.822 & 0.858 \\\\\n", "\\textbf{dummy\\_6} & 0.8389 & 0.012 & 68.270 & 0.000 & 0.815 & 0.863 \\\\\n", "\\textbf{dummy\\_7} & 0.8270 & 0.012 & 68.571 & 0.000 & 0.803 & 0.851 \\\\\n", "\\textbf{dummy\\_8} & 0.8193 & 0.006 & 127.483 & 0.000 & 0.807 & 0.832 \\\\\n", "\\textbf{dummy\\_9} & 0.8141 & 0.009 & 94.527 & 0.000 & 0.797 & 0.831 \\\\\n", "\\textbf{dummy\\_10} & 0.8186 & 0.009 & 95.389 & 0.000 & 0.802 & 0.835 \\\\\n", "\\textbf{dummy\\_11} & 0.8196 & 0.008 & 98.621 & 0.000 & 0.803 & 0.836 \\\\\n", "\\textbf{dummy\\_12} & 0.8093 & 0.011 & 74.134 & 0.000 & 0.788 & 0.831 \\\\\n", "\\textbf{dummy\\_13} & 0.7979 & 0.009 & 91.692 & 0.000 & 0.781 & 0.815 \\\\\n", "\\textbf{dummy\\_14} & 0.7920 & 0.006 & 136.831 & 0.000 & 0.781 & 0.803 \\\\\n", "\\textbf{dummy\\_15} & 0.8009 & 0.009 & 86.535 & 0.000 & 0.783 & 0.819 \\\\\n", "\\textbf{dummy\\_16} & 0.7855 & 0.008 & 95.460 & 0.000 & 0.769 & 0.802 \\\\\n", "\\textbf{dummy\\_17} & 0.7802 & 0.006 & 134.769 & 0.000 & 0.769 & 0.792 \\\\\n", "\\textbf{dummy\\_18} & 0.7793 & 0.005 & 159.336 & 0.000 & 0.770 & 0.789 \\\\\n", "\\textbf{dummy\\_19} & 0.7608 & 0.005 & 165.405 & 0.000 & 0.752 & 0.770 \\\\\n", "\\textbf{dummy\\_20} & 0.7666 & 0.008 & 90.479 & 0.000 & 0.750 & 0.783 \\\\\n", "\\textbf{dummy\\_21} & 0.7763 & 0.004 & 193.437 & 0.000 & 0.768 & 0.784 \\\\\n", "\\textbf{dummy\\_22} & 0.7685 & 0.009 & 83.916 & 0.000 & 0.751 & 0.786 \\\\\n", "\\textbf{dummy\\_23} & 0.7624 & 0.007 & 107.675 & 0.000 & 0.748 & 0.776 \\\\\n", "\\textbf{dummy\\_24} & 0.7524 & 0.009 & 88.352 & 0.000 & 0.736 & 0.769 \\\\\n", "\\textbf{dummy\\_25} & 0.7645 & 0.007 & 109.601 & 0.000 & 0.751 & 0.778 \\\\\n", "\\textbf{dummy\\_26} & 0.7619 & 0.008 & 101.205 & 0.000 & 0.747 & 0.777 \\\\\n", "\\textbf{dummy\\_27} & 0.7562 & 0.007 & 115.994 & 0.000 & 0.743 & 0.769 \\\\\n", "\\textbf{dummy\\_28} & 0.7550 & 0.012 & 64.369 & 0.000 & 0.732 & 0.778 \\\\\n", "\\textbf{dummy\\_29} & 0.7496 & 0.009 & 80.449 & 0.000 & 0.731 & 0.768 \\\\\n", "\\textbf{dummy\\_30} & 0.7489 & 0.009 & 80.411 & 0.000 & 0.731 & 0.767 \\\\\n", "\\textbf{dummy\\_31} & 0.7609 & 0.014 & 53.037 & 0.000 & 0.733 & 0.789 \\\\\n", "\\textbf{dummy\\_32} & 0.7553 & 0.010 & 77.474 & 0.000 & 0.736 & 0.774 \\\\\n", "\\textbf{dummy\\_33} & 0.7380 & 0.012 & 61.602 & 0.000 & 0.714 & 0.761 \\\\\n", "\\textbf{dummy\\_34} & 0.7390 & 0.014 & 53.602 & 0.000 & 0.712 & 0.766 \\\\\n", "\\textbf{dummy\\_35} & 0.7446 & 0.010 & 75.854 & 0.000 & 0.725 & 0.764 \\\\\n", "\\textbf{dummy\\_36} & 0.7504 & 0.015 & 48.912 & 0.000 & 0.720 & 0.780 \\\\\n", "\\textbf{dummy\\_37} & 0.7522 & 0.014 & 54.524 & 0.000 & 0.725 & 0.779 \\\\\n", "\\textbf{dummy\\_38} & 0.7519 & 0.008 & 92.959 & 0.000 & 0.736 & 0.768 \\\\\n", "\\textbf{dummy\\_39} & 0.7376 & 0.016 & 46.603 & 0.000 & 0.707 & 0.769 \\\\\n", "\\textbf{dummy\\_40} & 0.7522 & 0.013 & 56.892 & 0.000 & 0.726 & 0.778 \\\\\n", "\\textbf{dummy\\_41} & 0.7306 & 0.016 & 44.760 & 0.000 & 0.699 & 0.763 \\\\\n", "\\textbf{dummy\\_42} & 0.7442 & 0.016 & 46.127 & 0.000 & 0.713 & 0.776 \\\\\n", "\\textbf{dummy\\_43} & 0.7431 & 0.015 & 48.644 & 0.000 & 0.713 & 0.773 \\\\\n", "\\textbf{dummy\\_44} & 0.7448 & 0.012 & 64.457 & 0.000 & 0.722 & 0.767 \\\\\n", "\\textbf{dummy\\_45} & 0.7395 & 0.022 & 33.836 & 0.000 & 0.697 & 0.782 \\\\\n", "\\textbf{dummy\\_46} & 0.7380 & 0.020 & 36.567 & 0.000 & 0.698 & 0.778 \\\\\n", "\\textbf{dummy\\_47} & 0.7392 & 0.007 & 103.763 & 0.000 & 0.725 & 0.753 \\\\\n", "\\textbf{dummy\\_48} & 0.7392 & 0.006 & 118.057 & 0.000 & 0.727 & 0.751 \\\\\n", "\\textbf{dummy\\_49} & 0.7204 & 0.003 & 235.312 & 0.000 & 0.714 & 0.726 \\\\\n", "\\textbf{dummy\\_50} & 0.7360 & 0.014 & 51.464 & 0.000 & 0.708 & 0.764 \\\\\n", "\\textbf{dummy\\_51} & 0.7607 & 0.007 & 106.698 & 0.000 & 0.747 & 0.775 \\\\\n", "\\textbf{dummy\\_52} & 0.7470 & 0.018 & 41.705 & 0.000 & 0.712 & 0.782 \\\\\n", "\\textbf{dummy\\_53} & 0.7185 & 0.024 & 29.979 & 0.000 & 0.671 & 0.765 \\\\\n", "\\textbf{dummy\\_54} & 0.7162 & 0.026 & 27.416 & 0.000 & 0.665 & 0.767 \\\\\n", "\\textbf{dummy\\_55} & 0.7359 & 0.008 & 94.731 & 0.000 & 0.721 & 0.751 \\\\\n", "\\textbf{dummy\\_56} & 0.7562 & 0.013 & 57.553 & 0.000 & 0.730 & 0.782 \\\\\n", "\\textbf{dummy\\_57} & 0.7456 & 0.014 & 54.295 & 0.000 & 0.719 & 0.773 \\\\\n", "\\textbf{dummy\\_58} & 0.7367 & 0.019 & 38.880 & 0.000 & 0.700 & 0.774 \\\\\n", "\\textbf{dummy\\_59} & 0.7418 & 0.010 & 75.578 & 0.000 & 0.723 & 0.761 \\\\\n", "\\textbf{dummy\\_60} & 0.7061 & 0.013 & 53.249 & 0.000 & 0.680 & 0.732 \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\\begin{tabular}{lclc}\n", "\\textbf{Omnibus:} & 32414.573 & \\textbf{ Durbin-Watson: } & 1.884 \\\\\n", "\\textbf{Prob(Omnibus):} & 0.000 & \\textbf{ Jarque-Bera (JB): } & 51459.998 \\\\\n", "\\textbf{Skew:} & -1.240 & \\textbf{ Prob(JB): } & 0.00 \\\\\n", "\\textbf{Kurtosis:} & 2.972 & \\textbf{ Cond. No. } & 182. \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "%\\caption{OLS Regression Results}\n", "\\end{center}\n", "\n", "Notes: \\newline\n", " [1] Standard Errors are robust to cluster correlation (cluster)" ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: int_act R-squared: 0.072\n", "Model: OLS Adj. R-squared: 0.071\n", "Method: Least Squares F-statistic: nan\n", "Date: Thu, 25 May 2023 Prob (F-statistic): nan\n", "Time: 12:14:17 Log-Likelihood: -99100.\n", "No. Observations: 200916 AIC: 1.987e+05\n", "Df Residuals: 200668 BIC: 2.012e+05\n", "Df Model: 247 \n", "Covariance Type: cluster \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "surv_2 0.1311 0.009 14.673 0.000 0.114 0.149\n", "surv_3 0.1050 0.012 9.114 0.000 0.082 0.128\n", "surv_4 0.1484 0.007 22.369 0.000 0.135 0.161\n", "surv_5 0.1993 0.006 34.835 0.000 0.188 0.211\n", "surv_6 0.0018 0.006 0.297 0.766 -0.010 0.014\n", "surv_7 -0.0542 0.003 -18.761 0.000 -0.060 -0.049\n", "surv_8 -0.0879 0.004 -23.885 0.000 -0.095 -0.081\n", "surv_9 -0.0424 0.003 -12.378 0.000 -0.049 -0.036\n", "surv_10 -0.0261 0.002 -14.318 0.000 -0.030 -0.023\n", "surv_11 0.0186 0.001 17.571 0.000 0.017 0.021\n", "surv_12 -0.0234 0.010 -2.461 0.014 -0.042 -0.005\n", "surv_13 -0.0117 0.003 -3.555 0.000 -0.018 -0.005\n", "surv_14 -0.0210 0.002 -8.975 0.000 -0.026 -0.016\n", "surv_15 -0.0609 0.031 -1.994 0.046 -0.121 -0.001\n", "surv_16 0.0967 0.003 28.467 0.000 0.090 0.103\n", "surv_17 -0.0949 0.003 -36.232 0.000 -0.100 -0.090\n", "surv_18 -0.0192 0.003 -7.282 0.000 -0.024 -0.014\n", "surv_19 -0.0338 0.002 -14.979 0.000 -0.038 -0.029\n", "surv_20 -0.0119 0.002 -5.220 0.000 -0.016 -0.007\n", "surv_21 0.2008 0.004 55.723 0.000 0.194 0.208\n", "surv_22 0.2633 0.003 98.844 0.000 0.258 0.269\n", "surv_23 0.2211 0.005 43.026 0.000 0.211 0.231\n", "surv_24 -0.0978 0.003 -31.933 0.000 -0.104 -0.092\n", "surv_25 -0.0339 0.003 -11.092 0.000 -0.040 -0.028\n", "surv_26 -0.0677 0.002 -35.625 0.000 -0.071 -0.064\n", "surv_27 0.0089 0.002 4.661 0.000 0.005 0.013\n", "surv_28 -0.2647 0.093 -2.845 0.004 -0.447 -0.082\n", "surv_29 -0.2490 0.093 -2.676 0.007 -0.431 -0.067\n", "surv_30 0.2299 0.001 177.669 0.000 0.227 0.232\n", "surv_31 0.1351 0.002 66.140 0.000 0.131 0.139\n", "surv_32 0.1409 0.001 115.053 0.000 0.139 0.143\n", "surv_33 0.1773 0.001 166.216 0.000 0.175 0.179\n", "surv_34 0.2325 0.003 66.853 0.000 0.226 0.239\n", "surv_35 0.2477 0.003 86.169 0.000 0.242 0.253\n", "surv_36 0.2003 0.002 125.179 0.000 0.197 0.203\n", "surv_37 0.1971 0.002 114.849 0.000 0.194 0.200\n", "surv_38 0.0105 0.002 5.665 0.000 0.007 0.014\n", "surv_39 0.1289 0.004 30.273 0.000 0.121 0.137\n", "surv_40 0.0177 0.002 10.193 0.000 0.014 0.021\n", "surv_41 -0.0403 0.010 -4.097 0.000 -0.060 -0.021\n", "surv_42 -0.1246 0.003 -42.495 0.000 -0.130 -0.119\n", "surv_43 0.0563 0.009 6.124 0.000 0.038 0.074\n", "surv_44 0.1851 0.002 79.500 0.000 0.181 0.190\n", "surv_45 0.2186 0.002 144.724 0.000 0.216 0.222\n", "surv_46 0.1201 0.002 70.394 0.000 0.117 0.123\n", "surv_47 0.2410 0.004 67.671 0.000 0.234 0.248\n", "surv_48 0.1943 0.004 53.354 0.000 0.187 0.201\n", "surv_49 0.1328 0.004 33.429 0.000 0.125 0.141\n", "surv_50 0.2204 0.003 81.206 0.000 0.215 0.226\n", "surv_51 0.1825 0.006 31.031 0.000 0.171 0.194\n", "surv_52 0.1958 0.004 49.681 0.000 0.188 0.204\n", "surv_53 0.1969 0.006 33.731 0.000 0.185 0.208\n", "surv_54 -0.1226 0.006 -21.129 0.000 -0.134 -0.111\n", "surv_55 -0.1446 0.006 -24.102 0.000 -0.156 -0.133\n", "surv_56 0.0475 0.006 7.594 0.000 0.035 0.060\n", "surv_57 -0.0168 0.002 -7.466 0.000 -0.021 -0.012\n", "surv_58 0.2192 0.004 61.165 0.000 0.212 0.226\n", "surv_59 0.1690 0.004 38.620 0.000 0.160 0.178\n", "surv_60 0.1847 0.003 53.875 0.000 0.178 0.191\n", "surv_61 0.1604 0.003 46.577 0.000 0.154 0.167\n", "surv_62 -0.0823 0.003 -30.603 0.000 -0.088 -0.077\n", "surv_63 -0.0256 0.003 -9.385 0.000 -0.031 -0.020\n", "surv_64 0.0493 0.015 3.376 0.001 0.021 0.078\n", "surv_65 0.0542 0.015 3.689 0.000 0.025 0.083\n", "weekday_1 0.0025 0.004 0.596 0.551 -0.006 0.011\n", "weekday_2 0.0080 0.005 1.663 0.096 -0.001 0.017\n", "weekday_3 0.0074 0.004 2.082 0.037 0.000 0.014\n", "weekday_4 0.0083 0.005 1.780 0.075 -0.001 0.018\n", "weekday_5 0.0052 0.006 0.833 0.405 -0.007 0.018\n", "weekday_6 0.0101 0.005 2.072 0.038 0.001 0.020\n", "dist_2.0 -0.0024 0.003 -0.873 0.383 -0.008 0.003\n", "dist_3.0 -0.0051 0.001 -3.416 0.001 -0.008 -0.002\n", "dist_4.0 -0.0050 0.001 -4.585 0.000 -0.007 -0.003\n", "dist_5.0 -0.0043 0.005 -0.832 0.406 -0.014 0.006\n", "dist_6.0 -0.0116 0.006 -1.930 0.054 -0.023 0.000\n", "dist_7.0 -0.0195 0.006 -3.334 0.001 -0.031 -0.008\n", "dist_8.0 -0.0220 0.005 -4.088 0.000 -0.032 -0.011\n", "dist_9.0 -0.0068 0.005 -1.429 0.153 -0.016 0.003\n", "dist_10.0 -0.0092 0.011 -0.826 0.409 -0.031 0.013\n", "dist_11.0 -0.0194 0.008 -2.380 0.017 -0.035 -0.003\n", "dist_12.0 -0.0071 0.009 -0.810 0.418 -0.024 0.010\n", "dist_13.0 -0.0144 0.006 -2.251 0.024 -0.027 -0.002\n", "dist_14.0 -0.0200 0.008 -2.583 0.010 -0.035 -0.005\n", "dist_15.0 -0.0083 0.009 -0.883 0.377 -0.027 0.010\n", "dist_16.0 -0.0238 0.012 -1.992 0.046 -0.047 -0.000\n", "dist_17.0 -0.0176 0.014 -1.279 0.201 -0.045 0.009\n", "dist_18.0 -0.0292 0.019 -1.563 0.118 -0.066 0.007\n", "dist_19.0 -0.0120 0.015 -0.816 0.414 -0.041 0.017\n", "dist_20.0 -0.0260 0.007 -3.655 0.000 -0.040 -0.012\n", "dist_21.0 -0.0311 0.009 -3.521 0.000 -0.048 -0.014\n", "dist_22.0 -0.0249 0.012 -2.108 0.035 -0.048 -0.002\n", "dist_23.0 -0.0046 0.008 -0.562 0.574 -0.021 0.012\n", "dist_24.0 -0.0237 0.006 -3.876 0.000 -0.036 -0.012\n", "dist_25.0 -0.0123 0.009 -1.325 0.185 -0.030 0.006\n", "dist_26.0 -0.0198 0.013 -1.560 0.119 -0.045 0.005\n", "dist_27.0 -0.0162 0.010 -1.703 0.088 -0.035 0.002\n", "dist_28.0 -0.0140 0.009 -1.539 0.124 -0.032 0.004\n", "dist_29.0 -0.0247 0.013 -1.833 0.067 -0.051 0.002\n", "dist_30.0 -0.0302 0.013 -2.399 0.016 -0.055 -0.006\n", "dist_31.0 -0.0199 0.017 -1.153 0.249 -0.054 0.014\n", "dist_32.0 -0.0251 0.013 -1.906 0.057 -0.051 0.001\n", "dist_33.0 -0.0094 0.009 -1.048 0.295 -0.027 0.008\n", "dist_34.0 -0.0554 0.023 -2.416 0.016 -0.100 -0.010\n", "dist_35.0 -0.0379 0.019 -1.985 0.047 -0.075 -0.000\n", "dist_36.0 -0.0471 0.017 -2.854 0.004 -0.079 -0.015\n", "dist_37.0 -0.0276 0.024 -1.153 0.249 -0.074 0.019\n", "dist_38.0 -0.0425 0.013 -3.339 0.001 -0.067 -0.018\n", "dist_39.0 -0.0471 0.018 -2.643 0.008 -0.082 -0.012\n", "dist_40.0 -0.0255 0.024 -1.072 0.284 -0.072 0.021\n", "dist_41.0 -0.0346 0.014 -2.469 0.014 -0.062 -0.007\n", "dist_42.0 -0.0506 0.012 -4.279 0.000 -0.074 -0.027\n", "dist_43.0 -0.0426 0.017 -2.580 0.010 -0.075 -0.010\n", "dist_44.0 -0.0263 0.011 -2.338 0.019 -0.048 -0.004\n", "dist_45.0 -0.0199 0.030 -0.656 0.512 -0.079 0.039\n", "dist_46.0 -0.0389 0.015 -2.518 0.012 -0.069 -0.009\n", "dist_47.0 -0.0451 0.025 -1.822 0.068 -0.094 0.003\n", "dist_48.0 -0.0346 0.022 -1.550 0.121 -0.078 0.009\n", "dist_49.0 -0.0282 0.014 -2.061 0.039 -0.055 -0.001\n", "dist_50.0 -0.0163 0.008 -2.034 0.042 -0.032 -0.001\n", "dist_51.0 -0.0790 0.021 -3.707 0.000 -0.121 -0.037\n", "dist_52.0 -0.0265 0.010 -2.649 0.008 -0.046 -0.007\n", "dist_53.0 -0.0372 0.017 -2.183 0.029 -0.071 -0.004\n", "dist_54.0 -0.0215 0.015 -1.427 0.153 -0.051 0.008\n", "dist_55.0 -0.0613 0.054 -1.145 0.252 -0.166 0.044\n", "dist_56.0 -0.0376 0.008 -4.784 0.000 -0.053 -0.022\n", "dist_57.0 -0.0619 0.034 -1.799 0.072 -0.129 0.006\n", "dist_58.0 -0.0202 0.027 -0.754 0.451 -0.073 0.032\n", "dist_59.0 -0.0844 0.035 -2.407 0.016 -0.153 -0.016\n", "dist_60.0 0.0057 0.038 0.149 0.881 -0.069 0.080\n", "dist_61.0 0.0018 0.012 0.158 0.874 -0.021 0.025\n", "dist_62.0 -0.0517 0.037 -1.403 0.161 -0.124 0.021\n", "dist_63.0 -0.0799 0.020 -4.021 0.000 -0.119 -0.041\n", "dist_64.0 -0.0292 0.024 -1.203 0.229 -0.077 0.018\n", "dist_65.0 -0.0494 0.018 -2.805 0.005 -0.084 -0.015\n", "dist_66.0 -0.0006 0.051 -0.011 0.991 -0.100 0.099\n", "dist_67.0 -0.0272 0.016 -1.715 0.086 -0.058 0.004\n", "dist_68.0 -0.0528 0.028 -1.908 0.056 -0.107 0.001\n", "dist_69.0 -0.0362 0.032 -1.115 0.265 -0.100 0.027\n", "dist_70.0 -0.0683 0.013 -5.180 0.000 -0.094 -0.042\n", "dist_71.0 -0.0148 0.016 -0.926 0.354 -0.046 0.017\n", "dist_72.0 -0.0827 0.023 -3.615 0.000 -0.128 -0.038\n", "dist_73.0 -0.0828 0.014 -5.895 0.000 -0.110 -0.055\n", "dist_74.0 -0.0559 0.024 -2.358 0.018 -0.102 -0.009\n", "dist_75.0 -0.0347 0.018 -1.931 0.053 -0.070 0.001\n", "dist_76.0 -0.0637 0.016 -3.869 0.000 -0.096 -0.031\n", "dist_77.0 -0.0799 0.032 -2.503 0.012 -0.143 -0.017\n", "dist_78.0 -0.0154 0.022 -0.694 0.488 -0.059 0.028\n", "dist_79.0 -0.0827 0.028 -2.904 0.004 -0.139 -0.027\n", "dist_80.0 -0.0561 0.022 -2.582 0.010 -0.099 -0.014\n", "dist_81.0 -0.0746 0.019 -3.901 0.000 -0.112 -0.037\n", "dist_82.0 -0.0487 0.016 -3.139 0.002 -0.079 -0.018\n", "dist_83.0 -0.0284 0.032 -0.890 0.374 -0.091 0.034\n", "dist_84.0 -0.0265 0.013 -1.978 0.048 -0.053 -0.000\n", "dist_85.0 0.0141 0.020 0.706 0.480 -0.025 0.053\n", "dist_86.0 0.0051 0.083 0.062 0.951 -0.157 0.167\n", "dist_87.0 0.0089 0.033 0.271 0.786 -0.056 0.073\n", "dist_88.0 -0.0980 0.069 -1.417 0.156 -0.234 0.038\n", "dist_89.0 -0.1763 0.093 -1.905 0.057 -0.358 0.005\n", "dist_90.0 -0.1926 0.007 -26.783 0.000 -0.207 -0.179\n", "dist_91.0 -0.3617 0.006 -64.617 0.000 -0.373 -0.351\n", "dist_93.0 0.0271 0.352 0.077 0.939 -0.662 0.716\n", "dist_94.0 -0.0790 0.127 -0.623 0.533 -0.328 0.170\n", "dist_95.0 -0.1500 0.015 -9.831 0.000 -0.180 -0.120\n", "dist_96.0 -0.0238 0.016 -1.455 0.146 -0.056 0.008\n", "dist_97.0 -0.0485 0.011 -4.443 0.000 -0.070 -0.027\n", "dist_98.0 0.0241 0.023 1.062 0.288 -0.020 0.069\n", "dist_99.0 0.0249 0.020 1.230 0.219 -0.015 0.065\n", "dist_100.0 -0.0104 0.005 -2.227 0.026 -0.019 -0.001\n", "dist_101.0 -0.1039 0.029 -3.567 0.000 -0.161 -0.047\n", "dist_102.0 0.0491 0.007 6.817 0.000 0.035 0.063\n", "dist_103.0 -0.1675 0.007 -22.419 0.000 -0.182 -0.153\n", "dist_104.0 0.0140 0.004 3.731 0.000 0.007 0.021\n", "dist_106.0 -0.0234 0.006 -4.138 0.000 -0.034 -0.012\n", "dist_107.0 -0.1331 0.008 -16.402 0.000 -0.149 -0.117\n", "dist_108.0 -0.1094 0.061 -1.781 0.075 -0.230 0.011\n", "dist_109.0 -0.0756 0.007 -10.644 0.000 -0.090 -0.062\n", "dist_110.0 -0.3173 0.015 -21.545 0.000 -0.346 -0.288\n", "dist_111.0 -0.3234 0.009 -37.120 0.000 -0.340 -0.306\n", "dist_114.0 -0.0938 0.065 -1.441 0.149 -0.221 0.034\n", "dist_115.0 -0.0748 0.018 -4.173 0.000 -0.110 -0.040\n", "dist_116.0 0.0533 0.005 10.922 0.000 0.044 0.063\n", "dist_117.0 0.0753 0.004 17.032 0.000 0.067 0.084\n", "dist_118.0 -0.0971 0.005 -18.748 0.000 -0.107 -0.087\n", "dist_119.0 -0.0479 0.007 -6.776 0.000 -0.062 -0.034\n", "dist_121.0 -0.1373 0.008 -18.243 0.000 -0.152 -0.123\n", "dist_122.0 -0.0528 0.007 -7.461 0.000 -0.067 -0.039\n", "dist_123.0 -0.0993 0.009 -11.228 0.000 -0.117 -0.082\n", "dist_nan 0.2368 0.093 2.546 0.011 0.055 0.419\n", "dummy_1 0.8768 0.012 73.977 0.000 0.854 0.900\n", "dummy_2 0.8739 0.020 44.474 0.000 0.835 0.912\n", "dummy_3 0.8547 0.013 68.060 0.000 0.830 0.879\n", "dummy_4 0.8528 0.013 66.303 0.000 0.828 0.878\n", "dummy_5 0.8395 0.009 91.506 0.000 0.822 0.858\n", "dummy_6 0.8389 0.012 68.270 0.000 0.815 0.863\n", "dummy_7 0.8270 0.012 68.571 0.000 0.803 0.851\n", "dummy_8 0.8193 0.006 127.483 0.000 0.807 0.832\n", "dummy_9 0.8141 0.009 94.527 0.000 0.797 0.831\n", "dummy_10 0.8186 0.009 95.389 0.000 0.802 0.835\n", "dummy_11 0.8196 0.008 98.621 0.000 0.803 0.836\n", "dummy_12 0.8093 0.011 74.134 0.000 0.788 0.831\n", "dummy_13 0.7979 0.009 91.692 0.000 0.781 0.815\n", "dummy_14 0.7920 0.006 136.831 0.000 0.781 0.803\n", "dummy_15 0.8009 0.009 86.535 0.000 0.783 0.819\n", "dummy_16 0.7855 0.008 95.460 0.000 0.769 0.802\n", "dummy_17 0.7802 0.006 134.769 0.000 0.769 0.792\n", "dummy_18 0.7793 0.005 159.336 0.000 0.770 0.789\n", "dummy_19 0.7608 0.005 165.405 0.000 0.752 0.770\n", "dummy_20 0.7666 0.008 90.479 0.000 0.750 0.783\n", "dummy_21 0.7763 0.004 193.437 0.000 0.768 0.784\n", "dummy_22 0.7685 0.009 83.916 0.000 0.751 0.786\n", "dummy_23 0.7624 0.007 107.675 0.000 0.748 0.776\n", "dummy_24 0.7524 0.009 88.352 0.000 0.736 0.769\n", "dummy_25 0.7645 0.007 109.601 0.000 0.751 0.778\n", "dummy_26 0.7619 0.008 101.205 0.000 0.747 0.777\n", "dummy_27 0.7562 0.007 115.994 0.000 0.743 0.769\n", "dummy_28 0.7550 0.012 64.369 0.000 0.732 0.778\n", "dummy_29 0.7496 0.009 80.449 0.000 0.731 0.768\n", "dummy_30 0.7489 0.009 80.411 0.000 0.731 0.767\n", "dummy_31 0.7609 0.014 53.037 0.000 0.733 0.789\n", "dummy_32 0.7553 0.010 77.474 0.000 0.736 0.774\n", "dummy_33 0.7380 0.012 61.602 0.000 0.714 0.761\n", "dummy_34 0.7390 0.014 53.602 0.000 0.712 0.766\n", "dummy_35 0.7446 0.010 75.854 0.000 0.725 0.764\n", "dummy_36 0.7504 0.015 48.912 0.000 0.720 0.780\n", "dummy_37 0.7522 0.014 54.524 0.000 0.725 0.779\n", "dummy_38 0.7519 0.008 92.959 0.000 0.736 0.768\n", "dummy_39 0.7376 0.016 46.603 0.000 0.707 0.769\n", "dummy_40 0.7522 0.013 56.892 0.000 0.726 0.778\n", "dummy_41 0.7306 0.016 44.760 0.000 0.699 0.763\n", "dummy_42 0.7442 0.016 46.127 0.000 0.713 0.776\n", "dummy_43 0.7431 0.015 48.644 0.000 0.713 0.773\n", "dummy_44 0.7448 0.012 64.457 0.000 0.722 0.767\n", "dummy_45 0.7395 0.022 33.836 0.000 0.697 0.782\n", "dummy_46 0.7380 0.020 36.567 0.000 0.698 0.778\n", "dummy_47 0.7392 0.007 103.763 0.000 0.725 0.753\n", "dummy_48 0.7392 0.006 118.057 0.000 0.727 0.751\n", "dummy_49 0.7204 0.003 235.312 0.000 0.714 0.726\n", "dummy_50 0.7360 0.014 51.464 0.000 0.708 0.764\n", "dummy_51 0.7607 0.007 106.698 0.000 0.747 0.775\n", "dummy_52 0.7470 0.018 41.705 0.000 0.712 0.782\n", "dummy_53 0.7185 0.024 29.979 0.000 0.671 0.765\n", "dummy_54 0.7162 0.026 27.416 0.000 0.665 0.767\n", "dummy_55 0.7359 0.008 94.731 0.000 0.721 0.751\n", "dummy_56 0.7562 0.013 57.553 0.000 0.730 0.782\n", "dummy_57 0.7456 0.014 54.295 0.000 0.719 0.773\n", "dummy_58 0.7367 0.019 38.880 0.000 0.700 0.774\n", "dummy_59 0.7418 0.010 75.578 0.000 0.723 0.761\n", "dummy_60 0.7061 0.013 53.249 0.000 0.680 0.732\n", "==============================================================================\n", "Omnibus: 32414.573 Durbin-Watson: 1.884\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 51459.998\n", "Skew: -1.240 Prob(JB): 0.00\n", "Kurtosis: 2.972 Cond. No. 182.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors are robust to cluster correlation (cluster)\n", "\"\"\"" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = sm.OLS(y, X)\n", "results = model.fit(cov_type='cluster', cov_kwds={'groups': [df['survey'], df['date_elec']]})\n", "results.summary()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "consistency = pd.concat([results.params, results.conf_int()], axis=1)\n", "consistency = consistency[consistency.index.str.contains('dummy_')].reset_index(drop=True)\n", "consistency.columns = ['est', 'conf_int_low', 'conf_int_high']\n", "consistency['dist_pre'] = -(consistency.index + 1)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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estconf_int_lowconf_int_highdist_pre
00.8767930.8535630.900023-1
10.8738530.8353430.912364-2
20.8547070.8300940.879320-3
30.8527630.8275550.877972-4
40.8395380.8215560.857520-5
50.8389090.8148250.862993-6
60.8269570.8033200.850594-7
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" ], "text/plain": [ " est conf_int_low conf_int_high dist_pre\n", "0 0.876793 0.853563 0.900023 -1\n", "1 0.873853 0.835343 0.912364 -2\n", "2 0.854707 0.830094 0.879320 -3\n", "3 0.852763 0.827555 0.877972 -4\n", "4 0.839538 0.821556 0.857520 -5\n", "5 0.838909 0.814825 0.862993 -6\n", "6 0.826957 0.803320 0.850594 -7\n", "7 0.819330 0.806733 0.831926 -8\n", "8 0.814097 0.797217 0.830977 -9\n", "9 0.818631 0.801811 0.835452 -10" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "consistency[0:10]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "consistency.to_csv('consistency.csv', index=False) " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Figure 1 over time\n", "\n", "Construct Figure 1 per decade." ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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surveyid_survdate_elecdecadedist_predist_pre_weekweekday_predist_posint_act
1AUTNES12013-09-292010314436.01
2AUTNES12013-09-292010415116.00
3AUTNES12013-09-292010162520.00
5AUTNES12013-09-292010233535.00
6AUTNES12013-09-292010334217.01
..............................
334785SNES652014-09-14201012121.01
334786SNES652014-09-14201010141.01
334787SNES652014-09-14201011131.01
334788SNES652014-09-14201012121.00
334789SNES652014-09-14201012121.01
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200916 rows × 9 columns

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" ], "text/plain": [ " survey id_surv date_elec decade dist_pre dist_pre_week \n", "1 AUTNES 1 2013-09-29 2010 31 4 \\\n", "2 AUTNES 1 2013-09-29 2010 41 5 \n", "3 AUTNES 1 2013-09-29 2010 16 2 \n", "5 AUTNES 1 2013-09-29 2010 23 3 \n", "6 AUTNES 1 2013-09-29 2010 33 4 \n", "... ... ... ... ... ... ... \n", "334785 SNES 65 2014-09-14 2010 12 1 \n", "334786 SNES 65 2014-09-14 2010 10 1 \n", "334787 SNES 65 2014-09-14 2010 11 1 \n", "334788 SNES 65 2014-09-14 2010 12 1 \n", "334789 SNES 65 2014-09-14 2010 12 1 \n", "\n", " weekday_pre dist_pos int_act \n", "1 4 36.0 1 \n", "2 1 16.0 0 \n", "3 5 20.0 0 \n", "5 5 35.0 0 \n", "6 2 17.0 1 \n", "... ... ... ... \n", "334785 2 1.0 1 \n", "334786 4 1.0 1 \n", "334787 3 1.0 1 \n", "334788 2 1.0 0 \n", "334789 2 1.0 1 \n", "\n", "[200916 rows x 9 columns]" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_stata('../../datasets/voters/raw/Data/Analysis/analysis_indiv.dta')\n", "df.dropna(subset=['int_act'], inplace=True)\n", "df = df[(df['dist_pre'] <= 60) & (df['dist_pre'] > 0)]\n", "\n", "df['id_surv'] = df['id_surv'].astype(int)\n", "df['dist_pre'] = df['dist_pre'].astype(int)\n", "df['weekday_pre'] = df['weekday_pre'].astype(int)\n", "df['int_act'] = df['int_act'].astype(int)\n", "df['decade'] = df['date_elec'].dt.year // 10 * 10\n", "df['dist_pre_week'] = df['dist_pre'] // 7\n", "\n", "df = df[['survey', 'id_surv', 'date_elec', 'decade', 'dist_pre', 'dist_pre_week', 'weekday_pre', 'dist_pos', 'int_act']]\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dist_pre_week\n", "1 48916\n", "0 28678\n", "2 27412\n", "3 26162\n", "4 23076\n", "5 18600\n", "6 13465\n", "7 9263\n", "8 5344\n", "Name: count, dtype: int64" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['dist_pre_week'].value_counts()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Helper function" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [], "source": [ "def get_params(df_all, decade):\n", " \n", " df = df_all[df_all['decade'] == decade].copy()\n", "\n", " df['survey'], _ = pd.factorize(df['survey'])\n", " df['date_elec'], _ = pd.factorize(df['date_elec'])\n", " \n", " sample_ref = df['dist_pre_week'] == 1\n", "\n", " dummy = pd.get_dummies(df['dist_pre_week'], prefix='dummy', dtype=int)\n", "\n", " surv_dummy = pd.get_dummies(df['id_surv'], prefix='surv', dtype=int)\n", " surv_mean = surv_dummy.loc[sample_ref, :].mean()\n", " calsurv_dummy = surv_dummy.sub(surv_mean)\n", " calsurv_dummy = calsurv_dummy.iloc[:, 1:]\n", "\n", " weekday_dummy = pd.get_dummies(df['weekday_pre'], prefix='weekday', dtype=int)\n", " weekday_mean = weekday_dummy.loc[sample_ref, :].mean()\n", " calweekday_dummy = weekday_dummy.sub(weekday_mean)\n", " calweekday_dummy = calweekday_dummy.iloc[:, 1:]\n", "\n", " distpos_dummy = pd.get_dummies(df['dist_pos'], prefix='dist', dummy_na=True, dtype=int)\n", " distpos_mean = distpos_dummy.loc[sample_ref, :].mean()\n", " caldistpos_dummy = distpos_dummy.sub(distpos_mean)\n", " caldistpos_dummy = caldistpos_dummy.iloc[:, 1:]\n", "\n", " #X = pd.concat([calsurv_dummy, calweekday_dummy, caldistpos_dummy, dummy], axis=1)\n", " X = pd.concat([calsurv_dummy, caldistpos_dummy, dummy], axis=1)\n", " y = df['int_act']\n", "\n", " model = sm.OLS(y, X)\n", " results = model.fit(cov_type='cluster', cov_kwds={'groups': df['date_elec']})\n", "\n", " params = pd.concat([results.params, results.conf_int()], axis=1)\n", " params = params[params.index.str.contains('dummy_')].reset_index(drop=True)\n", " params.columns = ['est', 'conf_int_low', 'conf_int_high']\n", " params['dist_pre_week'] = -params.index\n", " params['decade'] = decade\n", "\n", " params = params[['decade', 'dist_pre_week', 'est', 'conf_int_low', 'conf_int_high']]\n", " \n", " return params" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [], "source": [ "decades = list(range(1950, 2020, 10))\n", "\n", "params = pd.DataFrame(columns=['decade', 'dist_pre_week', 'est', 'conf_int_low', 'conf_int_high'])\n", "\n", "for decade in decades:\n", " params_d = get_params(df, decade)\n", " params = pd.concat([params, params_d], axis=0)\n" ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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decadedist_pre_weekestconf_int_lowconf_int_high
0195000.8099650.7403260.879603
11950-10.7722420.7599820.784502
21950-20.7891330.7800150.798252
31950-30.7350690.7259300.744208
41950-40.7624950.7504820.774507
..................
42010-40.7402990.7154500.765149
52010-50.7429770.7159650.769990
62010-60.7283030.6901940.766412
72010-70.7121370.6793950.744880
82010-80.7197200.6888980.750542
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62 rows × 5 columns

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" ], "text/plain": [ " decade dist_pre_week est conf_int_low conf_int_high\n", "0 1950 0 0.809965 0.740326 0.879603\n", "1 1950 -1 0.772242 0.759982 0.784502\n", "2 1950 -2 0.789133 0.780015 0.798252\n", "3 1950 -3 0.735069 0.725930 0.744208\n", "4 1950 -4 0.762495 0.750482 0.774507\n", ".. ... ... ... ... ...\n", "4 2010 -4 0.740299 0.715450 0.765149\n", "5 2010 -5 0.742977 0.715965 0.769990\n", "6 2010 -6 0.728303 0.690194 0.766412\n", "7 2010 -7 0.712137 0.679395 0.744880\n", "8 2010 -8 0.719720 0.688898 0.750542\n", "\n", "[62 rows x 5 columns]" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [], "source": [ "params.to_csv('overtime.csv', index=False)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Debates" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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month_preday_premonth_posday_posageid_respday_elecmonth_elecyear_elecyear_pre...dummy_pos107dummy_pos108dummy_pos109dummy_pos110dummy_pos111dummy_pos112dummy_pos113dummy_pos114dummy_pos115dummy_pos116
09.04.0NaNNaN48.0206.029.09.02013.02013.0...0.00.00.00.00.00.00.00.00.00.0
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399507 rows × 359 columns

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11Austria2013-08-2900.01.00.00.00.00.00.0...0.00.00.00.00.00.00.00.00.01.0
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399492Germany2017-09-0370.00.00.00.00.00.01.0...0.00.01.00.00.00.00.00.00.00.0
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" ], "text/plain": [ " country date_debate id_date int_act weekday_dummy1 weekday_dummy2 \n", "1 Austria 2013-08-29 0 0.0 0.0 0.0 \\\n", "3 Austria 2013-08-29 0 0.0 0.0 1.0 \n", "4 Austria 2013-08-29 0 0.0 0.0 0.0 \n", "7 Austria 2013-08-29 0 1.0 0.0 0.0 \n", "11 Austria 2013-08-29 0 0.0 1.0 0.0 \n", "... ... ... ... ... ... ... \n", "399492 Germany 2017-09-03 7 0.0 0.0 0.0 \n", "399493 Germany 2017-09-03 7 1.0 1.0 0.0 \n", "399495 Germany 2017-09-03 7 0.0 0.0 1.0 \n", "399502 Germany 2017-09-03 7 0.0 0.0 0.0 \n", "399503 Germany 2017-09-03 7 0.0 0.0 0.0 \n", "\n", " weekday_dummy3 weekday_dummy4 weekday_dummy5 weekday_dummy6 ... \n", "1 0.0 0.0 1.0 0.0 ... \\\n", "3 0.0 0.0 0.0 0.0 ... \n", "4 0.0 0.0 1.0 0.0 ... \n", "7 0.0 1.0 0.0 0.0 ... \n", "11 0.0 0.0 0.0 0.0 ... \n", "... ... ... ... ... ... \n", "399492 0.0 0.0 0.0 1.0 ... \n", "399493 0.0 0.0 0.0 0.0 ... \n", "399495 0.0 0.0 0.0 0.0 ... \n", "399502 0.0 0.0 1.0 0.0 ... \n", "399503 0.0 1.0 0.0 0.0 ... \n", "\n", " dummy_pos115 dummy_pos116 dummya dummyl3 dummyl2 dummyl1 \n", "1 0.0 0.0 0.0 0.0 0.0 0.0 \\\n", "3 0.0 0.0 1.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 1.0 0.0 0.0 0.0 \n", "11 0.0 0.0 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... ... \n", "399492 0.0 0.0 1.0 0.0 0.0 0.0 \n", "399493 0.0 0.0 0.0 0.0 0.0 0.0 \n", "399495 0.0 0.0 1.0 0.0 0.0 0.0 \n", "399502 0.0 0.0 0.0 0.0 1.0 0.0 \n", "399503 0.0 0.0 1.0 0.0 0.0 0.0 \n", "\n", " dummyu1 dummyu2 dummyu3 dummyb \n", "1 1.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 1.0 \n", "7 0.0 0.0 0.0 0.0 \n", "11 0.0 0.0 0.0 1.0 \n", "... ... ... ... ... \n", "399492 0.0 0.0 0.0 0.0 \n", "399493 1.0 0.0 0.0 0.0 \n", "399495 0.0 0.0 0.0 0.0 \n", "399502 0.0 0.0 0.0 0.0 \n", "399503 0.0 0.0 0.0 0.0 \n", "\n", "[263681 rows x 134 columns]" ] }, "execution_count": 200, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = analysis_debate_indiv.copy()\n", "df['id_date'] = df.groupby(['country', 'date_debate']).ngroup()\n", "df.dropna(subset=['int_act'], inplace=True)\n", "\n", "X = df[X_cols]\n", "y = df['int_act']\n", "df = df[all_cols]\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['int_act'].values" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = sm.OLS(y, X)\n", "results = model.fit(cov_type='cluster', cov_kwds={'groups': [df['id_date'], df['date_elec']]})\n", "results.summary()" ] } ], "metadata": { "kernelspec": { "display_name": "twopoints-venv", "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": "3.10.9" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }