{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python: Real-Data Example for Multi-Period Difference-in-Differences\n", "\n", "In this example, we replicate a [real-data demo notebook](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data) from the [did-R-package](https://bcallaway11.github.io/did/index.html) in order to illustrate the use of `DoubleML` for multi-period difference-in-differences (DiD) models. \n", "\n", "\n", "\n", "The notebook requires the following packages:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pyreadr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn.linear_model import LinearRegression, LogisticRegression\n", "from sklearn.dummy import DummyRegressor, DummyClassifier\n", "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n", "\n", "from doubleml.data import DoubleMLPanelData\n", "from doubleml.did import DoubleMLDIDMulti" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Causal Research Question\n", "\n", "[Callaway and Sant'Anna (2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) study the causal effect of raising the minimum wage on teen employment in the US using county data over a period from 2001 to 2007. A county is defined as treated if the minimum wage in that county is above the federal minimum wage. We focus on a preprocessed balanced panel data set as provided by the [did-R-package](https://bcallaway11.github.io/did/index.html). The corresponding documentation for the `mpdta` data is available from the [did package website](https://bcallaway11.github.io/did/reference/mpdta.html). We use this data solely as a demonstration example to help readers understand differences in the `DoubleML` and `did` packages. An analogous notebook using the same data is available from the [did documentation](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data).\n", "\n", "We follow the original notebook and provide results under identification based on unconditional and conditional parallel trends. For the Double Machine Learning (DML) Difference-in-Differences estimator, we demonstrate two different specifications, one based on linear and logistic regression and one based on their $\\ell_1$ penalized variants Lasso and logistic regression with cross-validated penalty choice. The results for the former are expected to be very similar to those in the [did data example](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data). Minor differences might arise due to the use of sample-splitting in the DML estimation.\n", "\n", "\n", "## Data\n", "\n", "We will download and read a preprocessed data file as provided by the [did-R-package](https://bcallaway11.github.io/did/index.html).\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "year", "rawType": "int32", "type": "integer" }, { "name": "countyreal", "rawType": "float64", "type": "float" }, { "name": "lpop", "rawType": "float64", "type": "float" }, { "name": "lemp", "rawType": "float64", "type": "float" }, { "name": "first.treat", "rawType": "float64", "type": "float" }, { "name": "treat", "rawType": "float64", "type": "float" } ], "conversionMethod": "pd.DataFrame", "ref": "5e2a60ba-8445-46e1-b56a-ecf010c51d7d", "rows": [ [ "0", "2003", "8001.0", "5.896760933305299", "8.461469042643875", "2007.0", "1.0" ], [ "1", "2004", "8001.0", "5.896760933305299", "8.336869637284956", "2007.0", "1.0" ], [ "2", "2005", "8001.0", "5.896760933305299", "8.340217320947035", "2007.0", "1.0" ], [ "3", "2006", "8001.0", "5.896760933305299", "8.37816098272068", "2007.0", "1.0" ], [ "4", "2007", "8001.0", "5.896760933305299", "8.487352349405215", "2007.0", "1.0" ] ], "shape": { "columns": 6, "rows": 5 } }, "text/html": [ "
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" ], "text/plain": [ " year countyreal lpop lemp first.treat treat\n", "0 2003 8001.0 5.896761 8.461469 2007.0 1.0\n", "1 2004 8001.0 5.896761 8.336870 2007.0 1.0\n", "2 2005 8001.0 5.896761 8.340217 2007.0 1.0\n", "3 2006 8001.0 5.896761 8.378161 2007.0 1.0\n", "4 2007 8001.0 5.896761 8.487352 2007.0 1.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# download file from did package for R\n", "url = \"https://github.com/bcallaway11/did/raw/refs/heads/master/data/mpdta.rda\"\n", "pyreadr.download_file(url, \"mpdta.rda\")\n", "\n", "mpdta = pyreadr.read_r(\"mpdta.rda\")[\"mpdta\"]\n", "mpdta.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To work with [DoubleML](https://docs.doubleml.org/stable/index.html), we initialize a `DoubleMLPanelData` object. The input data has to satisfy some requirements, i.e., it should be in a *long* format with every row containing the information of one unit at one time period. Moreover, the data should contain a column on the unit identifier and a column on the time period. The requirements are virtually identical to those of the [did-R-package](https://bcallaway11.github.io/did/index.html), as listed in [their data example](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data). In line with the naming conventions of [DoubleML](https://docs.doubleml.org/stable/index.html), the treatment group indicator is passed to `DoubleMLPanelData` by the `d_cols` argument. To flexibly handle different formats for handling time periods, the time variable `t_col` can handle `float`, `int` and `datetime` formats. More information are available in the [user guide](https://docs.doubleml.org/dev/guide/data_backend.html#doublemlpaneldata). To indicate never treated units, we set their value for the treatment group variable to `np.inf`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can initialize the ``DoubleMLPanelData`` object, specifying\n", "\n", " - `y_col` : the outcome\n", " - `d_cols`: the group variable indicating the first treated period for each unit\n", " - `id_col`: the unique identification column for each unit\n", " - `t_col` : the time column\n", " - `x_cols`: the additional pre-treatment controls\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLPanelData Object ==================\n", "\n", "------------------ Data summary ------------------\n", "Outcome variable: lemp\n", "Treatment variable(s): ['first.treat']\n", "Covariates: ['lpop']\n", "Instrument variable(s): None\n", "Time variable: year\n", "Id variable: countyreal\n", "No. Observations: 500\n", "\n", "------------------ DataFrame info ------------------\n", "\n", "RangeIndex: 2500 entries, 0 to 2499\n", "Columns: 6 entries, year to treat\n", "dtypes: float64(5), int32(1)\n", "memory usage: 107.6 KB\n", "\n" ] } ], "source": [ "# Set values for treatment group indicator for never-treated to np.inf\n", "mpdta.loc[mpdta['first.treat'] == 0, 'first.treat'] = np.inf\n", "\n", "dml_data = DoubleMLPanelData(\n", " data=mpdta,\n", " y_col=\"lemp\",\n", " d_cols=\"first.treat\",\n", " id_col=\"countyreal\",\n", " t_col=\"year\",\n", " x_cols=['lpop']\n", ")\n", "print(dml_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we specified a pre-treatment confounding variable `lpop` through the `x_cols` argument. To consider cases under unconditional parallel trends, we can use dummy learners to ignore the pre-treatment confounding variable. This is illustrated below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ATT Estimation: Unconditional Parallel Trends\n", "\n", "We start with identification under the unconditional parallel trends assumption. To do so, initialize a `DoubleMLDIDMulti` object (see [model documentation](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did)), which takes the previously initialized `DoubleMLPanelData` object as input. We use scikit-learn's `DummyRegressor` (documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html)) and `DummyClassifier` (documentation [here](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html)) to ignore the pre-treatment confounding variable. At this stage, we can also pass further options, for example specifying the number of folds and repetitions used for cross-fitting. \n", "\n", "When calling the `fit()` method, the model estimates standard combinations of $ATT(g,t)$ parameters, which corresponds to the defaults in the [did-R-package](https://bcallaway11.github.io/did/index.html). These combinations can also be customized through the `gt_combinations` argument, see [the user guide](https://docs.doubleml.org/stable/guide/models.html#panel-data)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " coef std err t P>|t| 2.5 % 97.5 %\n", "ATT(2004.0,2003,2004) -0.0105 0.0232 -0.4526 0.6508 -0.0560 0.0350\n", "ATT(2004.0,2003,2005) -0.0704 0.0310 -2.2700 0.0232 -0.1312 -0.0096\n", "ATT(2004.0,2003,2006) -0.1372 0.0363 -3.7763 0.0002 -0.2085 -0.0660\n", "ATT(2004.0,2003,2007) -0.1008 0.0343 -2.9381 0.0033 -0.1680 -0.0335\n", "ATT(2006.0,2003,2004) 0.0065 0.0232 0.2811 0.7786 -0.0390 0.0520\n", "ATT(2006.0,2004,2005) -0.0027 0.0195 -0.1400 0.8886 -0.0410 0.0355\n", "ATT(2006.0,2005,2006) -0.0046 0.0179 -0.2585 0.7960 -0.0397 0.0304\n", "ATT(2006.0,2005,2007) -0.0412 0.0202 -2.0404 0.0413 -0.0807 -0.0016\n", "ATT(2007.0,2003,2004) 0.0304 0.0151 2.0197 0.0434 0.0009 0.0600\n", "ATT(2007.0,2004,2005) -0.0027 0.0165 -0.1645 0.8693 -0.0350 0.0296\n", "ATT(2007.0,2005,2006) -0.0310 0.0179 -1.7348 0.0828 -0.0660 0.0040\n", "ATT(2007.0,2006,2007) -0.0260 0.0167 -1.5628 0.1181 -0.0587 0.0066\n" ] } ], "source": [ "dml_obj = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", " ml_g=DummyRegressor(),\n", " ml_m=DummyClassifier(),\n", " control_group=\"never_treated\",\n", " n_folds=10\n", ")\n", "\n", "dml_obj.fit()\n", "print(dml_obj.summary.round(4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The summary displays estimates of the $ATT(g,t_\\text{eval})$ effects for different combinations of $(g,t_\\text{eval})$ via $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$, where\n", " - $\\mathrm{g}$ specifies the group\n", " - $t_\\text{pre}$ specifies the corresponding pre-treatment period\n", " - $t_\\text{eval}$ specifies the evaluation period\n", "\n", "This corresponds to the estimates given in `att_gt` function in the [did-R-package](https://bcallaway11.github.io/did/index.html), where the standard choice is $t_\\text{pre} = \\min(\\mathrm{g}, t_\\text{eval}) - 1$ (without anticipation).\n", "\n", "Remark that this includes pre-tests effects if $\\mathrm{g} > t_{eval}$, e.g. $ATT(2007,2005)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As usual for the DoubleML-package, you can obtain joint confidence intervals via bootstrap." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.5 % 97.5 %\n", "ATT(2004.0,2003,2004) -0.075498 0.054493\n", "ATT(2004.0,2003,2005) -0.157304 0.016469\n", "ATT(2004.0,2003,2006) -0.239029 -0.035446\n", "ATT(2004.0,2003,2007) -0.196831 -0.004706\n", "ATT(2006.0,2003,2004) -0.058475 0.071524\n", "ATT(2006.0,2004,2005) -0.057417 0.051949\n", "ATT(2006.0,2005,2006) -0.054715 0.045468\n", "ATT(2006.0,2005,2007) -0.097724 0.015351\n", "ATT(2007.0,2003,2004) -0.011777 0.072669\n", "ATT(2007.0,2004,2005) -0.048822 0.043404\n", "ATT(2007.0,2005,2006) -0.081068 0.019054\n", "ATT(2007.0,2006,2007) -0.072675 0.020621\n" ] } ], "source": [ "level = 0.95\n", "\n", "ci = dml_obj.confint(level=level)\n", "dml_obj.bootstrap(n_rep_boot=5000)\n", "ci_joint = dml_obj.confint(level=level, joint=True)\n", "print(ci_joint)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A visualization of the effects can be obtained via the `plot_effects()` method.\n", "\n", "Remark that the plot used joint confidence intervals per default. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [ "nbsphinx-thumbnail" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " return math.isfinite(val)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = dml_obj.plot_effects()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Effect Aggregation\n", "\n", "As the [did-R-package](https://bcallaway11.github.io/did/index.html), the $ATT$'s can be aggregated to summarize multiple effects.\n", "For details on different aggregations and details on their interpretations see [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001).\n", "\n", "The aggregations are implemented via the `aggregate()` method. We follow the structure of the [did package notebook](https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data) and start with an aggregation relative to the treatment timing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Event Study Aggregation\n", "\n", "\n", "We can aggregate the $ATT$s relative to the treatment timing. This is done by setting `aggregation=\"eventstudy\"` in the `aggregate()` method. \n", " `aggregation=\"eventstudy\"` aggregates $\\widehat{ATT}(\\mathrm{g},t_\\text{pre},t_\\text{eval})$ based on exposure time $e = t_\\text{eval} - \\mathrm{g}$ (respecting group size)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLDIDAggregation Object ==================\n", " Event Study Aggregation \n", "\n", "------------------ Overall Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", "-0.077214 0.019951 -3.870174 0.000109 -0.116317 -0.038111\n", "------------------ Aggregated Effects ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", "-3.0 0.030446 0.015075 2.019662 0.043418 0.000900 0.059992\n", "-2.0 -0.000549 0.013317 -0.041223 0.967118 -0.026650 0.025552\n", "-1.0 -0.024393 0.014200 -1.717808 0.085832 -0.052226 0.003439\n", "0.0 -0.019919 0.011816 -1.685694 0.091855 -0.043079 0.003241\n", "1.0 -0.050930 0.016783 -3.034679 0.002408 -0.083824 -0.018037\n", "2.0 -0.137238 0.036342 -3.776254 0.000159 -0.208467 -0.066008\n", "3.0 -0.100768 0.034297 -2.938126 0.003302 -0.167989 -0.033548\n", "------------------ Additional Information ------------------\n", "Score function: observational\n", "Control group: never_treated\n", "Anticipation periods: 0\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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vn9Uuu+wSSZLETjvtFJ/73OdqrHvrrbfiP//5T9x7771x1llnZcer+9S2ulxGtqW9RkR069Ztq81Sqq9eN1ddj5NddtklMplM/Otf/6p0+emn1baNrl27xtlnnx1nn312rFy5Mg499NC49tprhVIANHnuKQUAzdgnn3wSDz/8cBxzzDFx8sknV/m6+OKLY8WKFfHYY49FRMTQoUOjoqIi/ud//ie7jUwmUyXQ6tGjR+yxxx7x61//OlauXJkdf+655+Ktt96qU2/dunWLww47LH7+85/H/PnzqyxftGhR9vHQoUNj+vTp8cYbb2THli5dGpMmTaq0zoaZNhtm6kRErFmzJn72s59V2X5BQUG1l7CdcsopMXfu3Erfgw0++eSTKCsri4iIo446KiIibrvttko1P/nJT6qstzWdeOKJ0apVq7juuusq7WfE+v1esmRJRFT/vUiSJH76059W2WZBQUFErJ9ptjUNHTo0CgsL46abboqKiooqyzf+N66rDWHp1u51c9X1OBkxYkTk5ubG9ddfX2V22sb/NgUFBdXu04Z/zw06dOgQffr0ifLy8q2wFwDQsMyUAoBm7LHHHosVK1bEcccdV+3yAw88MIqKimLSpElx6qmnxogRI+KAAw6Ib3zjG/Huu+9G375947HHHoulS5dGROXZHDfddFMcf/zxcfDBB8fZZ58dH3/8cdxxxx2xxx57VAqqajNhwoQ45JBDYs8994zRo0fHzjvvHAsWLIjp06fHRx99FG+++WZERHzrW9+K3/zmN/GlL30pLrnkkigoKIhf/OIX0atXr1i6dGm2r4MOOii6dOkSI0eOjEsvvTRycnLivvvuqxLeRET0798/HnjggRgzZkx84QtfiA4dOsSxxx4bZ555Zvzud7+LCy64IJ555pk4+OCDY926dfHvf/87fve738Wf//zn2H///WOfffaJ008/PX72s5/F8uXL46CDDoqpU6fWeabYltpll13ie9/7XowdOzbef//9GDFiRHTs2DFmz54djzzySJx33nlxxRVXRN++fWOXXXaJK664IubOnRuFhYXx+9//vtr7M/Xv3z8i1t+0fejQodGqVas47bTTPnOvhYWFceedd8aZZ54Z++23X5x22mlRVFQUH3zwQTzxxBNx8MEHxx133LFZ22zXrl3069cvHnjggfjc5z4XXbt2jT322CP22GOPWtf761//GqtXr64yvtdee23R/Znqepz06dMnvv3tb8cNN9wQX/ziF+PEE0+MvLy8eOWVV6JHjx4xfvz4iFj/b3DnnXfG9773vejTp09069YtjjjiiOjXr18cdthh0b9//+jatWu8+uqr8dBDD8XFF1+82T0DQKPTMB/6BwCk4dhjj03y8/OTsrKyGmtGjRqVtGnTJlm8eHGSJEmyaNGi5Ctf+UrSsWPHpFOnTsmoUaOSF154IYmIZPLkyZXWnTx5ctK3b98kLy8v2WOPPZLHHnssOemkk5K+fftma2bPnp1ERPLDH/6w2td/7733krPOOispLi5O2rRpk2y//fbJMccckzz00EOV6mbMmJF88YtfTPLy8pIddtghGT9+fHLbbbclEZGUlJRk61544YXkwAMPTNq1a5f06NEj+da3vpX8+c9/TiIieeaZZ7J1K1euTL7yla8knTt3TiIi6d27d3bZmjVrkptvvjnZfffdk7y8vKRLly5J//79k+uuuy5Zvnx5tu6TTz5JLr300mSbbbZJCgoKkmOPPTb58MMPk4hIxo0bV+P3/NMefPDBKv2NGzcuiYhk0aJF1a7z+9//PjnkkEOSgoKCpKCgIOnbt29y0UUXJe+880625l//+lcyZMiQpEOHDsm2226bjB49OnnzzTeTiEjuueeebN3atWuTSy65JCkqKkpycnKSDb8i1vRv98wzzyQRkTz44IOVxu+5554kIpJXXnmlSv3QoUOTTp06Jfn5+ckuu+ySjBo1Knn11VezNSNHjkwKCgqq7OeG78PGXnzxxaR///5J27ZtN/m93tBrTV8brzto0KBk9913r7KN3r17J8OHD68yXtfjJEmS5Je//GWy7777ZusGDRqUTJkyJbu8pKQkGT58eNKxY8ckIpJBgwYlSZIk3/ve95IDDjgg6dy5c9KuXbukb9++yY033pisWbOmxn0GgKYiJ0mq+dMhAMBGHn300TjhhBPib3/7Wxx88MG11u6zzz5RVFRU7b2LtrbLLrssfv7zn8fKlSub1E2yAQBwTykA4FM++eSTSs/XrVsXt99+exQWFsZ+++2XHa+oqIi1a9dWqn322WfjzTffjMMOO6ze+1qyZEncd999ccghhwikAACaIPeUAgAqueSSS+KTTz6JgQMHRnl5eTz88MPx4osvxk033RTt2rXL1s2dOzeGDBkSX/3qV6NHjx7x73//OyZOnBjFxcVxwQUXbPW+Bg4cGIcddljstttusWDBgrj77rujtLQ0rrnmmq3+WgAA1D+hFABQyRFHHBE/+tGP4vHHH4/Vq1dHnz594vbbb69yY+UuXbpE//794xe/+EUsWrQoCgoKYvjw4fH9738/ttlmm63e19FHHx0PPfRQ3HXXXZGTkxP77bdf3H333XHooYdu9dcCAKD+uacUAAAAAKlzTykAAAAAUieUAgAAACB17im1FWQymZg3b1507NgxcnJyGrodAAAAgAaTJEmsWLEievToEbm5Nc+HEkptBfPmzYuePXs2dBsAAAAAjcaHH34YO+ywQ43LhVJbQceOHSNi/Te7sLCwgbsBAAAAaDilpaXRs2fPbF5SE6HUVrDhkr3CwkKhFAAAAEDEJm9x5EbnAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKSudUM3QMMoK1+7WfUFeQ4VAAAAYOuRNLRQHb/95GbVZ245tp46AQAAAFoil+8BAAAAkDozpVqoFTceVel52Zq1UXzdlIiIKBn3pSho69AAAAAA6o/koYWq7R5RBW1bu4cUAAAAUK9cvgcAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKROKAUAAABA6oRSAAAAAKSuyYVSEyZMiB133DHy8/NjwIAB8fLLL9da/+CDD0bfvn0jPz8/9txzz/jTn/5UafmoUaMiJyen0tewYcPqcxcAAAAAWrwmFUo98MADMWbMmBg3bly8/vrrsffee8fQoUNj4cKF1da/+OKLcfrpp8c555wTM2bMiBEjRsSIESPin//8Z6W6YcOGxfz587Nfv/3tb9PYHQAAAIAWq0mFUj/+8Y9j9OjRcfbZZ0e/fv1i4sSJ0b59+/jlL39Zbf1Pf/rTGDZsWHzzm9+M3XbbLW644YbYb7/94o477qhUl5eXF8XFxdmvLl26pLE7AAAAAC1Wkwml1qxZE6+99loMGTIkO5abmxtDhgyJ6dOnV7vO9OnTK9VHRAwdOrRK/bPPPhvdunWLz3/+83HhhRfGkiVLtv4OAAAAAJDVuqEbqKvFixfHunXronv37pXGu3fvHv/+97+rXaekpKTa+pKSkuzzYcOGxYknnhg77bRTvPfee3H11VfHUUcdFdOnT49WrVpVu93y8vIoLy/PPi8tLY2IiEwmE5lMZov2r6Flkkylx011PwAAAICGVddMocmEUvXltNNOyz7ec889Y6+99opddtklnn322Rg8eHC164wfPz6uu+66KuOLFi2K1atX11uv9WnVmnXZx4sWLoqyttUHcgAAAAC1WbFiRZ3qmkwote2220arVq1iwYIFlcYXLFgQxcXF1a5TXFy8WfURETvvvHNsu+228e6779YYSo0dOzbGjBmTfV5aWho9e/aMoqKiKCwsrOsuNSpla9ZmHxd1K4qCtk3m0AAAAAAakfz8/DrVNZnkoW3bttG/f/+YOnVqjBgxIiLWTwebOnVqXHzxxdWuM3DgwJg6dWpcdtll2bEpU6bEwIEDa3ydjz76KJYsWRLbbbddjTV5eXmRl5dXZTw3Nzdyc5vMbboqyc3JrfS4qe4HAAAA0LDqmik0qeRhzJgx8T//8z9x7733xsyZM+PCCy+MsrKyOPvssyMi4qyzzoqxY8dm67/+9a/HU089FT/60Y/i3//+d1x77bXx6quvZkOslStXxje/+c34+9//Hu+//35MnTo1jj/++OjTp08MHTq0QfYRAAAAoCVoMjOlIiJOPfXUWLRoUXz3u9+NkpKS2GeffeKpp57K3sz8gw8+qJTGHXTQQXH//ffHd77znbj66qtj1113jUcffTT22GOPiIho1apV/OMf/4h77703li1bFj169IgjjzwybrjhhmpnQgEAAACwdeQkSZI0dBNNXWlpaXTq1CmWL1/edO8pVb42On77yYiIWHHjUVGQ16TySgAAAKCRqGtO0qQu3wMAAACgeRBKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqRNKAQAAAJA6oRQAAAAAqWvd0A0AbFBWvnaz6gvynMIAAACaKu/ogEaj47ef3Kz6zC3H1lMnAAAA1DeX7wEAAACQOjOlgEZjxY1HVXpetmZtFF83JSIiSsZ9KQraOmUBAAA0F97hUcXqinXu1UODqO24K2jb2nEJAADQjLh8r4Vbl0ni9/+YF8fc/XJ2rOjav8SQidPj9/+YF+sySQN2BwAAADRXph20YKWrK+Lke1+Np2ctrrJs2ruLY9q7i2PIrtvGQyP3j8L8Ng3QIQAAANBcmSnVQq3LJDUGUht7etbiOPneV82YAgAAALYqoVQL9eg/528ykNrg6VmL4w9vl9RzRwAAAEBLIpRqoe58cc5m1r9fP40AAAAALZJQqgVaXbEupr1bt1lSG0ydtThWV6yrp44AAACAlkYo1QKtKF+b6noAAAAAnyaUaoE65m3Zhy5u6XoAAAAAnyaUaoHy27SKI/psu1nrDN5128hv06qeOgIAAABaGqFUC3XhQb03s37H+mkEAAAAaJGEUi3UiD22iyG71m221JBdt43jdy+u544AAACAlkQo1UK1ys2Jh0buv8lgasiu28ZDI/ePVrk5KXUGAAAAtATuXN2CFea3iSdHHxh/eLsk7vjb7Hj2vSXZZYN33TYuPGjHOH73YoEUAAAAsNUJpVq4Vrk5ceKe28XQzxVFx28/GRERi649MrbpkNfAnQEAAADNmcv3qMKn7AEAAAD1TSgFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkrnVDNwAAjVlZ+drNqi/I818rAADUhd+cAaAWHb/95GbVZ245tp46AQCA5sXlewAAAACkzkwpAKjFihuPqvS8bM3aKL5uSkRElIz7UhS09V8pDSNTUbZZ9bltCuqpEwCALeM3aQCoRW33iCpo29o9pGgwcyZ02az6nS5bU0+dAABsGZfvAQAAAJA6f94FAGiCel/0caXnmYqy+PCuHSIioud5H7lcDwBo9IRSAABNUG2hU26bAqEUANDouXwPAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKaBJWV6xr6BYAAADYippcKDVhwoTYcccdIz8/PwYMGBAvv/xyrfUPPvhg9O3bN/Lz82PPPfeMP/3pT5WWJ0kS3/3ud2O77baLdu3axZAhQ2LWrFn1uQvAJqzLJPH7f8yLY+7+78930bV/iSETp8fv/zEv1mWSBuwOAACAraFJhVIPPPBAjBkzJsaNGxevv/567L333jF06NBYuHBhtfUvvvhinH766XHOOefEjBkzYsSIETFixIj45z//ma35wQ9+ELfddltMnDgxXnrppSgoKIihQ4fG6tWr09otYCOlqyviqP/5e3z516/Fc/+7pNKyae8uji//+rU46n/+HqWrKxqoQwAAALaGJhVK/fjHP47Ro0fH2WefHf369YuJEydG+/bt45e//GW19T/96U9j2LBh8c1vfjN22223uOGGG2K//faLO+64IyLWz5L6yU9+Et/5znfi+OOPj7322it+/etfx7x58+LRRx9Ncc+AiPUzpE6+99V4etbiWuuenrU4Tr73VTOmAAAAmrDWDd1AXa1ZsyZee+21GDt2bHYsNzc3hgwZEtOnT692nenTp8eYMWMqjQ0dOjQbOM2ePTtKSkpiyJAh2eWdOnWKAQMGxPTp0+O0007b7B7XrFlTZTw3Nzdat25dqa4mOTk50aZNmy2qraioiCSp/k36pmrXVKz977K1FRF5rWus/bS2bdtuUe3atWsjk8lsldo2bdpETk5OvdauW7cu1q2r+b5Gm1PbunXryM3NbTS1mUwm1q5dW2Ntq1atolWrVvVe+9AbH20ykNrg6VmL4/dvfBQj9uhe63aTJImKippnVW3881lftRG1/yw3hXPEltZGNL9zxMbnyyT+uy/OEfV/jqhr7aZ+PpvrOSJTUXXcOaJutX6P+Gy1jeHn3jnC7xGfrnWOcI5wjtj82q15jqht3Y01mVBq8eLFsW7duujevfIb0O7du8e///3vatcpKSmptr6kpCS7fMNYTTXVKS8vj/Ly8uzz0tLSiIi45ZZbIi8vr0p9nz594owzzsg+/8EPflDjAda7d+8YNWpU9vmtt94aq1atqrZ2u+22i/POOy/7/I477ohly5ZVW1tUVBRf+9rXss9//vOfx6JFi7LP1yS5EbFfRERMnPjz+Nbll2aX3X333TF//vxqt9u+ffv45je/mX1+3333xZw5c6qtbdOmTVx99dXZ57/97W/j3XffrbY2ImLcuHHZxw899FDMnDmzxtqxY8dm/2N57LHH4s0336yx9oorroiCgoKIiHjyySfj1VdfrbH261//enTu3DkiIqZMmVJjABoRceGFF0a3bt0iIuK5556L5557rsbac889N7bffvuIWH+Z6dNPP11j7ciRI2PHHXeMiIhXXnklnnzyyRprTz/99Pjc5z4XERFvvvlm/OEPf6ix9uSTT47dd989IiLefvvteOihh2qsPf7442OfffaJiIj//Oc/8dvf/rbG2qOOOioOOOCAiIh4//334957762xdsiQIXHwwQdHRMTcuXNj7OS/RkRhjfWfNnby8/F2u/9UGR84cGAceeSRERGxbNmy+OlPf1rjNvbff/8YPnx4RESUlZXFLbfcUmPt3nvvHSNGjIiI9SfZ8ePH11i72267xSmnnJJ9fuONN9ZY2xTOERvr3LlzfP3rX88+b2nniI3Plx9/vCzad9smIpwj0jhH/OIXv6ixdtCgQXHYYYdFRMTChQvjzjvvrLG2uZ4jWsea+GqX9Y8zmUxEJuMc8X/8HrFjRDhHtPRzRITfIzbmHLGec8R6zhHrbc1zxMaZSW2aTCjVmIwfPz6uu+66KuNlZWXVpq+lpaWV7nu1cuXKGlPaFStWVKn95JNP6lS7YsWKKCsrq7Y2Pz+/1tqKja7kXLlyZZ23m8lk6lzbunXrSrWlpaU11kbEZtdu+I9i+fLltdYuWrQou7wutRsS3mXLltVau3jxf2f41KV2Q6r88ccf11q7ZMmSaN++fZ1rN3zfli5dWmvt0qVLt6h2yZIltdZ+/PHHW1Q7d8GieD9T90AqImJ2pjCWl62K1lH5L2bLli3LbndT/8bLly/P1q5atarOtWvWrKm19tM/95tT2xjPERtr1apViz5HbHy+XLx4cbSJ9X8ldI6o33PE4sWLa63d+Od+c2qb0zmidU5FxP+FUosWLYqc1mXOEZtR6/cI54jqapvTOSLC7xEbc45YzzliPeeI9bbmOaKuoVROUtv8x0ZkzZo10b59+3jooYey6WHE+mR32bJl1aa0vXr1ijFjxsRll12WHRs3blw8+uij8eabb8b//u//xi677BIzZszIJrMR61PSffbZp8bEs7qZUj179owFCxZEYWHVN9VNYUpt2Zp1UXT9tIiIWDzuiOhc0K5O240wpXZLak2prVq7oPST2P5702qsrcmcsYOiqKBtpTFTaquvNe1+65wjNj5fLrvhyOiQt/574Rxh2n1DnyMyFWWx4O7tIiKi54VLIrdNgXNEHWv9HvHZahvDz71zhN8jPl3rHOEc4Ryx+bVb8xxRWloa3bt3j+XLl1ebk2zQZGZKtW3bNvr37x9Tp07NhlKZTCamTp0aF198cbXrDBw4MKZOnVoplJoyZUoMHDgwIiJ22mmnKC4ujqlTp2ZDqdLS0njppZfiwgsvrLGXvLy8ai/Ty8/Pj/z8/E3uS11qtqS2up7qWrsu578/oHlt87InkM+63dps/B9BU6jNzc2t9EPX3Go3PlE1RG2Xgrof6xsrKiyI/Dataq3Z8J9GXdRXbX393Kd1jmiI2sbwc19d7cbny1a5rbLny8bys9xczxGbUxvROH7u0z5HZFr9981Bbm5u5ObmOkc0otrG8rPsHLFeY/i593tE/dY2hp9754j/1jb0z71zRP3Xfvrns9ndUyoiYsyYMTFy5MjYf//944ADDoif/OQnUVZWFmeffXZERJx11lmx/fbbZ6+9/PrXvx6DBg2KH/3oRzF8+PCYPHlyvPrqq3HXXXdFxPpk77LLLovvfe97seuuu8ZOO+0U11xzTfTo0aPSbCyg/uW3aRVH9Nk2pr1btxudR0QM3nXbTQZSAAAANE5NKpQ69dRTY9GiRfHd7343SkpKYp999omnnnoqe6PyDz74oNIMn4MOOijuv//++M53vhNXX3117LrrrvHoo4/GHnvska351re+FWVlZXHeeefFsmXL4pBDDomnnnpqsxJBYOu48KDemxVKXXjQjvXXDAAAAPWqydxTqjErLS2NTp06bfJaycasrHxtdPz2+k9aWHHjUVGQ16TySpqJdZkkjvqfv8fTszYdTA3Zddt4cvSB0So3J4XO4L+cL2msMhVlMWfC+jud977o48htU9DAHQEALVVdc5LcGpcApKxVbk48NHL/GLLrtrXWDdl123ho5P4CKQBoZDIVZZv1BUDL5s+7QKNSmN8mnhx9YPzh7ZK442+z49n3lmSXDd5127jwoB3j+N2LBVIA0AhtmK1XVztdVrcb4QLQPAmlgEanVW5OnLjndjH0c0XZy6QWXXtkbNOh7p+4AgAAQOMmlAKaBJ+yBwCNX++LPq70PFNRFh/etUNERPQ87yP3OgPYyOZextwcz6FCKQAAYKuo7Q1TbpuCZvmGCmBLueTZjc4BAAAAaABmSgEAAACkzCXPQikAAACA1Lnk2eV7AAAAADQAM6VaqLLytZWfr1lb7eMNCvIcKgAAAMDWI2looTp++8kalxVfN6XKWOaWY+uzHQAAAKCFcfkeAAAAAKkzU6qFWnHjUQ3dAgAAANCCCaVaKPeIAgAAABqSy/cAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAJqZZO3qhm4BAGCThFIAAE1YklkXZbMejgWPHp8d++Dn28X83w+NslkPR5JZ14DdAQDUrHVDNwAAwJbJlJfGgidOjdUfTK2ybPWHz8TqD5+J/F6Do/vwByI3r7ABOgQAqJmZUgAATVCSWVdjILWx1R9MjQVPnGrGFADQ6AilAACaoFXv/WGTgdQGqz+YGqv+97F67ggAYPMIpQAAmqDSf/x88+rf3Lx6AID6JpQCAGhiMmtXx+oPn9msdVZ/OC0yPpUPAGhEhFIAsIVWV7hHDw0jWbMi1fUAAOqDUAoA6mBdJonf/2NeHHP3y9mxomv/EkMmTo/f/2NerMskDdgdLU1O246prgcAUB9aN3QDANDYla6uiJPvfTWenrW4yrJp7y6Oae8ujiG7bhsPjdw/CvPbNECHtDS5rfMjv+fhm3UJX37PIyK3dX49dgUAsHnMlAKAWqzLJDUGUht7etbiOPneV82YIjWFe52/efV7b149AEB9E0oBQC0e/ef8TQZSGzw9a3H84e2Seu4I1mu/y/GR32twnWrzew2O9jsfV88dAQBsHqEUANTizhfnbGb9+/XTCHxKTm6r6D78gU0GU/m9Bkf34Q9ETm6rlDoDAKgboRQA1GB1xbqY9m7dZkltMHXWYp/KR2py8wqjeMTj0e2YByJ/h0GVluX3PCK6HfNAFI94PHLzChuoQwCAmrnROQDUYEX52i1eL7+NWSmkIye3VRT0OSHa9T4y5kzoEhERvc6fH63abdPAnQEA1M5MKQCoQce8LfvbzZauB1tLjk/ZAwCaAKEUANQgv02rOKLPtpu1zuBdtzVLCgAA6kAoBQC1uPCg3ptZv2P9NAIAAM2MUAoAajFij+1iyK51my01ZNdt4/jdi+u5IwAAaB6EUgBQi1a5OfHQyP03GUwN2XXbeGjk/tEqNyelzgAAoGlzJ1YA2ITC/Dbx5OgD4w9vl8Qdf5sdz763JLts8K7bxoUH7RjH714skAIAgM0glAKAOmiVmxMn7rldDP1cUXT89pMREbHo2iNjmw55DdwZAAA0TS7fA4At5FP2AABgywmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1AmlAAAAAEidUAoAAACA1LVu6AYANigrX1v5+Zq11T7eoCDPKQwAAKCp8o4OaDQ6fvvJGpcVXzelyljmlmPrsx0AAJqJTEXZZtXntimop06AjQmlAAAAaNbmTOiyWfU7XbamnjoBNiaUAhqNFTce1dAtAAAAkBKhFNBouEcUAAD1ofdFH1d6nqkoiw/v2iEiInqe95HL9aCBeAcIAABAs1Zb6JTbpkAoBQ0kt6EbAAAAAKDlEUoBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAD1Llm7uqFbAKCRaTKh1NKlS+OMM86IwsLC6Ny5c5xzzjmxcuXKWtdZvXp1XHTRRbHNNttEhw4d4qSTTooFCxZUqsnJyanyNXny5PrcFQAAaNaSzLoom/VwLHj0+OzYBz/fLub/fmiUzXo4ksy6BuwOgMaiyYRSZ5xxRrz99tsxZcqUePzxx+P555+P8847r9Z1Lr/88vjjH/8YDz74YDz33HMxb968OPHEE6vU3XPPPTF//vzs14gRI+ppLwAAoHnLlJdGyaPHxMInTovVc5+vtGz1h8/EwidOi5JHj4lMeWkDdQhAY9G6oRuoi5kzZ8ZTTz0Vr7zySuy///4REXH77bfH0UcfHbfcckv06NGjyjrLly+Pu+++O+6///444ogjImJ9+LTbbrvF3//+9zjwwAOztZ07d47i4uJ0dgYAAJqpJLMuFjxxaqz+YGqtdas/mBoLnjg1ikc8Hjm5rVLqDoDGpkmEUtOnT4/OnTtnA6mIiCFDhkRubm689NJLccIJJ1RZ57XXXouKiooYMmRIdqxv377Rq1evmD59eqVQ6qKLLopzzz03dt5557jgggvi7LPPjpycnBr7KS8vj/Ly8uzz0tL1f+XJZDKRyWQ+074C0Lhlkkylx877NBYbH4uZTCbCsUkDKHv30U0GUhus/mBqlL37h2jfZ0T9NgXVcM6kMWpOx2Vdf0duEqFUSUlJdOvWrdJY69ato2vXrlFSUlLjOm3bto3OnTtXGu/evXulda6//vo44ogjon379vGXv/wlvva1r8XKlSvj0ksvrbGf8ePHx3XXXVdlfNGiRbF6tRs4AjRnq9b89z4oixYuirK2/sJP45CsXZV9vGjRoshpXdaA3dBSlb96+2bVL3nt9lhZeFA9dQM1c86kMWpOx+WKFSvqVNegodRVV10VN998c601M2fOrNcerrnmmuzjfffdN8rKyuKHP/xhraHU2LFjY8yYMdnnpaWl0bNnzygqKorCwsJ67ReAhlW2Zm32cVG3oiho2yT+vkMLkKkoiw//73FRUVHktilo0H5oeZK1q+ODhS9u1jqZBS9EUdfCyGmdX09dQfWcM2mMmtNxmZ9ft/N6g/4m/Y1vfCNGjRpVa83OO+8cxcXFsXDhwkrja9eujaVLl9Z4L6ji4uJYs2ZNLFu2rNJsqQULFtR6/6gBAwbEDTfcEOXl5ZGXl1dtTV5eXrXLcnNzIze3ydw7HoAtkJuTW+mx8z4NJVPxqb+ervuk8uNPHZtN+RdbmoZ1a7fwL/pryyK3bfut2wxsykbnSO/jaDSa0XFZ194bNJQqKiqKoqKiTdYNHDgwli1bFq+99lr0798/IiKmTZsWmUwmBgwYUO06/fv3jzZt2sTUqVPjpJNOioiId955Jz744IMYOHBgja/1xhtvRJcuXWoMpAAAGoM5E7rUuOzDu3aoMrbTZWvqsx2InLYdU10PgKavSVxzsNtuu8WwYcNi9OjRMXHixKioqIiLL744TjvttOwn782dOzcGDx4cv/71r+OAAw6ITp06xTnnnBNjxoyJrl27RmFhYVxyySUxcODA7E3O//jHP8aCBQviwAMPjPz8/JgyZUrcdNNNccUVVzTk7gIAQJOT2zo/8nseHqs/fKbO6+T3PCJyXboH0GI1iVAqImLSpElx8cUXx+DBgyM3NzdOOumkuO2227LLKyoq4p133olVq/57Y7Bbb701W1teXh5Dhw6Nn/3sZ9nlbdq0iQkTJsTll18eSZJEnz594sc//nGMHj061X0DANhcvS/6uKFbgCoK9zp/s0Kpwr3Pr8duAGjscpIkSRq6iaautLQ0OnXqFMuXL3ejc4Bmrqx8bXT89pMREbHixqOiIK/J/H0HoN4lmXVR8ugxsfqDqZusze81OIpHPB45uT7FlPRlKsqyl0H3vuhj992jUWhOx2Vdc5Kme9csAACgUcnJbRXdhz8Q+b0G11qX32twdB/+gEAKoIUTSgEAAFtNbl5hFI94PLod80Dk7zCo0rL8nkdEt2MeiOIRj0dunisMAFo61xwAAABbVU5uqyjoc0K0631k9lKUXufPj1bttmngzgBoTMyUAgAA6l2OT9kD4FOEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOpaN3QDANCYlZWvrfx8zdpqH29QkOe/VgAAqAu/OQNALTp++8kalxVfN6XKWOaWY+uzHQAAaDZcvgcAAABA6syUAoBarLjxqIZuAQAAmiWhFADUwj2iAACgfrh8DwAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUbVEo1apVq1i4cGGV8SVLlkSrVq0+c1MAAAAANG9bFEolSVLteHl5ebRt2/YzNQQAAABA89d6c4pvu+22iIjIycmJX/ziF9GhQ4fssnXr1sXzzz8fffv23bodAgAAANDsbFYodeutt0bE+plSEydOrHSpXtu2bWPHHXeMiRMnbt0OAQAAAGh2NiuUmj17dkREHH744fHwww9Hly5d6qUpAAAAAJq3zQqlNnjmmWe2dh8AAAAAtCBbdKPzk046KW6++eYq4z/4wQ/iy1/+8mduCgAAAIDmbYtCqeeffz6OPvroKuNHHXVUPP/885+5KQAAAACaty0KpVauXBlt27atMt6mTZsoLS39zE0BAAAA0LxtUSi15557xgMPPFBlfPLkydGvX7/P3BQAAAAAzdsW3ej8mmuuiRNPPDHee++9OOKIIyIiYurUqfHb3/42Hnzwwa3aIAAAAADNzxaFUscee2w8+uijcdNNN8VDDz0U7dq1i7322iuefvrpGDRo0NbuEQAAAIBmZotCqYiI4cOHx/Dhw7dmLwAAAAC0EFt0T6mIiGXLlsUvfvGLuPrqq2Pp0qUREfH666/H3Llzt1pzAAAAADRPWzRT6h//+EcMGTIkOnXqFO+//36ce+650bVr13j44Yfjgw8+iF//+tdbu08AAAAAmpEtmik1ZsyYGDVqVMyaNSvy8/Oz40cffXQ8//zzW605AAAAAJqnLQqlXnnllTj//POrjG+//fZRUlLymZsCAAAAoHnbolAqLy8vSktLq4z/5z//iaKios/cFAAAAADN2xaFUscdd1xcf/31UVFREREROTk58cEHH8SVV14ZJ5100lZtEAAAAIDmZ4tCqR/96EexcuXK6NatW3zyyScxaNCg6NOnT3Ts2DFuvPHGrd0jAAAAAM3MFn36XqdOnWLKlCnxwgsvxJtvvhkrV66M/fbbL4YMGbK1+wMAAACgGapzKNW1a9f4z3/+E9tuu238v//3/+KnP/1pHHzwwXHwwQfXZ38AAAAANEN1vnxvzZo12Zub33vvvbF69ep6awoAAACA5q3OM6UGDhwYI0aMiP79+0eSJHHppZdGu3btqq395S9/udUaBAAAAKD5qXMo9Zvf/CZuvfXWeO+99yIiYvny5WZLAQAAALBF6hxKde/ePb7//e9HRMROO+0U9913X2yzzTb11hgAAAAAzVed7ynVtWvXWLx4cUREHH744dG2bdt6awoAAACA5s2NzgEAAABInRudAwAAAJC6LbrReU5OjhudAwAAALDF3OgcAAAAgNTV+Z5SERFHH310LF++PGbPnh3bbLNNfP/7349ly5Zlly9ZsiT69eu3tXsEAAAAoJnZrFDqqaeeivLy8uzzm266KZYuXZp9vnbt2njnnXe2XncAAAAANEubFUp9WpIkW6sPAAAAAFqQzxRKAQAAAMCW2KxQKicnJ3JycqqMAQAAAMDmqPOn70Wsv1xv1KhRkZeXFxERq1evjgsuuCAKCgoiIirdbwoAAAAAarJZodTIkSMrPf/qV79apeass876bB0BAABNUqairMbnn14WEZHbpqDeewKg8dqsUOqee+6prz4AAIAmbs6ELjUu+/CuHaqM7XTZmvpsB4BGzo3OAQAAABqRZO3qhm4hFZs1U6ohLV26NC655JL44x//GLm5uXHSSSfFT3/60+jQoUON69x1111x//33x+uvvx4rVqyIjz/+ODp37vyZtwsAAFTV+6KPG7oFgCYnyayLVe/9IUrf+Fl27IOfbxf5PQ+Pwr3Oj/a7HB85ua0asMP602RmSp1xxhnx9ttvx5QpU+Lxxx+P559/Ps4777xa11m1alUMGzYsrr766q26XQAAoKrcNgWb9QXQ0mXKS6Pk0WNi4ROnxeq5z1datvrDZ2LhE6dFyaPHRKa8tIE6rF85SZIkDd3EpsycOTP69esXr7zySuy///4REfHUU0/F0UcfHR999FH06NGj1vWfffbZOPzww6vMlPqs292gtLQ0OnXqFMuXL4/CwsIt20kAAABSkakoy94DrfdFHwtJaRBJZl2UPHpMrP5g6iZr83sNjuIRjzeZGVN1zUmaxOV706dPj86dO2eDo4iIIUOGRG5ubrz00ktxwgknpLrd8vLyKC8vzz4vLV2fWGYymchkMlvUCwAAAOnY+H1bJpOJ8D6OBlD27qN1CqQiIlZ/MDXK3v1DtO8zon6b2krqmo00iVCqpKQkunXrVmmsdevW0bVr1ygpKUl9u+PHj4/rrruuyviiRYti9eqWcTMyAACApipZuyr7eNGiRZHTuqwBu6GlKn/19s2qX/La7bGy8KB66mbrWrFiRZ3qGjSUuuqqq+Lmm2+utWbmzJkpdVN3Y8eOjTFjxmSfl5aWRs+ePaOoqMjlewAAAI1cpqIsPvy/x0VFRS7fI3XJ2tXxwcIXN2udzIIXoqhrYeS0zq+nrrae/Py69digodQ3vvGNGDVqVK01O++8cxQXF8fChQsrja9duzaWLl0axcXFW/z6W7rdvLy8yMvLqzKem5sbublN5t7xAAAALdNG79u8j6MhrFu7hbPz1pZFbtv2W7eZelDXn6kGDaWKioqiqKhok3UDBw6MZcuWxWuvvRb9+/ePiIhp06ZFJpOJAQMGbPHr19d2AQAAAGqS07Zjqus1Vk0iDt5tt91i2LBhMXr06Hj55ZfjhRdeiIsvvjhOO+207CfkzZ07N/r27Rsvv/xydr2SkpJ444034t13342IiLfeeiveeOONWLp0aZ23CwAAALA15bbOj/yeh2/WOvk9j4jcJnDp3uZoEqFURMSkSZOib9++MXjw4Dj66KPjkEMOibvuuiu7vKKiIt55551Yteq/N6ybOHFi7LvvvjF69OiIiDj00ENj3333jccee6zO2wUAAADY2gr3On/z6vfevPqmICdJkqShm2jqSktLo1OnTrF8+XI3OgcAAGjkMhVlMWdCl4iI6H3Rx250ToNIMuui5NFjYvUHUzdZm99rcBSPeDxyclul0NlnV9ecpMnMlAIAAABoLnJyW0X34Q9Efq/Btdbl9xoc3Yc/0GQCqc0hlAIAAABoALl5hVE84vHodswDkb/DoErL8nseEd2OeSCKRzweuXnN86qsBv30PQAAAICWLCe3VRT0OSHa9T4ye1lpr/PnR6t22zRwZ/XPTCkAAACARiSnmX3KXk2EUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAAACkTigFAAAAQOqEUgAAALRYydrVDd0CtFhCKQAAAFqEJLMuymY9HAsePT479sHPt4v5vx8aZbMejiSzrgG7g5andUM3AAAAAPUtU14aC544NVZ/MLXKstUfPhOrP3wm8nsNju7DH4jcvMIG6BBaHjOlAAAAaNaSzLoaA6mNrf5gaix44lQzpiAlQikAAACatVXv/WGTgdQGqz+YGqv+97F67giIEEoBAADQzJX+4+ebV//m5tUDW0YoBQAAQLOVWbs6Vn/4zGats/rDaZHxqXxQ74RSAAAANFvJmhWprgfUnVAKAACAZiunbcdU1wPqTigFAABAs5XbOj/yex6+Wevk9zwiclvn11NHwAZCKQAAAJq1wr3O37z6vTevHtgyQikAAACatfa7HB/5vQbXqTa/1+Bov/Nx9dwRECGUAgAAoJnLyW0V3Yc/sMlgKr/X4Og+/IHIyW2VUmfQsgmlAAAAaPZy8wqjeMTj0e2YByJ/h0GVluX3PCK6HfNAFI94PHLzChuoQ2h5Wjd0AwAAAJCGnNxWUdDnhGjX+8iYM6FLRET0On9+tGq3TQN3Bi2TmVIAAAC0WDk+ZQ8ajFAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABIXZMJpZYuXRpnnHFGFBYWRufOneOcc86JlStX1rrOXXfdFYcddlgUFhZGTk5OLFu2rErNjjvuGDk5OZW+vv/979fTXgAAAAAQ0YRCqTPOOCPefvvtmDJlSjz++OPx/PPPx3nnnVfrOqtWrYphw4bF1VdfXWvd9ddfH/Pnz89+XXLJJVuzdQAAAAA+pXVDN1AXM2fOjKeeeipeeeWV2H///SMi4vbbb4+jjz46brnllujRo0e161122WUREfHss8/Wuv2OHTtGcXHx1mwZAAAAgFo0iZlS06dPj86dO2cDqYiIIUOGRG5ubrz00kufefvf//73Y5tttol99903fvjDH8batWs/8zYBAAAAqFmTmClVUlIS3bp1qzTWunXr6Nq1a5SUlHymbV966aWx3377RdeuXePFF1+MsWPHxvz58+PHP/5xjeuUl5dHeXl59nlpaWlERGQymchkMp+pHwAAAOrXxu/bMplMhPdxNALN6bisazbSoKHUVVddFTfffHOtNTNnzqzXHsaMGZN9vNdee0Xbtm3j/PPPj/Hjx0deXl6164wfPz6uu+66KuOLFi2K1atX11uvAAAAfHbJ2lXZx4sWLYqc1mUN2A2s15yOyxUrVtSprkFDqW984xsxatSoWmt23nnnKC4ujoULF1YaX7t2bSxdunSr3wtqwIABsXbt2nj//ffj85//fLU1Y8eOrRRmlZaWRs+ePaOoqCgKCwu3aj8AAABsXZmKsvjw/x4XFRVFbpuCBu0HIprXcZmfn1+nugYNpYqKiqKoqGiTdQMHDoxly5bFa6+9Fv3794+IiGnTpkUmk4kBAwZs1Z7eeOONyM3NrXK54Mby8vKqnUWVm5sbublN4jZdAAAALddG79u8j6PRaEbHZV17bxL3lNptt91i2LBhMXr06Jg4cWJUVFTExRdfHKeddlr2k/fmzp0bgwcPjl//+tdxwAEHRMT6e1GVlJTEu+++GxERb731VnTs2DF69eoVXbt2jenTp8dLL70Uhx9+eHTs2DGmT58el19+eXz1q1+NLl26NNj+AgAAADR3TSZ2mzRpUvTt2zcGDx4cRx99dBxyyCFx1113ZZdXVFTEO++8E6tW/fcazIkTJ8a+++4bo0ePjoiIQw89NPbdd9947LHHImL9jKfJkyfHoEGDYvfdd48bb7wxLr/88krbBQAAAGDry0mSJGnoJpq60tLS6NSpUyxfvtw9pQAAABq5TEVZzJmw/uqY3hd93KTv3UPz0ZyOy7rmJE1mphQAAAAAzYdQCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUCaUAAAAASJ1QCgAAAIDUtW7oBgAAAABamkxFWY3PP70sIiK3TUG995Q2oRQAAABAyuZM6FLjsg/v2qHK2E6XranPdhqEy/cAAAAASJ2ZUgAAAAAp633Rxw3dQoMTSgEAAACkrDneI2pzuXwPAAAAgNQ1mVBq6dKlccYZZ0RhYWF07tw5zjnnnFi5cmWt9Zdcckl8/vOfj3bt2kWvXr3i0ksvjeXLl1eq++CDD2L48OHRvn376NatW3zzm9+MtWvX1vfuAAAAALRoTebyvTPOOCPmz58fU6ZMiYqKijj77LPjvPPOi/vvv7/a+nnz5sW8efPilltuiX79+sWcOXPiggsuiHnz5sVDDz0UERHr1q2L4cOHR3Fxcbz44osxf/78OOuss6JNmzZx0003pbl7AAAAAC1KTpIkSUM3sSkzZ86Mfv36xSuvvBL7779/REQ89dRTcfTRR8dHH30UPXr0qNN2HnzwwfjqV78aZWVl0bp163jyySfjmGOOiXnz5kX37t0jImLixIlx5ZVXxqJFi6Jt27Z12m5paWl06tQpli9fHoWFhVu2kwAAAKQiU1EWcyZ0iYj1N5t2bx/YuuqakzSJy/emT58enTt3zgZSERFDhgyJ3NzceOmll+q8nQ3fjNatW2e3u+eee2YDqYiIoUOHRmlpabz99ttbbwcAAAAAqKRJXL5XUlIS3bp1qzTWunXr6Nq1a5SUlNRpG4sXL44bbrghzjvvvErb3TiQiojs89q2W15eHuXl5dnnpaWlERGRyWQik8nUqR8AAAAaxsbv2zKZTIT3cbBV1TUbadBQ6qqrroqbb7651pqZM2d+5tcpLS2N4cOHR79+/eLaa6/9zNsbP358XHfddVXGFy1aFKtXr/7M2wcAAKD+JGtXZR8vWrQoclqXNWA30PysWLGiTnUNGkp94xvfiFGjRtVas/POO0dxcXEsXLiw0vjatWtj6dKlUVxcXOv6K1asiGHDhkXHjh3jkUceiTZt2mSXFRcXx8svv1ypfsGCBdllNRk7dmyMGTMm+7y0tDR69uwZRUVF7ikFAADQyGUqyuLD/3tcVFTknlKwleXn59eprkFDqaKioigqKtpk3cCBA2PZsmXx2muvRf/+/SMiYtq0aZHJZGLAgAE1rldaWhpDhw6NvLy8eOyxx6p8UwYOHBg33nhjLFy4MHt54JQpU6KwsDD69etX43bz8vIiLy+vynhubm7k5jaJ23QBAAC0XBu9b/M+Dra+uv5MNYmfvN122y2GDRsWo0ePjpdffjleeOGFuPjii+O0007LfvLe3Llzo2/fvtmZT6WlpXHkkUdGWVlZ3H333VFaWholJSVRUlIS69ati4iII488Mvr16xdnnnlmvPnmm/HnP/85vvOd78RFF11UbegEAAAAwNbRJG50HhExadKkuPjii2Pw4MGRm5sbJ510Utx2223Z5RUVFfHOO+/EqlXrrw1+/fXXs5/M16dPn0rbmj17duy4447RqlWrePzxx+PCCy+MgQMHRkFBQYwcOTKuv/769HYMAAAAoAXKSZIkaegmmrrS0tLo1KlTLF++3D2lAAAAGrlMRVnMmdAlIiJ6X/Sxe0rBVlbXnKRJXL4HAAAAQPMilAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgda0bugEAAACoT5mKshqff3pZRERum4J67wkQSgEAANDMzZnQpcZlH961Q5WxnS5bU5/tAP/H5XsAAAAApM5MKQAAAJq13hd93NAtANUQSgEAANCsuUcUNE4u3wMAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFInlAIAAAAgdUIpAAAAAFLXZEKppUuXxhlnnBGFhYXRuXPnOOecc2LlypW11l9yySXx+c9/Ptq1axe9evWKSy+9NJYvX16pLicnp8rX5MmT63t3AAAAAFq01g3dQF2dccYZMX/+/JgyZUpUVFTE2WefHeedd17cf//91dbPmzcv5s2bF7fcckv069cv5syZExdccEHMmzcvHnrooUq199xzTwwbNiz7vHPnzvW5KwAAAAAtXk6SJElDN7EpM2fOjH79+sUrr7wS+++/f0REPPXUU3H00UfHRx99FD169KjTdh588MH46le/GmVlZdG69fo8LicnJx555JEYMWLEFvdXWloanTp1iuXLl0dhYeEWbwcAAACgqatrTtIkZkpNnz49OnfunA2kIiKGDBkSubm58dJLL8UJJ5xQp+1s+GZsCKQ2uOiii+Lcc8+NnXfeOS644II4++yzIycnp8btlJeXR3l5efZ5aWlpRERkMpnIZDKbs2sAAAAAzUpds5EmEUqVlJREt27dKo21bt06unbtGiUlJXXaxuLFi+OGG26I8847r9L49ddfH0cccUS0b98+/vKXv8TXvva1WLlyZVx66aU1bmv8+PFx3XXXVRlftGhRrF69uk79AAAAADRHK1asqFNdg4ZSV111Vdx888211sycOfMzv05paWkMHz48+vXrF9dee22lZddcc0328b777htlZWXxwx/+sNZQauzYsTFmzJhK2+/Zs2cUFRW5fA8AAABo0fLz8+tU16Ch1De+8Y0YNWpUrTU777xzFBcXx8KFCyuNr127NpYuXRrFxcW1rr9ixYoYNmxYdOzYMR555JFo06ZNrfUDBgyIG264IcrLyyMvL6/amry8vErLNtyWa+XKlZGb22Q+0BAAAABgq1u5cmVE/DcvqUmDhlJFRUVRVFS0ybqBAwfGsmXL4rXXXov+/ftHRMS0adMik8nEgAEDalyvtLQ0hg4dGnl5efHYY4/VKal74403okuXLjUGUtXZMC2tZ8+edV4HAAAAoDlbsWJFdOrUqcblTeLT9yIijjrqqFiwYEFMnDgxKioq4uyzz479998/7r///oiImDt3bgwePDh+/etfxwEHHBClpaVx5JFHxqpVq+KRRx6JgoKC7LaKioqiVatW8cc//jEWLFgQBx54YOTn58eUKVPiiiuuiCuuuKLae0bVJJPJxLx586Jjx4613iC9sdtwGeKHH37oMkQaDccljZHjksbKsUlj5LiksXJs0hg1l+MySZJYsWJF9OjRo9YryprEjc4jIiZNmhQXX3xxDB48OHJzc+Okk06K2267Lbu8oqIi3nnnnVi1alVERLz++uvx0ksvRUREnz59Km1r9uzZseOOO0abNm1iwoQJcfnll0eSJNGnT5/48Y9/HKNHj96s3nJzc2OHHXb4jHvYeBQWFjbpg5/myXFJY+S4pLFybNIYOS5prBybNEbN4bisbYbUBk0mlOratWt2VlR1dtxxx0rXKh522GGbvHZx2LBhMWzYsK3WIwAAAAB1467cAAAAAKROKEVWXl5ejBs3brNu8g71zXFJY+S4pLFybNIYOS5prBybNEYt7bhsMjc6BwAAAKD5MFMKAAAAgNQJpQAAAABInVAKAAAAgNQJpQAAAABInVCqhbn22mujb9++UVBQEF26dIkhQ4bESy+9tMn1JkyYEDvuuGPk5+fHgAED4uWXX06hW1qCioqKuPLKK2PPPfeMgoKC6NGjR5x11lkxb968Ta7ruKS+Pfzww3HkkUfGNttsEzk5OfHGG2/Uab0HH3ww+vbtG/n5+bHnnnvGn/70p/ptlBZlc899jkfq2/PPPx/HHnts9OjRI3JycuLRRx/d5DrPPvts7LfffpGXlxd9+vSJX/3qV/XeJy3L+PHj4wtf+EJ07NgxunXrFiNGjIh33nlnk+s5Z1Kf7rzzzthrr72isLAwCgsLY+DAgfHkk0/Wuk5zPyaFUi3M5z73ubjjjjvirbfeir/97W+x4447xpFHHhmLFi2qcZ0HHnggxowZE+PGjYvXX3899t577xg6dGgsXLgwxc5prlatWhWvv/56XHPNNfH666/Hww8/HO+8804cd9xxta7nuCQNZWVlccghh8TNN99c53VefPHFOP300+Occ86JGTNmxIgRI2LEiBHxz3/+sx47paXY3HOf45E0lJWVxd577x0TJkyoU/3s2bNj+PDhcfjhh8cbb7wRl112WZx77rnx5z//uZ47pSV57rnn4qKLLoq///3vMWXKlKioqIgjjzwyysrKalzHOZP6tsMOO8T3v//9eO211+LVV1+NI444Io4//vh4++23q61vCcdkTpIkSUM3QcMpLS2NTp06xdNPPx2DBw+utmbAgAHxhS98Ie64446IiMhkMtGzZ8+45JJL4qqrrkqzXVqIV155JQ444ICYM2dO9OrVq9oaxyVpev/992OnnXaKGTNmxD777FNr7amnnhplZWXx+OOPZ8cOPPDA2GeffWLixIn13CnN3eae+xyPpC0nJyceeeSRGDFiRI01V155ZTzxxBOV3lSddtppsWzZsnjqqadS6JKWaNGiRdGtW7d47rnn4tBDD622xjmThtC1a9f44Q9/GOecc06VZS3hmDRTqgVbs2ZN3HXXXdGpU6fYe++9a6x57bXXYsiQIdmx3NzcGDJkSEyfPj2tVmlhli9fHjk5OdG5c+dqlzsuacymT59e6diMiBg6dKhjk89sS859jkcaI8clDWH58uURsT4AqIljkzStW7cuJk+eHGVlZTFw4MBqa1rCMSmUaoEef/zx6NChQ+Tn58ett94aU6ZMiW233bba2sWLF8e6deuie/fulca7d+8eJSUlabRLC7N69eq48sor4/TTT4/CwsJqaxyXNGYlJSWOTerFlpz7HI80RjUdl6WlpfHJJ580UFc0Z5lMJi677LI4+OCDY4899qixzjmTNLz11lvRoUOHyMvLiwsuuCAeeeSR6NevX7W1LeGYFEo1Y5MmTYoOHTpkv/76179GRGSv33/xxRdj2LBhccopp7gPD6mp6biMWH/T81NOOSWSJIk777yzAbukJart2AQAmq6LLroo/vnPf8bkyZMbuhWIz3/+8/HGG2/ESy+9FBdeeGGMHDky/vWvfzV0Ww2mdUM3QP057rjjYsCAAdnn22+/fUREFBQURJ8+faJPnz5x4IEHxq677hp33313jB07tso2tt1222jVqlUsWLCg0viCBQuiuLi4fneAZqmm43JDIDVnzpyYNm1ajbOkIhyX1I+ajs3NVVxc7NikXmzJuc/xSGNU03FZWFgY7dq1a6CuaK4uvvjiePzxx+P555+PHXbYodZa50zS0LZt2+jTp09ERPTv3z9eeeWV+OlPfxo///nPq9S2hGPSTKlmrGPHjtnwqU+fPjX+J5/JZKK8vLzaZW3bto3+/fvH1KlTK9VPnTq1xuteoTbVHZcbAqlZs2bF008/Hdtss02t23BcUh/qes7clIEDB1Y6NiMipkyZ4tjkM9uSc5/jkcbIcUkakiSJiy++OB555JGYNm1a7LTTTptcx7FJQ6jt/XiLOCYTWoyVK1cmY8eOTaZPn568//77yauvvpqcffbZSV5eXvLPf/4zW3fEEUckt99+e/b55MmTk7y8vORXv/pV8q9//Ss577zzks6dOyclJSUNsRs0M2vWrEmOO+64ZIcddkjeeOONZP78+dmv8vLybJ3jkoawZMmSZMaMGckTTzyRREQyefLkZMaMGcn8+fOzNWeeeWZy1VVXZZ+/8MILSevWrZNbbrklmTlzZjJu3LikTZs2yVtvvdUQu0Azs6lzn+ORhrBixYpkxowZyYwZM5KISH784x8nM2bMSObMmZMkSZJcddVVyZlnnpmt/9///d+kffv2yTe/+c1k5syZyYQJE5JWrVolTz31VEPtAs3QhRdemHTq1Cl59tlnK/1+uWrVqmyNcyZpu+qqq5LnnnsumT17dvKPf/wjueqqq5KcnJzkL3/5S5IkLfOYFEq1IJ988klywgknJD169Ejatm2bbLfddslxxx2XvPzyy5XqevfunYwbN67S2O2335706tUradu2bXLAAQckf//731PsnOZs9uzZSURU+/XMM89k6xyXNIR77rmn2mNz42Nx0KBByciRIyut97vf/S753Oc+l7Rt2zbZfffdkyeeeCLdxmnWajv3OR5pCM8880y158oNx+LIkSOTQYMGVVlnn332Sdq2bZvsvPPOyT333JN63zRvNf1+ufGx5pxJ2v7f//t/Se/evZO2bdsmRUVFyeDBg7OBVJK0zGMyJ0mSJL15WQAAAADgnlIAAAAANAChFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACpE0oBAAAAkDqhFAAAAACp+//K6ZFIisg46wAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# rerun bootstrap for valid simultaneous inference (as values are not saved) \n", "dml_obj.bootstrap(n_rep_boot=5000)\n", "aggregated_eventstudy = dml_obj.aggregate(\"eventstudy\")\n", "# run bootstrap to obtain simultaneous confidence intervals\n", "print(aggregated_eventstudy)\n", "fig, ax = aggregated_eventstudy.plot_effects()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, the $ATT$ could also be aggregated according to (calendar) time periods or treatment groups, see the [user guide](https://docs.doubleml.org/dev/guide/models.html#effect-aggregation)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregation Details\n", "\n", "**TODO**: Keep or drop this?\n", "\n", "The `DoubleMLDIDAggregation` objects include several `DoubleMLFrameworks` which support methods like `bootstrap()` or `confint()`.\n", "Further, the weights can be accessed via the properties\n", "\n", " - ``overall_aggregation_weights``: weights for the overall aggregation\n", " - ``aggregation_weights``: weights for the aggregation\n", "\n", "To clarify, e.g. for the eventstudy aggregation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If one would like to consider how the aggregated effect with $e=0$ is computed, one would have to look at the third set of weights within the ``aggregation_weights`` property" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0. , 0. , 0. , 0. , 0. ,\n", " 0.23391813, 0. , 0. , 0. , 0. ,\n", " 0.76608187, 0. ])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aggregated_eventstudy.aggregation_weights[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ATT Estimation: Conditional Parallel Trends\n", "\n", "We briefly demonstrate how to use the `DoubleMLDIDMulti` model with conditional parallel trends. As the rationale behind DML is to flexibly model nuisance components as prediction problems, the DML DiD estimator includes pre-treatment covariates by default. In DiD, the nuisance components are the outcome regression and the propensity score estimation for the treatment group variable. This is why we had to enforce dummy learners in the unconditional parallel trends case to ignore the pre-treatment covariates. Now, we can replicate the classical doubly robust DiD estimator as of [Callaway and Sant'Anna(2021)](https://doi.org/10.1016/j.jeconom.2020.12.001) by using linear and logistic regression for the nuisance components. This is done by setting `ml_g` to `LinearRegression()` and `ml_m` to `LogisticRegression()`. Similarly, we can also choose other learners, for example by setting `ml_g` and `ml_m` to `LassoCV()` and `LogisticRegressionCV()`. We present the results for the ATTs and their event-study aggregation in the corresponding effect plots.\n", "\n", "Please note that the example is meant to illustrate the usage of the `DoubleMLDIDMulti` model in combination with ML learners. In real-data applicatoins, careful choice and empirical evaluation of the learners are required. Default measures for the prediction of the nuisance components are printed in the model summary, as briefly illustrated below." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " return math.isfinite(val)\n" ] }, { "data": { "text/plain": [ "(
,\n", " [,\n", " ,\n", " ])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HD6tmzZpq27atGjZs6Dyr53Rff/21Fi9erGXLlum2225ztnfu3Fk9evTQypUrddtttyk+Pl7PPvusevXqpY8//th5ptVjjz3mnDZXXL766itt3bpVjz/+uLMtMTFRWVlZqlKlSr74vLajR4+qUaNGOnbsmHx8fBQVFeUS5+/vr4oVK+ro0aOSpJMnT+rEiRP65ptvtGnTJk2ZMkU1a9bUkiVLNGbMGPn5+WnEiBGF5nns2DGX/Z+Z01dffXX+Bw8AAIqM6XsAAMDjXn75ZW3YsMHl51zuu+8+l8flypWTJK1bt06nTp0qkTwLc++997o8/uCDD2S32zVgwACdOHHC+RMdHa0GDRpo8+bNztjTC1Lp6ek6ceKE2rVrJ2NMkc7UWblypcqVK6du3bq57CtvWl3evjZu3CibzaYxY8a4TP27//77C9zuwYMHL+gsqbi4ON12222qU6eOHn74YWd73nTEgICAfM/Ju25YXkxGRob8/f0L3H5gYKAzLm+qXkJCghYvXqwJEyZowIAB+r//+z81bdpU06dPP2uu58rp9CmUAACg+HGmFAAA8LjWrVsXeqHzgvj6+qp69eoubXXq1NH48eP1wgsvaNmyZbr66qt14403avDgwc6CVUk5886Be/fulTFGDRo0KDD+9LsJHj58WJMnT9ZHH32U7xpQedfCOpu9e/cqOTk531lFeeLi4iQ5zt6SlC+nyMhI50XD/6309HRdf/31Sk1N1ddff+1yram84ltWVla+52VmZrrEBAUFFTilMC/29DjJ8Xqefnac1WrVLbfcoilTpjjPNivIuXI6vWAIAACKH0UpAABQ6gQEBMhqzX/C9+zZszV06FB9+OGHWr9+vcaOHauZM2fqu+++y1fEKk5nFi/sdrssFovWrl0rHx+ffPF5xZrc3Fx169ZNiYmJeuSRR9S4cWOFhIToyJEjGjp0qOx2+zn3bbfbFRUVpWXLlhW4PjIy8gKO6PzZbDb16dNHv/zyi9atW6dmzZq5rI+IiFBAQIBzytzp8tqqVq0qyTF1Ljc3V3FxcS7FNpvNpoSEBGdcRESEAgMDVb58+Xyvc97zTp48WWhRKm/aXmE55e0HAACUDIpSAACgTGnevLmaN2+uxx9/XN9++63at2+vhQsXOqdynT51raTUq1dPxhjVqVNHDRs2LDRu165d+vPPP/Xmm2/qjjvucLYXNH2xsLzr1aunjRs3qn379mc9s6dWrVqSHGdW1a1b19keHx+f7wyt82W323XHHXfo888/13vvvaeOHTvmi7FarWrevLm2b9+eb922bdtUt25dhYWFSZJatGghSdq+fbt69uzpjNu+fbvsdrtzvdVqVYsWLfTDDz/IZrO5TPnLu+7U2YpyzZo1k6+vr7Zv364BAwY42202m3bu3OnSBgAAih/XlAIAAGVCSkqKcnJyXNqaN28uq9XqMj0rJCRESUlJJZpLnz595OPjo2nTpuW7YLsxRgkJCZLkPLvn9BhjjObNm5dvmyEhIZKUL/cBAwYoNzdXTz31VL7n5OTkOOO7du0qPz8/zZ8/32V/c+fOLfAY9u/fr/3795/9QP9nzJgxevfdd7VgwQL16dOn0Lh+/frphx9+cClM7dmzR5s2bVL//v2dbddee60iIiL0yiuvuDz/lVdeUXBwsHr16uVsu+WWW5Sbm6s333zT2ZaZmally5apadOmLmc7/fHHHzp8+LDzcbly5dS1a1e9/fbbSk1Ndba/9dZbSktLc8kJAAAUP86UAgAAZcKmTZs0evRo9e/fXw0bNlROTo7eeust+fj4qG/fvs64li1bauPGjXrhhRdUtWpV1alTR23atCnWXOrVq6fp06fr0Ucf1cGDB9W7d2+FhYXpwIEDWr16tYYPH64JEyaocePGqlevniZMmKAjR44oPDxc77//foFnLrVs2VKSNHbsWMXExMjHx0cDBw5Ux44dNWLECM2cOVM7d+5U9+7d5efnp71792rlypWaN2+e+vXrp8jISE2YMEEzZ87U9ddfr549e2rHjh1au3atKlWqlG9/Xbp0kaRzXux87ty5WrBggdq2bavg4GC9/fbbLutvvvlmZ0Ft5MiReu2119SrVy9NmDBBfn5+euGFF1S5cmU9+OCDzucEBQXpqaee0qhRo9S/f3/FxMToq6++0ttvv62nn35aERERztgRI0Zo8eLFGjVqlP7880/VrFlTb731lg4dOqSPP/7YJZcmTZqoY8eO2rJli7Pt6aefVrt27dSxY0cNHz5cf//9t2bPnq3u3burR48eZz12AADwLxkAAAAPWbJkiZFkfvjhhwLXHzhwwEgyS5YscbYNGTLEhISE5Iv966+/zLBhw0y9evVMYGCgiYiIMJ07dzYbN250ifvjjz/MNddcY4KCgowkM2TIkCLlunLlSiPJbN682dk2ZcoUI8nEx8cX+Jz333/fdOjQwYSEhJiQkBDTuHFjM2rUKLNnzx5nzO+//266du1qQkNDTaVKlcw999xjfv7553zHnZOTY8aMGWMiIyONxWIxZ36Ne/XVV03Lli1NUFCQCQsLM82bNzcPP/ywOXr0qDMmNzfXTJs2zVSpUsUEBQWZTp06mV9//dXUqlUr3+tQq1YtU6tWrXO+LkOGDDGSCv05cOCAS/x///tf069fPxMeHm5CQ0PN9ddfb/bu3Vvgtl999VXTqFEj4+/vb+rVq2fmzJlj7HZ7vrjjx4+bIUOGmIiICBMQEGDatGljPvvss3xxkkzHjh3ztX/11VemXbt2JjAw0ERGRppRo0aZlJSUcx47AAD4dyzGnHFOOQAAAAAAAFDCuKYUAAAAAAAA3I6iFAAAAAAAANyOohQAAAAAAADcjqIUAAAAAAAA3I6iFAAAAAAAANzO19MJlAV2u11Hjx5VWFiYLBaLp9MBAAAAAADwGGOMUlNTVbVqVVmthZ8PRVGqGBw9elQ1atTwdBoAAAAAAABe47///a+qV69e6HqKUsUgLCxMkuPFDg8P93A2F85utys+Pl6RkZFnrWQC7kS/hDeiX8Jb0TfhdTIzZW6/XVnZ2fJfvlzW4GBPZwQ4MWbCG5WVfpmSkqIaNWo46yWFoShVDPKm7IWHh5f6olRmZqbCw8NLdedH2UK/hDeiX8Jb0TfhdYKDZZ8wQblJSQqPiJDV39/TGQFOjJnwRmWtX57rEkcUpQAAAACUDF9fqUsX2eLiHMsAAJym9JfdAAAAAAAAUOpQlAIAAABQMux26a+/5HPokGMZAIDTcA4tAAAAgJJhs8ly//0Ks9mkNWuYwgcAcMFvhYvcsZRMHUvJlCTZ7UaJJ9MVYUuW1eq4GFmV8EBVCQ/0ZIoAAAAorSwWKSJCJjPTsQwAwGkoSl3kFm09pCc3/Fno+sndGmpqTCM3ZgQAAIDSLif9mHLTYyVJ9mfH6WRioiok/y5rquPqIT4h0fINqeLJFAEAXoCi1EVuRNtauvGSypKk32NTdceKnfrPwBZqGh0mSZwlBQAAgPOW+strSto23aUt9rTl8m0eV4W2k92bFADA61CUusidPj3PbjeSpMZRobqienkPZgUAAIDSLOzSexRc7wZJUtaJ3UpYP1QVuy9VQKUmkhxnSgEAQFEKAAAAQLHyDanimJ5ns8n+xhtSoOQXVlcBUZd7OjUAgBexejoBAAAAAGWU3S7Lzz87lo3ds7kAALwORSkAAAAAJcPXV6ZvX8eyD5M0AACuKEoBAAAAKBm+vlKHDo5lHx/P5gIA8DoUpQAAAAAAAOB2pa4o9fLLL6t27doKDAxUmzZt9P333581fuXKlWrcuLECAwPVvHlzffrppy7rhw4dKovF4vLTo0ePkjwEAAAA4OJgjBQf/88yAACnKVVFqXfffVfjx4/XlClT9NNPP+myyy5TTEyM4uLiCoz/9ttvdeutt+quu+7Sjh071Lt3b/Xu3Vu//vqrS1yPHj107Ngx588777zjjsMBAAAAyrasLFlmzHAsZ9s8mwsAwOuUqqLUCy+8oHvuuUd33nmnmjZtqoULFyo4OFhvvPFGgfHz5s1Tjx499NBDD6lJkyZ66qmndMUVV+ill15yiQsICFB0dLTzp0KFCu44HAAAAKDsCwrydAYAAC9Vam6BYbPZ9OOPP+rRRx91tlmtVnXt2lVbt24t8Dlbt27V+PHjXdpiYmK0Zs0al7YtW7YoKipKFSpU0LXXXqvp06erYsWKheaSlZWlrKws5+OUlBRJkt1ul91eem91a/53m15jSvdxoGyx2+0yxtAn4VXol/BW9E14HX9/2adPl95rL+PrR9+EV2HMhDcqK/2yqPmXmqLUiRMnlJubq8qVK7u0V65cWX/88UeBz4mNjS0wPjY21vm4R48e6tOnj+rUqaP9+/dr0qRJuu6667R161b5FHKHkJkzZ2ratGn52uPj45WZmXm+h+Y1EhPT/vfvScUFZHs4G8DBbrcrOTlZxhhZraXq5E6UYfRLeCv6JrxRzsmTkqTEkyflay34shuAJzBmwhuVlX6ZmppapLhSU5QqKQMHDnQuN2/eXJdeeqnq1aunLVu2qEuXLgU+59FHH3U5AyslJUU1atRQZGSkwsPDSzznkhKR5ef4N6KCoqKYwgjvYLfbZbFYFBkZWaoHZZQt9Et4K/omvFGmvYKOS4qoUEGBUVGeTgdwYsyENyor/TIwMLBIcaWmKFWpUiX5+Pjo+PHjLu3Hjx9XdHR0gc+Jjo4+r3hJqlu3ripVqqR9+/YVWpQKCAhQQEBAvnar1VqqO43FYnX+W5qPA2WPxWIp9Z8vlH7HUjJ1LMVxNqzdbpR48pQislNltVokSVXCA1UlvGi/fIGSxJgJr5KdLcu770oWyWK30y/hdRgz4Y3KQr8sau6lpijl7++vli1b6vPPP1fv3r0lOSqIn3/+uUaPHl3gc9q2bavPP/9c999/v7Ntw4YNatu2baH7+fvvv5WQkKAqVaoUZ/oAgFJu0dZDenLDn4Wun9ytoabGNHJjRgBQCuTmyvL991IbSfZcT2cDAPAypaYoJUnjx4/XkCFD1KpVK7Vu3Vpz585Venq67rzzTknSHXfcoWrVqmnmzJmSpHHjxqljx46aPXu2evXqpRUrVmj79u169dVXJUlpaWmaNm2a+vbtq+joaO3fv18PP/yw6tevr5iYGI8dJwDA+4xoW0s3XuK4TuHvsam6Y8VO/WdgCzWNDpMkzpICgIL4+srccIMU953kU6r+9AAAuEGp+s1wyy23KD4+XpMnT1ZsbKxatGihzz77zHkx88OHD7ucItauXTstX75cjz/+uCZNmqQGDRpozZo1atasmSTJx8dHv/zyi958800lJSWpatWq6t69u5566qkCp+cBAC5ep0/Ps9uNJKlxVKiuqF7eg1kBgJfz9ZWuvVZaIamQmwgBAC5epaooJUmjR48udLreli1b8rX1799f/fv3LzA+KChI69atK870AAAAAAAAUASl96pZAAAAALybMVJy8j/LAACchqIUAAAAgJKRlSXL1KmO5WybR1MBAHgfilIAAAAASk4pvqU5AKBk8RsCAAAAQMkIDJSZPdux7M+NhAAArihKAQAAAAAAwO0oSgEAAAAAAMDtKEoBAAAAKBnZ2dKqlY7lnBzP5gIA8DoUpQAAAACUjNxcWb751rFsz/VsLgAAr0NRCgAAAEDJ8PWViYlxLPv4ejYXAIDXoSgFAAAAoGT4+ko9ejiWfXw8mwsAwOtQlAIAAAAAAIDbUZQCAAAAUDKMkTIy/lkGAOA0FKUAAAAAlIysLFkmTXIsZ9s8mwsAwOtQlAIAAAAAAIDbUZQCAAAAUDICAmSef96x7Ofv2VwAAF6HohQAAACAkmGx/HPXPYvFs7kAALwORSkAAAAAAAC4HUUpAAAAACUjJ0f68EPHcm6uZ3MBAHgdilIAAAAASkZOjixbtjiWc3M8mgoAwPtQlAIAAABQMnx9ZTp1ciz7+Ho0FQCA96EoBQAAAKBk+PpKN93kWM674DkAAP9DUQoAAAAAAABuR1EKAAAAQMkw5p8LnBvj2VwAAF6HohQAAACAkpGVJcuECY7lbJtncwEAeB2KUgAAAAAAAHA7ilIAAAAASkZAgMyMGY5lP3/P5gIA8DrclxUAAABAybBYpKCgf5YBAPnkpB9TbnqsJMlut8uemKgsRchqdZxH5BMSLd+QKp5MscRQlAIAAAAAAPCQ1F9eU9K26S5tsactl2/zuCq0nezepNyEohQAAACAkpGTI332mWM57y58AAAXYZfeo+B6N0iSsk7sVsL6oarYfakCKjWR5DhTqqyiKAUAAACgZOTkyLJundRGUm6Op7MBAK/kG1LFOT3PbrdLkvwiGikg6nJPpuUWXOgcAAAAQMnw8ZFp386xbPXxbC4AAK9DUQoAAABAyfDzk/r1dyz7MkkDAOCKohQAAAAAAADcjqIUAAAAgGJnzz6l1F/fUMKWcZKkhC3jlPrrG7Jnn/JwZgAAb8E5tAAAAACKlT37lE5sGKGM/Z/JkpgslZOyY39UYuKfyji8WZW6LZLVL9jTaQIAPIwzpQAAAAAUq/Q9K5RxcJ3sucnKDc9rzZXdlqSMQ+uUvmeFJ9MDAHgJilIAAAAAilXanytlz05zPLC4rrNnpyntz5XuTwoA4HVKXVHq5ZdfVu3atRUYGKg2bdro+++/P2v8ypUr1bhxYwUGBqp58+b69NNPXdYbYzR58mRVqVJFQUFB6tq1q/bu3VuSh+B1TtlytHjbIY1ZvUuSNGb1Li3edkinbDkezgwAAAClUW7aEcnkFrzSnutYDwC46JWqa0q9++67Gj9+vBYuXKg2bdpo7ty5iomJ0Z49exQVFZUv/ttvv9Wtt96qmTNn6vrrr9fy5cvVu3dv/fTTT2rWrJkk6dlnn9WLL76oN998U3Xq1NETTzyhmJgY/f777woMDDyv/Gw2m2w2W752q9Uq39NugVtQTB6LxSI/P78Lis3OzpYx5rxiT9lyNGLVL1r/5wmlZTueu/3vZO35+Hdt2ntCiwdcJj+LKXS7kuTv71+kHM6MzcnJkd1uL5ZYPz8/WSyWEo3Nzc1Vbm4hX67OM9bX11dWq9VrYu12u3JyCi9C+vj4yMfHxyOxdrtdNptNubm5znyNMcrOzi7Sds8Ve/rns6RipbN/lr15jPi3sVLZHCOycxzvvdE/x8IY4ZkxoqDYi3mMOPPzxRhRtFi+R/y72II+n7kBVWQs+2Qx+T+3xuKj3IAqBfZPxoj8sRLfIy4k9nzHiLNhjPh3sd7w3aC0jRGSlJOdXehntDSMEWd7rst2zNk+1V6mTZs2uvLKK/XSSy9JcnSsGjVqaMyYMZo4cWK++FtuuUXp6en65JNPnG1XXXWVWrRooYULF8oYo6pVq+rBBx/UhAkTJEnJycmqXLmyli5dqoEDBxaYR1ZWlrKyspyPU1JSVKNGDT3yyCMKCAjIF1+/fn0NGjTI+XjGjBmFdsZatWpp6NChzsfPPfecTp0q+A4lVapU0fDhw52P582bp6SkpAJjIyMjNXLkSOfjBQsWKD4+XjuOJGvjiQBl1u8gGSNZLM5/IwJ99cz1TZSz8zMdO3aswO0GBwfroYcecj5eunSpDh06VGCsn5+fJk2a5Hy8bNky7du3r8BYSZoyZYpz+b333tPu3bsLjX300Uedv1jWrFmjn3/+udDYCRMmKCQkRJL0f//3f9q+fXuhsePGjVP58uUlSevXr9fWrVsLjb3vvvucxdEtW7boiy++KDT27rvvVrVq1SRJ33zzjTZu3Fho7JAhQ1S7dm1J0vfff6+1a9cWGnvrrbeqYcOGkqSdO3fqww8/LDS2X79+uuSSSyRJv/32m1atWlVo7E033aQWLVpIkv7880+98847hcZed911at26tSTp4MGDevPNNwuN7dq1q9q3by9JOnLkiBYvXpwvxhij9PR09ezZU507d5YkxcXF6ZVXXil0u23btlX37t0lSUlJSZo3b16hsa1atVKvXr0kSenp6Xr++ecLjb3sssvUu3dvSY5BdubMmYXGNmnSRAMGDHA+njZtWqGx3jxGFKR8+fIaN26c8/Grr7560Y0RR3OD9HrmJdo4tLk6Na0piTHCU2NEno4dO6pTp06SLu4xombNmurZs6ciIyNltVoZI/6H7xG1Jbl3jMiK26lqWZ/r+kttzq+YeT75xV9HArooIKpFvu0yRjjwPcKhpMcIu92u+Ph4bdmyRX/88UehsYwRDnyPcCjpMSIz9kcdf6+9PkoeqkR7lQJjS8MYkZWVpVmzZik5OVnh4eEFbMGh1JwpZbPZ9OOPP+rRRx91tlmtVnXt2rXQD+/WrVs1fvx4l7aYmBitWbNGknTgwAHFxsaqa9euzvXlypVTmzZttHXr1kKLUjNnzizwTU9PTy+w+pqSkqK4uDjn47S0tEKrtKmpqfliMzIyihSbmpqq9PT0AmMDAwMLjD1yIkn2atfK5dvC//615ebo+z8OqsFZtmu324ucg6+vr0tsSkpKobGSzjs27xdFcnLyWWPj4+Od64sSm1fhTUpKOmvsiRMnnMtFic2rKp88efKssQkJCQoODi5ybN7rlpiYeNbYxMTEC4pNSEg4a+zJkycvKPbEiRMFxhpjlJmZWaTYPElJSc7Yc73HycnJzthTp04VOdZms5019szP/fnEetMYURAfH58ix5bVMSLDSLLk9WHHWbWMEZ4ZI/Kc/rm/mMeIlJQUJSUlyRgjq9XKGHEesXyPKN4xwgTV0Z7ky5Tzy1/qVTNRfuWNci1B+ttWQ3syyymgfB3lFLB9xggHvkc4lPQYYbfblZycfM73mTHin1i+R5T8GJFz8qQkKSMjQ+k5BceXhjHi9BN5zuaCzpTy8fHRsWPH8k2ZS0hIUFRU1FlP47tQR48e1f+zd+9xMpb/H8ffM7s7ez5Y1u46S44RIlrqh1LENznEN3RQKCGVdFJRKjrr8E0q2VSkfCvfDiiJRBJlRTlHcljrtEd2Z3fn+v2x7WTswVq7M7O7r+fjMQ/3XPdn7vnc65pr7/nsfV937dq19cMPPyguLs7Zfv/99+u7777T2rVrC7zGZrNpzpw5Gjx4sLNtxowZevzxx3Xo0CH98MMP6ty5sw4cOKDY2H8qkIMGDZLFYtGHH35YaC5FnSl16NChQiuA3nxKbbsXv9PWY3YV9iqLpBY1g7X+rs5ecUotp91XzVNq8/+CFRMT4+zD3nBKLafde99p9+4cI37Zl6xLZ67Tj2Pj1L5epCTGCE67944xwhij5ORk55lSjBEli+U44txii/wdnn1CGb/Pl3nneWU03SffGhcrpNUt8j+vv6x+QYVulzGiYKzEcURpYksyRuQfZ1arVq3IOIkx4lxjveHYoCKNEflnSkX2XyH/mhedcbuSd44Rqampio6OLp8zpYoaCLKyslwGgsrK39+/0Mv0AgICSjQP1dnMVXU2sYXldKbY2tXDtCP5qHIcBf9PfawWxYYHlmq7JXE2fcUbYq1Wq8uHrrLFnjpQeVOsw+GQv7+//Pz8nL/YJDl/EZSEN8SW1+e+vMcIT8Z6w+f+9NgT9hzN27Bfb/+Yd+nAXf/7XcMvqa8hbWsryObnFZ/lqjZGFMUbPveeGCMcDocsFousVqusVitjhBfFestn2a1jRECAgjqO1sn6HZUxP041ur+iwJh2Jdpmsds9x1jJOz73HEeUb2xJP58Wi0U2m83lOLMstnu2sd7yWeY4Io8nP/eWv/uizeZf4s+oN44RJZ1T6qyKUq+88oqkvA/urFmzFBIS4lyXm5urlStXqlmzZmezyRKrUaOGfHx8dOjQIZf2Q4cOKSYmptDXxMTEFBuf/++hQ4dczpQ6dOiQ85rWym5Qm1pa/1eykjMLVo1DbT4a1KaWB7ICAO9zwp6jER9t1JKtSUrLyhszT78xRJCtwlwVDwAAAHjcWR09T58+XVLemVIzZ850qfLZbDY1aNBAM2fOLNsMT9l+u3bttGzZMucEgQ6HQ8uWLdPYsWMLfU1cXJyWLVumu+++29m2dOlS5+V/DRs2VExMjJYtW+YsQqWmpmrt2rW64447ymU/vM2QtrX17Y4jWrI1Sen2HOU4JF+rFGrzVY9mNTWkbW1PpwgAXmHehv1asjXJpYifa6TkzBwt2ZqkeRv2a0TH+h7MEAAAAKhYzqootXv3bklSt27d9Mknn5zx2tuyNn78eN18881q3769OnTooJdeekkZGRm65ZZbJEk33XSTateu7byTxV133aUuXbrohRdeUO/evTV//nytX79eb775pqS8M77uvvtuPfnkk2rcuLEaNmyoRx99VLVq1XIWviq7IJuvZg1q7bwcZe1fKWpXO/yUy1H4qz8ASNJHCQeUbi987oR0e64+SjhAUQoATpeZKcvEidKFkuwlm/QWAFB1lKrisHz58rLOo0T+/e9/6/Dhw5o0aZISExPVpk0bLVmyRNHR0ZKkvXv3ulwL3KlTJ82bN0+PPPKIJk6cqMaNG2vhwoVq2bKlM+b+++9XRkaGbrvtNiUnJ+vSSy/VkiVLzurayYouyOarER3rq01smDq8skqv9mul9vXcW3AEAG+3PyVTuYXMvydJuQ6j/SmZbs4IACqIIu7cBABAqYpSAwYMUIcOHfTAAw+4tD/77LNat26dFixYUCbJFWbs2LFFXq63YsWKAm0DBw7UwIEDi9yexWLRlClTNGXKlLJKEQBQCdUOD9COIxlF3hiidnjV+WMGAJSYv7/MxInSsj6SX+W/IRIA4OyU7BYDp1m5cqV69epVoP3qq6/WypUrzzkpAAC8zaA2tRRiK/yOKdwYAgCKYLFIUVH/LAMAcIpSFaXS09MLvb2ln5+fUlNTzzkpAAC8zZC2tdWzWU1FBPjK9+/fnr5WqVoAN4YAAAAASqNURalWrVrpww8/LNA+f/58tWjR4pyTAgDA2+TfGOLZa1qoXe1wSVK72uF65poWmjWoNTeGAIDC5ORIq1blLecWfrMIAEDVVaoj6EcffVT9+/fXrl27dPnll0uSli1bpg8++KBc55MCAMCTuDEEAJylnBxZPv5Y6igpN8fT2QAAvEypilLXXHONFi5cqKlTp+q///2vAgMDdeGFF+qbb75Rly5dyjpHAAAAABWR1SrTurWkHyVLqS7SAABUYqW+1qB3797q3bt3WeYCAAAAoDKx2aRhw6T5b0h+fp7OBgDgZUr954rk5GTNmjVLEydO1LFjxyRJv/zyi/bv319myQEAAAAAAKByKtWZUr/++qu6d++u8PBw7dmzRyNGjFBkZKQ++eQT7d27V++++25Z5wkAAAAAAIBKpFRnSo0fP17Dhg3Tjh07FBAQ4Gzv1auXVq5cWWbJAQAAAKjAsrJkmTw5b9lu92wuAACvU6qi1Lp163T77bcXaK9du7YSExPPOSkAAAAAlYAxUmpq/hOPpgIA8D6lKkr5+/sr1fnL5R/bt29XVFTUOScFAAAAoBKw2WQmTMhb9mWicwCAq1IVpfr06aMpU6YoOztbkmSxWLR371498MADGjBgQJkmCAAAAKCCslql2rX/WQYA4BSl+s3wwgsvKD09XTVr1tTJkyfVpUsXnX/++QoNDdVTTz1V1jkCAAAAAACgkinV3ffCw8O1dOlSrV69Whs3blR6erouuugide/evazzAwAAAFBR5eRIP/2Ut5yb69lcAABep8RFqcjISG3fvl01atTQrbfeqpdfflmdO3dW586dyzM/AAAAABVVTo4sH3wgdZSUm+PpbAAAXqbEl+/Z7Xbn5OZz5sxRZmZmuSUFAAAAoBKwWmVatMhbtjCnFADAVYnPlIqLi1Pfvn3Vrl07GWM0btw4BQYGFho7e/bsMksQAAAAQAVls0kjR0rzZ0t+3H0PAOCqxEWp999/X9OnT9euXbskSSkpKZwtBQAAAAAAgFIpcVEqOjpaTz/9tCSpYcOGeu+991S9evVySwwAAAAAAACVV4kv7I6MjNSRI0ckSd26dZPNZiu3pAAAAABUAllZsjz1VN6y3e7ZXAAAXoeJzgEAAACUD2Okv/+wLRmPpgIA8D5MdA4AAACgfNhsMuPGSWt/lHyZ6BwA4KpUE51bLBYmOgcAAABQPKtVathQWvv3MgAAp2CicwAAAAAAALjdWf25olevXkpJSdHu3btVvXp1Pf3000pOTnauP3r0qFq0aFHWOQIAAACoiHJzpYSEvGVHrkdTAQB4n7MqSi1ZskRZWVnO51OnTtWxY8ecz3NycrRt27ayyw4AAABAxZWdLcucOXnLOTmezQUA4HXO6cJuY7iDBgAAAIAiWK1So0Z5yxbmlAIAuOI3AwAAAIDyYbPJjB2bt+zH3fcAAK7OqihlsVhksVgKtAEAAAAAAKB0HNknlLZ5to6uuEuSdHTFXUrbPFuO7BMezqx8lfjue1Le5XrDhg2Tv7+/JCkzM1OjRo1ScHCwJLnMNwUAAAAAAIDiObJP6MjS23Vyz1dyZKdJkrIP/axjx7br5N7lqnHlG7L6BXk4y/JxVkWpm2++2eX5DTfcUCDmpptuOreMAAAAAFQOdrsszz0n1ZWUne3pbADAK2Vsm59XkLIn/9NocuWwJ+vkn18pY9t8hba81WP5laezKkrFx8eXVx4AAAAAKhuHQzpwIK8oZRyezgYAvFL69gVyZKcXus6Rna707QsqbVGKic4BAAAAlA+bTWbUqLxlXyY6B4DC5Kbvl0xu4SsduXnrKymKUgAAAADKh9UqNW36zzIAoACfkNqSxafwlVafvPWVFL8ZAAAAAAAAPCSkyUBZ/UIKXWf1C1FIk4Fuzsh9KEoBAAAAKB+5udJvv+UtO4q4NAUAqrjgptcrsEEPWf0jJMvfU39bfGX1j1Bg/R4Kbnq9R/MrTxSlAAAAAJSP7GxZZs3KW87J8WwuAOClrH5BqnHlG4q87Gn5RV8kSfKLvkiRlz2tGle+IatfkIczLD8Vpih17NgxDR06VGFhYYqIiNDw4cOVnl747PT5MjMzNWbMGFWvXl0hISEaMGCADh065BJjsVgKPObPn1+euwIAAABUDVarVK9e3rKlwnz1AAC3s/oFKbTlrare9WVJUvWuLyu05a2VuiAlVaCi1NChQ/Xbb79p6dKl+uKLL7Ry5Urddtttxb7mnnvu0eeff64FCxbou+++04EDB9S/f/8CcfHx8Tp48KDz0bdv33LaCwAAAKAKsdlk7rknb9mPu+8BAFz5ejqBktiyZYuWLFmidevWqX379pKkV199Vb169dLzzz+vWrVqFXhNSkqK3n77bc2bN0+XX365pLziU/PmzfXjjz/qkksuccZGREQoJiamxPlkZWUpKyvL+Tw1NVWS5HA45HA4SrWP3sAYh/PfirwfqFwcDoeMMfRJeBXGS3grxkx4I/N3fzQV/FgZlQ9jJrxRZRkzS5p7hShKrVmzRhEREc6ClCR1795dVqtVa9euVb9+/Qq85ueff1Z2dra6d+/ubGvWrJnq1aunNWvWuBSlxowZoxEjRui8887TqFGjdMstt8hisRSZz7Rp0/T4448XaD98+LAyMzNLu5sed+xY+t//HleSf7aHswHyOBwOpaSkyBgjK7eShpdgvIS3YsyEN8o5flySdOz4cflakzycDfAPxkx4o8oyZqalpZUorkIUpRITE1WzZk2XNl9fX0VGRioxMbHI19hsNkVERLi0R0dHu7xmypQpuvzyyxUUFKSvv/5ao0ePVnp6usaNG1dkPg899JDGjx/vfJ6amqq6desqKipKYWFhpdhD7xCZlXdKdWRkNdWsWc3D2QB5HA6HLBaLoqKiOFiA12C8hLdizITXsduV9fL7SoyWIkNDFHDaMT3gSYyZ8EaZjmo6JCmyWrUKPWYGBASUKM6jRakHH3xQzzzzTLExW7ZsKdccHn30Uedy27ZtlZGRoeeee67YopS/v7/8/f0LtFut1go9mFn+nnzSYqnY+4HKx2KxVPjPFyoXxkt4M8ZMeBvLnj1StGSR6JfwOoyZ8DaWv/uipYL3y5Lm7tGi1L333qthw4YVG3PeeecpJiZGSUmup63l5OTo2LFjRc4FFRMTI7vdruTkZJezpQ4dOlTs/FEdO3bUE088oaysrEILTwAAAABKyM9P5tZbpd9+lHwrxEUaAAA38uhvhqioKEVFRZ0xLi4uTsnJyfr555/Vrl07SdK3334rh8Ohjh07Fvqadu3ayc/PT8uWLdOAAQMkSdu2bdPevXsVFxdX5HslJCSoWrVqFKQAAACAUsrJOKjcjLwpM7Jr26TfpOzknbL65l3+7BMcI9/gWE+mCADwAhXizxXNmzdXz549NXLkSM2cOVPZ2dkaO3asrr/+eued9/bv368rrrhC7777rjp06KDw8HANHz5c48ePV2RkpMLCwnTnnXcqLi7OOcn5559/rkOHDumSSy5RQECAli5dqqlTp2rChAme3F0AAACgQkv79S0lr33Spe3o18OcyxEdH1G1uEluzgoA4G0qRFFKkubOnauxY8fqiiuukNVq1YABA/TKK68412dnZ2vbtm06ceKEs2369OnO2KysLPXo0UMzZsxwrvfz89Nrr72me+65R8YYnX/++XrxxRc1cuRIt+4bAMD7HUzN1MHUvDusbk1Kd/5rtebdrTU2LECxYSWb0BEAKrvQC0cqqNE1ksMhx86dSk1JUVjbtrL+fQmfT3DR02kAAKqOClOUioyM1Lx584pc36BBAxljXNoCAgL02muv6bXXXiv0NT179lTPnj3LNE8AQOX0xpo/NWXpdpe2m+YnOJcnXdlEj/Vo6uasAMA7+QbH5l2el5kp8/RkWex22RbeIGtQkKdTAwB4kQpTlAIAwJNuj6uvPhdES5IcDqNjx48pslqky5lSAIDTWCxS3bpynDyZtwwAwCkoSgEAUAKnXp7ncDiUZMtSzZrhFfpWvQBQ7vz9ZV57TalJSQrgRkIAgNNwJA0AAAAAAAC3oygFAAAAAAAAt+PyvSqOu0kBAACg3NjtskyZopCMDGnaNCm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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dml_obj_linear_logistic = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", " ml_g=LinearRegression(),\n", " ml_m=LogisticRegression(penalty=None),\n", " control_group=\"never_treated\",\n", " n_folds=10\n", ")\n", "\n", "dml_obj_linear_logistic.fit()\n", "dml_obj_linear_logistic.bootstrap(n_rep_boot=5000)\n", "dml_obj_linear_logistic.plot_effects(title=\"Estimated ATTs by Group, Linear and logistic Regression\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We briefly look at the model summary, which includes some standard diagnostics for the prediction of the nuisance components." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLDIDMulti Object ==================\n", "\n", "------------------ Data summary ------------------\n", "Outcome variable: lemp\n", "Treatment variable(s): ['first.treat']\n", "Covariates: ['lpop']\n", "Instrument variable(s): None\n", "Time variable: year\n", "Id variable: countyreal\n", "No. Observations: 500\n", "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", "Control group: never_treated\n", "Anticipation periods: 0\n", "\n", "------------------ Machine learner ------------------\n", "Learner ml_g: LinearRegression()\n", "Learner ml_m: LogisticRegression(penalty=None)\n", "Out-of-sample Performance:\n", "Regression:\n", "Learner ml_g0 RMSE: [[0.17197022 0.18219482 0.25977582 0.25762107 0.1726177 0.15159716\n", " 0.20238368 0.20650302 0.17381994 0.15150495 0.20118645 0.16352671]]\n", "Learner ml_g1 RMSE: [[0.10293317 0.12500233 0.13673155 0.1356723 0.13881924 0.1126528\n", " 0.08473008 0.10271377 0.13282935 0.16430589 0.15938565 0.1613265 ]]\n", "Classification:\n", "Learner ml_m Log Loss: [[0.23186483 0.23061763 0.23153821 0.23146489 0.34981033 0.34925181\n", " 0.35002383 0.34858068 0.60836257 0.60569912 0.60630968 0.60700137]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 10\n", "No. repeated sample splits: 1\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", "ATT(2004.0,2003,2004) -0.013506 0.022282 -0.606161 0.544408 -0.057178 \n", "ATT(2004.0,2003,2005) -0.077125 0.028778 -2.680033 0.007361 -0.133528 \n", "ATT(2004.0,2003,2006) -0.132188 0.035710 -3.701704 0.000214 -0.202178 \n", "ATT(2004.0,2003,2007) -0.104989 0.033066 -3.175173 0.001497 -0.169797 \n", "ATT(2006.0,2003,2004) -0.000609 0.022310 -0.027312 0.978211 -0.044336 \n", "ATT(2006.0,2004,2005) -0.005410 0.018285 -0.295867 0.767332 -0.041248 \n", "ATT(2006.0,2005,2006) 0.003109 0.020571 0.151158 0.879851 -0.037209 \n", "ATT(2006.0,2005,2007) -0.041588 0.019824 -2.097880 0.035916 -0.080442 \n", "ATT(2007.0,2003,2004) 0.027959 0.014092 1.983968 0.047259 0.000338 \n", "ATT(2007.0,2004,2005) -0.004452 0.015648 -0.284504 0.776024 -0.035121 \n", "ATT(2007.0,2005,2006) -0.028741 0.018211 -1.578214 0.114517 -0.064434 \n", "ATT(2007.0,2006,2007) -0.027488 0.016248 -1.691824 0.090679 -0.059333 \n", "\n", " 97.5 % \n", "ATT(2004.0,2003,2004) 0.030165 \n", "ATT(2004.0,2003,2005) -0.020722 \n", "ATT(2004.0,2003,2006) -0.062197 \n", "ATT(2004.0,2003,2007) -0.040182 \n", "ATT(2006.0,2003,2004) 0.043118 \n", "ATT(2006.0,2004,2005) 0.030428 \n", "ATT(2006.0,2005,2006) 0.043428 \n", "ATT(2006.0,2005,2007) -0.002734 \n", "ATT(2007.0,2003,2004) 0.055580 \n", "ATT(2007.0,2004,2005) 0.026217 \n", "ATT(2007.0,2005,2006) 0.006952 \n", "ATT(2007.0,2006,2007) 0.004357 \n" ] } ], "source": [ "print(dml_obj_linear_logistic)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "es_linear_logistic = dml_obj_linear_logistic.aggregate(\"eventstudy\")\n", "es_linear_logistic.plot_effects(title=\"Estimated ATTs by Group, Linear and logistic Regression\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\bachp\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\matplotlib\\cbook.py:1762: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n", " return math.isfinite(val)\n" ] }, { "data": { "text/plain": [ "(
,\n", " [,\n", " ,\n", " ])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dml_obj_lasso = DoubleMLDIDMulti(\n", " obj_dml_data=dml_data,\n", " ml_g=LassoCV(),\n", " ml_m=LogisticRegressionCV(),\n", " control_group=\"never_treated\",\n", " n_folds=10\n", ")\n", "\n", "dml_obj_lasso.fit()\n", "dml_obj_lasso.bootstrap(n_rep_boot=5000)\n", "dml_obj_lasso.plot_effects(title=\"Estimated ATTs by Group, LassoCV and LogisticRegressionCV()\")\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLDIDMulti Object ==================\n", "\n", "------------------ Data summary ------------------\n", "Outcome variable: lemp\n", "Treatment variable(s): ['first.treat']\n", "Covariates: ['lpop']\n", "Instrument variable(s): None\n", "Time variable: year\n", "Id variable: countyreal\n", "No. Observations: 500\n", "\n", "------------------ Score & algorithm ------------------\n", "Score function: observational\n", "Control group: never_treated\n", "Anticipation periods: 0\n", "\n", "------------------ Machine learner ------------------\n", "Learner ml_g: LassoCV()\n", "Learner ml_m: LogisticRegressionCV()\n", "Out-of-sample Performance:\n", "Regression:\n", "Learner ml_g0 RMSE: [[0.17242002 0.18142511 0.25935844 0.25958681 0.17338302 0.15220244\n", " 0.20145569 0.20566302 0.17235633 0.15194071 0.20103747 0.16455826]]\n", "Learner ml_g1 RMSE: [[0.09911511 0.13069581 0.13900982 0.15099534 0.13918785 0.11262764\n", " 0.08873154 0.1102721 0.131316 0.16060588 0.15919193 0.16009149]]\n", "Classification:\n", "Learner ml_m Log Loss: [[0.22914236 0.22913907 0.22913769 0.22913938 0.35596113 0.35595921\n", " 0.35596421 0.35595231 0.60886635 0.60885348 0.60885687 0.60885362]]\n", "\n", "------------------ Resampling ------------------\n", "No. folds: 10\n", "No. repeated sample splits: 1\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % \\\n", "ATT(2004.0,2003,2004) -0.016264 0.023168 -0.702029 0.482661 -0.061672 \n", "ATT(2004.0,2003,2005) -0.077256 0.028982 -2.665626 0.007685 -0.134060 \n", "ATT(2004.0,2003,2006) -0.136480 0.035719 -3.820990 0.000133 -0.206487 \n", "ATT(2004.0,2003,2007) -0.104836 0.033835 -3.098432 0.001945 -0.171152 \n", "ATT(2006.0,2003,2004) -0.001649 0.023339 -0.070667 0.943663 -0.047394 \n", "ATT(2006.0,2004,2005) -0.005104 0.019313 -0.264274 0.791569 -0.042956 \n", "ATT(2006.0,2005,2006) -0.003129 0.017887 -0.174943 0.861124 -0.038187 \n", "ATT(2006.0,2005,2007) -0.041235 0.020320 -2.029240 0.042434 -0.081062 \n", "ATT(2007.0,2003,2004) 0.028086 0.015158 1.852874 0.063900 -0.001623 \n", "ATT(2007.0,2004,2005) -0.004803 0.016425 -0.292438 0.769951 -0.036996 \n", "ATT(2007.0,2005,2006) -0.030093 0.017879 -1.683182 0.092340 -0.065134 \n", "ATT(2007.0,2006,2007) -0.028444 0.016791 -1.693985 0.090268 -0.061354 \n", "\n", " 97.5 % \n", "ATT(2004.0,2003,2004) 0.029143 \n", "ATT(2004.0,2003,2005) -0.020452 \n", "ATT(2004.0,2003,2006) -0.066473 \n", "ATT(2004.0,2003,2007) -0.038520 \n", "ATT(2006.0,2003,2004) 0.044095 \n", "ATT(2006.0,2004,2005) 0.032748 \n", "ATT(2006.0,2005,2006) 0.031928 \n", "ATT(2006.0,2005,2007) -0.001408 \n", "ATT(2007.0,2003,2004) 0.057795 \n", "ATT(2007.0,2004,2005) 0.027389 \n", "ATT(2007.0,2005,2006) 0.004948 \n", "ATT(2007.0,2006,2007) 0.004466 \n" ] } ], "source": [ "# Model summary\n", "print(dml_obj_lasso)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\bachp\\Documents\\Promotion\\DissundPapers\\Software\\DoubleML\\doubleml-for-py\\doubleml\\did\\did_aggregation.py:368: UserWarning: Joint confidence intervals require bootstrapping which hasn't been performed yet. Automatically applying '.aggregated_frameworks.bootstrap(method=\"normal\", n_rep_boot=500)' with default values. For different bootstrap settings, call bootstrap() explicitly before plotting.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "es_rf = dml_obj_lasso.aggregate(\"eventstudy\")\n", "es_rf.plot_effects(title=\"Estimated ATTs by Group, LassoCV and LogisticRegressionCV()\")" ] } ], "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": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 2 }