Chapter V - Differences-in-Differences.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mastring Metrics\n",
    "## Chapter 5\n",
    "### Differences-in-Differences\n",
    "Analysis of the effect of monetary policy on bank failures in Mississippi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of number of banks in buisness and operation in each day of the years 1929 through 1934 in Mississippi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>bib6</th>\n",
       "      <th>bio6</th>\n",
       "      <th>bib8</th>\n",
       "      <th>bio8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10775</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10776</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10777</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10778</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10779</td>\n",
       "      <td>Friday</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    date    weekday  day  month  year  bib6  bio6  bib8  bio8\n",
       "0  10775     Monday    1      7  1929   141   141   169   169\n",
       "1  10776    Tuesday    2      7  1929   141   141   169   169\n",
       "2  10777  Wednesday    3      7  1929   141   141   169   169\n",
       "3  10778   Thursday    4      7  1929   141   141   169   169\n",
       "4  10779     Friday    5      7  1929   141   141   169   169"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banks = pd.read_csv('data/banks.csv')\n",
    "banks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y6_1930 = int(banks[(banks['year'] == 1930) & (banks['month']==7) & (banks['day']==1)]['bib6'].to_string(index=False))\n",
    "Y6_1930 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y8_1930 = int(banks[(banks['year'] == 1930) & (banks['month']==7) & (banks['day']==1)]['bib8'].to_string(index=False))\n",
    "Y8_1930 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y6_1931 = int(banks[(banks['year'] == 1931) & (banks['month']==7) & (banks['day']==1)]['bib6'].to_string(index=False))\n",
    "Y6_1931 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y8_1931 = int(banks[(banks['year'] == 1931) & (banks['month']==7) & (banks['day']==1)]['bib8'].to_string(index=False))\n",
    "Y8_1931 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_DD = (Y6_1931-Y6_1930) - (Y8_1931 - Y8_1930)\n",
    "delta_DD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_DD2 = (Y6_1931-Y8_1931) - (Y6_1930 - Y8_1930)\n",
    "delta_DD2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting figure 5.1 in the textbook, based on the above values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7eb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mticker\n",
    "\n",
    "X = [1930, 1931]\n",
    "Y6 = [Y6_1930, Y6_1931]\n",
    "Y8 = [Y8_1930, Y8_1931]\n",
    "CF = [Y6_1930, Y6_1931 - delta_DD]\n",
    "plt.plot(X, Y6, X, Y8, X, CF, 'g--')\n",
    "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Banks in Business\")\n",
    "plt.legend(('Sixth Disctrict', 'Eighth District', 'Sixth Discrict Counterfactual'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting the related data for Figure 5.2 in the text book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1929, 1930, 1931, 1932, 1933, 1934]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Years = list(banks['year'].unique())\n",
    "Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[141, 135, 121, 113, 102, 102]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y6=[]\n",
    "for year in Years:\n",
    "    Y6.append(int(banks[(banks['year'] == year) & (banks['month']==7) & (banks['day']==1)]['bib6'].to_string(index=False)))\n",
    "Y6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[169, 165, 132, 120, 111, 109]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y8=[]\n",
    "for year in Years:\n",
    "    Y8.append(int(banks[(banks['year'] == year) & (banks['month']==7) & (banks['day']==1)]['bib8'].to_string(index=False)))\n",
    "Y8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting figure 5.2 in the textbook, based on the above values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb999400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Years, Y6, Years, Y8)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Banks in Business\")\n",
    "plt.legend(('Sixth Disctrict', 'Eighth District'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the counterfactuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[135, 102, 90, 81, 79]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CF = []\n",
    "CF_Year = []\n",
    "DID = []\n",
    "for i in range(len(Years)):\n",
    "    if Years[i] >= 1930:\n",
    "        CF_Year.append(Years[i])\n",
    "        delta = (Y6[i]-Y6_1930)-(Y8[i]-Y8_1930)\n",
    "        CF.append(Y6[i]-delta)\n",
    "        DID.append(delta)\n",
    "#     elif Years[i] = 1930:\n",
    "#         CF_Year.append(Years[i])\n",
    "#         CF.append(Y6[i])\n",
    "CF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting figure 5.3 in the textbook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9d8860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Years, Y6, Years, Y8, CF_Year, CF, 'g--')\n",
    "# plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of Banks in Business\")\n",
    "plt.legend(('Sixth Disctrict', 'Eighth District', 'Sixth Disctrict Counterfactual'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dashed line depicts the counterfactual evolution of the number of banks in the sixth district if the same number of banks had failed in that district after 1930 as did in the eighth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting the difference-in-difference estimate for all years after 1930."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sMWrWrEl8fHy+7yLuLVYwTK6sXLmSL774gokTJ1KkSBGn4/jdgAEDCA0NpV+/fiQnJzN37lxCQkKcjmVyYcGCBbRv3546deqwatUqSpcu7XSkPEMymL/o/zcQKYJrsqOHgIrAOVxzUyxT1Z0+T5gNYWFhmpSU5HSMAkNVufvuuzlx4gR79+4t0D8ox40bR+/evWnVqhXz5s0jNDTU6UgmB+bOnUvHjh2pV68eK1as4KqrrnI6ks+JyGZVDfNk20xbGCIyCGgNJOKaj/soUAS4GRjmLiZ9VXV7bgKb/GnRokVs3rz5P88oFGQvvfQSISEh9OzZk7Zt27Jw4UKKFi3qdCyTDe+//z7PPvssDz74IEuXLg3Y3n65kWkLQ0QeVtVlmXxeHqiqqnni13prYfhPamoqd955JxcuXGDnzp12w9dt+vTpdOvWjYYNG7J48WKKFy/udCTjgWnTpvHcc88VyO8tOy2MTDuQp1csRKSQiJRyf340o2IhIlVEZJ2I7BaRnSLS273+TRHZIyLbRWShiKT7SLCINBeRvSKyT0Ty1xjZBcC8efPYsWMH0dHRVizS6NKlC++//z6JiYm0aNGC06dPOx3JZOHdd9+lW7duNGvWjKVLlxaoYpFtqprlC5gDlAKKA3uAw0C/LPapANR1L5cEvgNq4pqfu7B7/XBgeDr7BgE/ANcDIcA3QM2sct51111qfC8lJUVvvfVWrVWrlqakpDgdJ0+aO3euBgUF6X333aenTp1yOo7JwJgxYxTQ1q1b6/nz552O4wggST2oA6rq8Wi1NVX1D+CvuKZYrYprvu7MCtFhVd3iXj4N7AYqqepqVU1xb/YvIL3HJusB+1T1R1VNBuYCgTWbej42Z84c9uzZQ0xMjD2wloH27dvzySefkJSUROPGjfntt9+cjmSuMGLECPr06cMjjzxiHRU85GnBCBaRYFwFY5GqXszOSUSkOlAH143ztLoAK9LZpRLwS5r3B93rjMMuXrxITEwMderUoV27dk7HydPatWvHggUL2L59OxERERw/ftzpSMZt8ODB9O/fnyeffNK6QmeDpwVjEvATrktSG0SkGvC7JzuKSAlgPtDH3Uq5vH4AkALMTm+3dNale3deRLqLSJKIJB07dsyTSCYXZs6cyQ8//EBsbKyNoeSBVq1asXjxYvbs2UPDhg05cuSI05EKNFUlKiqKyMhIOnXqxAcffGAPW2aHJ9etgNAr3gtQ1oP9goFVwD+uWP808CVQLIP97gNWpXn/OvB6Vuezexi+df78ea1atarec889mpqa6nScfGXt2rVarFgxvfXWW/XQoUNOxymQUlNT9dVXX1VAu3btavff3PDBPYwFIpK2K8x1wOrMdhDXuM/TgN2qOirN+uZAf6CNqp7NYPdNwE0iUkNEQoAngcUeZjU+MnXqVA4cOEBcXJwN651NERERrFy5koMHDxIeHs4vv/yS9U7Ga1T1P1Pt9uzZk8mTJ9v9t5zwpKoAzwGf4uq9VB3YDjTNYp8HcV1G2g5sc79aAvtw3Z+4vG6ie/uKwPI0+7fE1bPqB2CAJzmtheE7Z8+e1QoVKuhDDz1krYtc+OKLL7RUqVJao0YN3b9/v9NxCoRLly5pz549FdDevXvbv98rkI0WhkcbuY5JL2AJsAO439P9/PmyguE7o0aNUkDXr1/vdJR87+uvv9arr75aq1atqvv27XM6TkC7dOmSdu3aVQF99dVXrVikIzsFI6snvdNOzCy4utLuALa6Wyej0tvPKfakt2+cOXOG66+/ntq1axMfH+90nICwbds2GjduXGDnhvaHS5cu/edBysjISGJiYuxSajq89qQ3rgfuLr9KAAtxXVK6vM4UAOPHj+fYsWPExcU5HSVg3HnnnSQmJpKSkkJ4eDg7d+apcTzzvYsXL9KxY0fef/99YmNjiY2NtWLhBVmOVpufWAvD+37//Xdq1KjB/fffz9KlS52OE3D27NlDREQEFy9eZM2aNdSuXdvpSPlecnIyHTp0YMGCBQwfPpxXX33V6Uh5mjdbGJcPeLOITBaR1SKScPmVu5gmPxgzZgy//fYbsbGxTkcJSLfeeivr16+nSJEiNGzYkM2bNzsdKV+7cOECjz32GAsWLGD06NFWLLzMoxaGiHwDTAQ2A5cur1fVPPWv21oY3nXy5Elq1KhB48aNmT9/vtNxAtr+/ftp2LAhp06dYtWqVdxzzz1OR8p3zp07xyOPPMLKlSt555136Nmzp9OR8gWvtzCAFFV9V1W/VtXNl1+5yGjygZEjR3L69GliYmKcjhLwatSowYYNG7jmmmto0qQJGzdudDpSvvLnn3/SunVrVq1axdSpU61Y+IinBWOJiPxdRCqISJnLL58mM446evQo48aNo3379tx+++1OxykQqlatyvr166lYsSLNmzcnMTHR6Uj5wunTp2nZsiXr1q1j5syZdO3a1elIAcvTgvE00A/4Atdlqc2AXfsJYMOHD+fcuXMMGjTI6SgFSqVKlUhMTKRatWq0bNnSujFn4ffff6dZs2Z8/vnnzJ49m06dMh1E2+SSRwVDVWuk87re1+GMM3799VfeeecdOnXqZM8HOOC6664jMTGRm266idatW7N8+XKnI+VJv/32G02aNGHTpk189NFHPPnkk05HCngeDzcqIreLyBMi0vnyy5fBjHOGDh1KSkoKUVFRTkcpsMqVK0dCQgK1atXir3/9K4sWLXI6Up5y/PhxGjVqxDfffMOCBQt49NFHnY5UIHjarTYaeNv9agiMANr4MJdxyIEDB5g8eTJdunTh+uutEemksmXLsnbtWurWrctjjz3GJ5984nSkPOHo0aNERESwa9cuFi1aROvWrZ2OVGB42sJ4DGgE/FtVnwVqAzY9VQAaPHgwAAMHDnQ4iQG4+uqrWb16Nffccw9PPvkkc+bMcTqSow4fPkyDBg3Yt28fy5Yto3nz5k5HKlA8LRjnVDUVSBGRUsBRXPNtmwDyww8/MH36dJ5//nmqVKnidBzjVqpUKVauXEn9+vXp2LEjM2fOdDqSIy4PDX/gwAFWrFhBo0aNnI5U4HhaMJJE5GpgCq4eUluAr32WyjgiNjaW4OBgXn/9daejmCuUKFGCZcuW0bhxY5599lmmTJnidCS/+umnn6hfvz5Hjhxh9erVhIeHOx2pQCqc9Sagqn93L04UkZVAKVXd7rtYxt/27NnDBx98wMsvv0yFChWcjmPSUaxYMRYvXsyjjz5K9+7dSU5OplevXk7H8rkffviBiIgI/vjjD9asWcPdd9/tdKQCK8sWhogUds+eh4hUAcJwTaRkAsigQYMoWrQo/fv3dzqKyUSRIkVYsGABbdu25YUXXmDUqDw1w4DX7d27l/DwcM6cOUNCQoIVC4dlWjBE5Dlc9yt+di+vxXUDfK6IZPqTRUSqiMg6EdktIjtFpLd7/ePu96kikuH4JSLyk4jsEJFtImIPCfrQjh07+Oijj+jduzflypVzOo7JQmhoKJ988gmPP/44ffv2ZejQoU5H8oldu3YRHh5OcnIyiYmJ1KlTx+lIBV5Wl6T6ADfgmvtiN1BNVY+LSDFc824Pz2TfFKCvqm4RkZLAZhGJB74FHgEmeZCvoaoe92A7kwvR0dFcddVVvPLKK05HMR4KDg5mzpw5BAcH88Ybb5CcnExUVFTAzPmwfft2GjduTFBQEImJidSsWdPpSIasC0ayqv4G/CYi+y7/8FbVsyKSnNmOqnoYOOxePi0iu4FKqhoPBMw/7Pxu8+bNLFy4kJiYGEqXLu10HJMNhQsX5v333yckJIRBgwaRnJzM4MGD8/3/W1u2bKFJkyYULVqUhIQEbr75ZqcjGbesCkZREamD69JViHtZ3K8inp5ERKoDdYCvspFNgdUiosAkVZ2cwbG7A93BNXibyZ6oqCjKlClDnz59nI5iciAoKIhp06YREhLCP//5Ty5cuMCbb76Zb4vG119/TbNmzbjqqqtISEiwh0fzmKwKxmHg8l21f6dZvvw+SyJSApgP9FHVP7KR7QFV/VVEygPxIrJHVTdcuZG7kEwG13wY2Th+gffll1+yfPlyhg0bRqlSpZyOY3KoUKFCTJw4kZCQEN566y2Sk5MZO3Zsvisan3/+OS1atPjPsCjVqlVzOpK5QqYFQ1Ub5ubgIhKMq1jMVtUF2dlXVX91/3lURBYC9YD/KRgm5yIjIylfvjwvvPCC01FMLokI48aNIyQkhFGjRnHhwgXeffddChXyeLg4RyUmJtKqVSsqVarE2rVrqVy5stORTDo8eg4jJ9xdcacBu1U1W33/RKQ4UMh976M40BSwOUK9KDExkbVr1zJ69GiKFy/udBzjBSLCyJEjCQ0NZejQoSQnJzN16lSCgvJ2L/g1a9bQpk0batSowZo1a+w5oDzMZwUDeADoBOwQkW3udW/gGoPqbaAcsExEtqlqMxGpCExV1ZbAtcBCd5O6MDBHVVf6MGuBoqpERkZSsWJFevTo4XQc40UiwpAhQwgNDWXQoEFcvHiR9957j8KFffm/es6tWLGCdu3acfPNN7NmzRrKly/vdCSTiUz/FYnIA6r6uYiEquqF7BxYVTfiujmenoXpbP8r0NK9/COuAQ6ND8THx7Nx40YmTJhAkSIe910w+YSIEB0dTUhIyH+63M6ePZvg4GCno/2XxYsX8/jjj1OrVi3i4+MpW7as05FMFrL6tWMccBfwJVDX93GMr6kqAwcOpFq1ajaVZYB7/fXXCQkJ4ZVXXuHixYvMnTuX0NC8Mcj0vHnz6NChA3Xr1mXlypXWpTufyKpgXBSRGUAlERl35Yeq+pJvYhlfWbp0KZs2bWLq1Kl55oeH8Z2+ffsSEhLCSy+9xCOPPML8+fMdb1XOmTOHzp07c88997BixQrroZePZFUwWgGNgQhco9SafCw1NZWoqChuvPFGOne2CRMLihdffJHQ0FCef/552rRpw6effkqxYsUcyTJz5ky6dOnCQw89xNKlSylRooQjOUzOZNWt9jiucaN2q+o3fspkfGTBggVs27aNWbNm5bnr2ca3unfvTnBwMF27dqVVq1YsWbLE773jpk6dSvfu3WnUqBGLFi1yrGiZnPO0k/YJEVkoIkdF5IiIzBcR6yidj1y6dIno6Ghuu+02OnTo4HQc44Bnn32WWbNmsX79epo3b84ff2TnOdrcmTBhAs899xzNmzdnyZIlVizyKU8LxgxgMVARqAQsca8z+cTcuXPZtWsXMTExeb5fvvGdp556ig8//JAvv/ySpk2bcurUKZ+fc/To0bzwwgu0adOGhQsXOn4PxeScqGY9moaIfKOqta9Yt01V7/RZshwICwvTpCQbCf1KKSkp1KxZk6JFi7J169Z88/Sv8Z1PP/2UJ554gr/85S+sXr2aMmXK+OQ8w4YN4/XXX+fRRx9lzpw5hISE+OQ8JudEZLOqZjjVRFqe/uQ4JiIdRSTI/eoInMh5RONPs2bN4vvvvycuLs6KhQHgr3/9KwsXLuTbb78lIiKCY8eOef0csbGxvP7663To0IG5c+dasQgEqprlC6iK65LUMVwTKn2Ka24Mj/b31+uuu+5S898uXLig1apV07vvvltTU1OdjmPymFWrVmmRIkW0Vq1aevjwYa8cMzU1VQcMGKCAdu7cWVNSUrxyXOMbQJJ6+DPWo183VfWAqrZR1XKqWl5V/6qqP/uohhkvmj59Oj///DOxsbH5bvRS43tNmzZl+fLl7N+/nwYNGnDo0KFcHU9VefXVVxkyZAjdunVjxowZds8sgNj1iQB2/vx5Bg8ezAMPPECzZs2cjmPyqIYNG7Jy5UoOHTpEeHg4Bw4cyNFxVJU+ffowcuRI/v73vzNp0iS7BBpg7NsMYJMmTeLQoUPExcVZ68Jk6qGHHiI+Pp7jx48THh7O/v37s7V/amoqPXv2ZNy4cbz88suMHz/eikUAyvIbFZFCIvKEP8IY7/nzzz8ZOnQoERERNGyYq2lNTAFx7733snbtWn7//Xfq16/Pvn37PNrv0qVLdOvWjUmTJvHaa6/x1ltv2S8oASrLgqGqqYDNsJPPTJgwgSNHjhAXF+d0FJOP3HXXXaxbt47z589Tv3599uzZk+n2KSkpPPPMM8yYMYP/Bq8mAAAYHElEQVSoqCj++c9/WrEIYJ62GeNF5BURqSIiZS6/fJrM5Njp06cZMWIEzZs35/7773c6jslnateuzbp160hNTSU8PJxvv/023e0uXrzIU089xQcffMDgwYOJiYmxYhHgPC0YXYBeuKZI3ex+ZfqEnLu4rBOR3SKyU0R6u9c/7n6fKiIZPiwiIs1FZK+I7BOR1zzMaYCxY8dy4sQJa12YHLv99ttJTEwkKCiIBg0asG3btv/6PDk5mfbt2/Pxxx/z5ptvMmDAAIeSGr/ytP9tdl9ABaCue7kk8B1QE7gNuAVIBMIy2DcI+AG4HggBvgFqZnVOew5D9eTJk3rVVVdp27ZtnY5iAsD333+vVapU0dKlS+umTZtUVfXcuXP68MMPK6Bjx451OKHJLbz9HIaIFBORgSIy2f3+JhFplUUhOqyqW9zLp4HdQCVV3a2qe7M4ZT1gn6r+qKrJwFygrSdZC7pRo0bx+++/ExtrU6Cb3LvxxhvZsGEDV111FY0aNWLdunW0bduWZcuWMXHiRF56yabEKUiyM/hgMnD5gvhBYLCnJxGR6kAd4CsPd6kE/JLm/UH3OpOJ48ePM2bMmP+MEWSMN1SvXp0NGzZQrlw5IiIiiI+PZ9q0aTz//PNORzN+5mnBuEFVRwAXAVT1HBnP1/1fRKQEMB/oo6qejqec3rHTHSVRRLqLSJKIJPliPJz8ZMSIEZw9e5ZBgwY5HcUEmCpVqrBhwwYefvhhZs+eTZcuXZyOZByQ1Yx7lyWLSFHcP7RF5AbgQlY7iUgwrmIxW1UXZCPXQaBKmveVgV/T21BVJwOTwTVabTbOEVD+/e9/M378eJ566iluu+02p+OYAFSxYkWWLl3qdAzjIE9bGNHASqCKiMwG1gKvZraDuPrXTQN2q+qobObaBNwkIjVEJAR4EtfghyYDQ4cOJTk5maioKKejGGMClEctDFWNF5EtwL24Lhf1Vtf0rZl5AOgE7BCRy33y3gBCgbeBcsAy97wazUSkIjBVVVuqaoqIvACswtVjarqq7sz2366A+OWXX5g4cSLPPPMMN954o9NxjDEBytNLUgDhwIO4LksFAwsz21hVN5LxfY7/2VdVfwVapnm/HFiejXwF1pAhQ1BVIiMjnY5ijAlgnnarfQfoAewAvgWeF5EJvgxmPLN//36mTZvGc889R7Vq1ZyOY4wJYJ62MMKB290PeSAiM3EVD+OwuLg4ChcubE/aGmN8ztOb3ntxzbp3WRVgu/fjmOz47rvvmDlzJj179qRixYpOxzHGBLhMWxgisgTXPYurgN0i8rX7/T3AF76PZzITExNDkSJFeO01G2rLGON7WV2SGumXFCbbvv32Wz788EP69+9P+fLlnY5jjCkAMi0Yqro+7XsRKZXVPsY/Bg0aRIkSJXjllVecjmKMKSA8+uEvIt2BOOAckIqru6ziGk3W+NnWrVuZP38+0dHRlC1b1uk4xpgCwtPWQj+glgcP6xk/iIqKonTp0rz88stORzHGFCCe9pL6ATjryyDGM1999RVLly7llVde4aqrrnI6jjGmAPG0hfE68IWIfEWaQQdV1QbD97OoqCiuueYam4fAGON3nhaMSUACrof1Un0Xx2Tms88+Y/Xq1YwcOZISJUo4HccYU8B4WjBSVPUfPk1iMqWqDBw4kAoVKtCzZ0+n4xhjCiBPC8Y6d0+pJfz3JamTPkll/sfatWvZsGEDb7/9NsWKFXM6jjGmABL38FCZbySyP53Vqqp5qlttWFiYJiUlOR3D61SV+++/n0OHDvH9998TGhrqdCRjTIAQkc2qGubJtp7Oh1Ejd5FMbqxYsYJ//etfTJ482YqFMcYxnrYwOqe3XlXf93qiXAjEFoaqEhYWxqlTp9izZw/BwcFORzLGBBCvtzCAu9MsFwEaAVuADAuGiFRxf34drp5Vk1V1rIiUAT4CqgM/AU+o6m/p7H+J/x9C/YCqtvEwa0D59NNP2bJlCzNnzrRiYYxxlEctjP/ZSeQqYFZmP8RFpAJQQVW3iEhJYDPwV+AZ4KSqDhOR14DSqto/nf3PqGq2+o4GWgsjNTWV2rVrc/HiRXbu3ElQUJDTkYwxAcYXLYwrnQVuymwDVT0MHHYvnxaR3UAloC3QwL3ZTCAR+J+CYeDjjz/+z6i0ViyMMU7zdPDBy/NigGs4kZrAx56eRESqA3WAr4Br3cUEVT0sIhmNzV1ERJKAFGCYqn7q6fkCQUpKCoMGDeL222/niSeecDqOMcZ43MJIOy9GCvCzqh70ZEcRKQHMB/qo6h8i4mm2qqr6q4hcDySIyA5V/SGd43cHugNUrVr1yo/zrdmzZ7N3714WLFhAoUKeDvlljDG+k6N7GB4fXCQYWAqsUtVR7nV7gQbu1kUFIFFVb8niOO8BS1V1XmbbBco9jIsXL3LLLbdQunRpkpKSyEaRNcaYbPHaPQz3A3sZVRRV1Rsy2VeAacDuy8XCbTHwNDDM/eeidPYtDZxV1Qsicg3wADAis6yBZMaMGezfv5/x48dbsTDG5BmZtjBE5MrZeQoBTwCvAFtU9dFM9n0Q+Iz/HrDwDVz3MT4GqgIHgMdV9aSIhAE9VLWbiNyPa8DDVPc5x6jqtKz+MoHQwrhw4QI33XQTlSpV4osvvrCCYYzxKa+1MFT1hPuAhYBOuCZS2gY8rKq7sth3I66Z+dLTKJ3tk4Bu7uUvgDuyCh+IpkyZwi+//MKMGTOsWBhj8pSsLkkFA12Al4GNQNv0bjwb7zh79ixDhgwhPDyciIgIp+MYY8x/yaqX1H5cvaLG4Lp8VFtEal/+UFUX+DBbgfPuu+/y73//m48//thaF8aYPCergrEG103v2u5XWgpYwfCS06dPM2zYMJo2bcpDDz3kdBxjjPkfWd3DeMZPOQq8t99+m+PHjxMXF+d0FGOMSVemT4SJSEf3De+MPr/B3RvK5MKpU6d48803ad26NfXq1XM6jjHGpCurS1Jlga0ishnX4IHHcI1WeyMQDhwHXvNpwgJg9OjRnDp1itjYWKejGGNMhrJ80ltEgoAIXA/PVQDOAbuBFap6wOcJsyE/Podx4sQJatSoQdOmTZk3L9MH2Y0xxuu8Olqtql4C4t0v42UjR47kzJkzxMTEOB3FGGMyZaPaOejIkSOMGzeODh06UKtWLafjGGNMpqxgOGj48OFcuHCB6Ohop6MYY0yWrGA45NChQ7zzzjt07tyZm2++2ek4xhiTpSzvYYjILbjmm7jVvWo3MEVV9/oyWKD75z//yaVLl4iMjHQ6ijHGeCSr5zDuwzWF6mlgMjAF+BNYJyL3+jxdgPr555+ZMmUK3bp1o0aNGk7HMcYYj2TVwogCOqhqYpp1n4pIAhANtPBVsEAWFxdHoUKFGDBggNNRjDHGY1ndw7jhimIBgKquB673SaIAt2/fPt577z169OhB5cqVnY5jjDEey6pgnM7ksz+9GaSgiImJISQkhNdeswfkjTH5S1aXpKqIyLh01gtQKbMdRaQK8D5wHa6Z8yar6lgRKQN8BFQHfgKeUNXf0tn/aWCg++1gVZ2ZRdY8b/fu3cyePZtXXnmF6667zuk4xhiTLVkVjH6ZfJbVGBwpQF9V3SIiJYHNIhIPPAOsVdVhIvIarrGo+qfd0V1UooEwXMOobxaRxekVlvxk0KBBFC9enFdffdXpKMYYk21ZDW+e49/qVfUwcNi9fFpEduNqlbQFGrg3m4mrF1b/K3ZvBsSr6kkAd6FpDnyY0zxO++abb/j4448ZOHAg11xzjdNxjDEm27KaonUJrt/w06WqbTw5iYhUB+oAXwHXuosJqnpYRMqns0sl4Jc07w+SwSUwEemO6zkRqlat6kkcR0RHR3P11VfTt29fp6MYY0yOZHVJamRuTyAiJYD5QB9V/cPDqUfT2yjdwqWqk3E9I0JYWFjmQ+86ZNOmTSxatIi4uDiuvvpqp+MYY0yOZHVJav3lZREp5153zNODi0gwrmIxO83830dEpIK7dVEBOJrOrgf5/8tWAJVxXbrKl6Kioihbtiy9e/d2OooxxuRYlmNJiUi0iBwH9gDficgxEYnyYD8BpgG7VXVUmo8WA0+7l58GFqWz+yqgqYiUFpHSQFP3unzn888/Z+XKlfTv35+SJUs6HccYY3Isq6FBXgYeBO5W1bKqWhq4B3jA/VlmHgA6AREiss39agkMA5qIyPdAE/d7RCRMRKYCuG92xwGb3K/YyzfA85vIyEiuvfZaevXq5XQUY4zJlazuYXQGmqjq8csrVPVHEekIrAZGZ7Sjqm4k/XsRAI3S2T4J6Jbm/XRgehb58rR169axbt06xo4dS7FixZyOY4wxuZLVJangtMXiMvd9jGDfRAoMqkpkZCSVK1eme/fuTscxxphcy6qFkZzDzwq8VatW8fnnn/Puu+9SpEgRp+MYY0yuiWrGPVFF5BLpjxklQBFVzVOtjLCwME1KyuoBdN9TVerVq8fx48fZu3cvISEhTkcyxph0ichmVQ3zZNusutUGeSdSwbJ48WKSkpKYPn26FQtjTMDItIWR3+SFFkZqaip16tTh3Llz7Nq1i8KFs5zU0BhjHOO1FobJvvnz57N9+3Zmz55txcIYE1CsheFFly5d4o477kBE2L59O0FBdkXPGJO3WQvDIR9++CG7d+9m3rx5ViyMMQHHWhhecvHiRW677TZKlizJ5s2bKVQoy1FXjDHGcdbCcMD777/PDz/8wOLFi61YGGMCkv1k84ILFy4QGxtLvXr1aNWqldNxjDHGJ6yF4QXTpk3jwIEDTJkyBQ/n+zDGmHzHWhi5dO7cOYYMGcJDDz1EkyZNnI5jjDE+Yy2MXJo4cSK//vorc+bMsdaFMSagWQsjF/7880+GDRtGo0aNCA8PdzqOMcb4lBWMXBg/fjxHjx4lLi7O6SjGGONzPisYIjJdRI6KyLdp1tUWkS9FZIeILBGRUhns+5N7m20i4vzws+n4448/GDFiBC1btuS+++5zOo4xxvicL1sY7wHNr1g3FXhNVe8AFgL9Mtm/oare6ekDJf42ZswYTp48SWxsrNNRjDHGL3xWMFR1A3DlPNy3ABvcy/HAo746vy+dPHmSt956i3bt2nHXXXc5HccYY/zC3/cwvgXauJcfB6pksJ0Cq0Vks4hkOr+piHQXkSQRSTp27JgXo2bsrbfe4vTp08TExPjlfMYYkxf4u2B0AXqJyGagJBlP8/qAqtYFWri3r5/RAVV1sqqGqWpYuXLlvJ/4CseOHWPs2LG0b9+eO+64w+fnM8aYvMKvBUNV96hqU1W9C/gQ+CGD7X51/3kU172Oev5Lmbnhw4dz7tw5Bg0a5HQUY4zxK78WDBEp7/6zEDAQmJjONsVFpOTlZaAprktZjjt8+DATJkygU6dO3HLLLU7HMcYYv/Jlt9oPgS+BW0TkoIh0BTqIyHfAHuBXYIZ724oisty967XARhH5BvgaWKaqK32VMzuGDh1KSkoKUVFRTkcxxhi/89nQIKraIYOPxqaz7a9AS/fyj0BtX+XKqQMHDjBp0iSeffZZrr/+eqfjGGOM39mT3h4aMmQIAAMHDnQ4iTHGOMMKhgd+/PFHpk+fTvfu3alatarTcYwxxhFWMDwQGxtL4cKFeeONN5yOYowxjrGCkYW9e/cya9YsevXqRYUKFZyOY4wxjrGCkYVBgwZRtGhR+vfv73QUY4xxlBWMTOzYsYOPPvqI3r1744+nyI0xJi+zgpGJ6OhoSpYsSd++fZ2OYowxjrOCkYEtW7awcOFC+vbtS5kyZZyOY4wxjrOCkYGoqCjKlClDnz59nI5ijDF5ghWMdHz55ZcsW7aMfv36UapUupMCGmNMgWMFIx1RUVGUL1+eF1980ekoxhiTZ/hsLKn8av369axZs4ZRo0ZRvHhxp+MYY0yeYS2MNFSVyMhIKlasSI8ePZyOY4wxeYq1MNJYs2YNn332GRMmTKBo0aJOxzHGmDzFWhhuqsrAgQOpWrUqXbt2dTqOMcbkOb6cQGm6iBwVkW/TrKstIl+KyA4RWSIi6XZBEpHmIrJXRPaJyGu+ypjWsmXL+Prrr4mKiiI0NNQfpzTGmHzFly2M94DmV6ybCrymqnfgmqu735U7iUgQMAFoAdTENUtfTR/mJDU1lcjISG644QY6d+7sy1MZY0y+5bOCoaobgJNXrL4F2OBejgceTWfXesA+Vf1RVZOBuUBbX+UEWLhwIdu2bSM6Oprg4GBfnsoYY/Itf9/D+BZo415+HKiSzjaVgF/SvD/oXucTly5dIjo6mltvvZW//e1vvjqNMcbke/4uGF2AXiKyGSgJJKezjaSzTjM6oIh0F5EkEUk6duxYtgOdPXuWe++9l8GDBxMUFJTt/Y0xpqDwa7daVd0DNAUQkZuBh9PZ7CD/3fKoDPyayTEnA5MBwsLCMiwsGSlZsiRTp07N7m7GGFPg+LWFISLl3X8WAgYCE9PZbBNwk4jUEJEQ4Elgsf9SGmOMSY8vu9V+CHwJ3CIiB0WkK64eT98Be3C1Gma4t60oIssBVDUFeAFYBewGPlbVnb7KaYwxxjOimu2rOHlWWFiYJiUlOR3DGGPyDRHZrKphnmxrT3obY4zxiBUMY4wxHrGCYYwxxiNWMIwxxnjECoYxxhiPBFQvKRE5Bvycw92vAY57MY7JPftO8ib7XvKe3Hwn1VS1nCcbBlTByA0RSfK0a5nxD/tO8ib7XvIef30ndknKGGOMR6xgGGOM8YgVjP832ekA5n/Yd5I32feS9/jlO7F7GMYYYzxiLQxjjDEeCdiCISLTReSoiHybZl1tEflSRHaIyBIRKeVeX09Etrlf34hIu8yOY3LOG9+LiFQRkXUisltEdopIb6f+PoHAS99JERH52r1up4jEOPX3CRTe+hnm/jxIRLaKyNJchVLVgHwB9YG6wLdp1m0Cwt3LXYA493IxoLB7uQJwNM37/zmOvZz9XtzLdd3rSwLfATWd/rvl15eXvhMBSrjXBwNfAfc6/XfLzy9v/Qxzr/sHMAdYmptMAdvCUNUNwMkrVt8CbHAvxwOPurc9q655OACKkGZK2AyOY3LIG9+Lqh5W1S3u5dO45k3x2bzvgc5L34mq6hn3+mD3y26Q5oK3foaJSGVcs5vmemrRgC0YGfgWaONefpw0U8GKyD0ishPYAfRI8x/f+F6OvxcRqQ7UwfUbrfGebH8n7sse23D9dhuvqvadeF9O/l8ZA7wKpOb25AWtYHQBeonIZlyXMpIvf6CqX6lqLeBu4HURKeJQxoIoR9+LiJQA5gN9VPUPP2cOdNn+TlT1kqreCVQG6onI7Q7kDnTZ+l5EpBVwVFU3e+Pkhb1xkPxCVfcATQFE5GZczbQrt9ktIn8CtwM2fZ8f5OR7EZFgXMVitqou8GfegiA3/6+o6ikRSQSa4/qN2HhJDr6XB4A2ItIS16WqUiLygap2zMn5C1QLQ0TKu/8sBAwEJrrf1xCRwu7lariuE/7kUMwCJ7vfi4gIMA3YraqjnEkd2HLwnZQTkavd64sCjYE9TmQPZNn9XlT1dVWtrKrVgSeBhJwWCwjggiEiHwJfAreIyEER6Qp0EJHvcP1D/hWY4d78QeAb9/XXhcDfVfV4JscxOeSl7+UBoBMQkaYrYUu//2UChJe+kwrAOhHZjqsnT7yq5q4LZwHnrZ9hXs3k7nJljDHGZCpgWxjGGGO8ywqGMcYYj1jBMMYY4xErGMYYYzxiBcMYY4xHrGAYk0PislFEWqRZ94SIrHQylzG+Yt1qjckF9/AXn+AazyoI2AY0V9UfcnHMwjaWmcmLrGAYk0siMgL4EygOnFbVOBF5GugFhABfAC+oaqqITMY1ZHVR4CNVjXUf4yAwCddwGmNU9RMH/irGZKpAjSVljI/EAFtwDQQX5m51tAPuV9UUd5F4Etd8BK+p6kn3MA7rRGSequ5yH+dPVX3Aib+AMZ6wgmFMLqnqnyLyEXBGVS+ISGNcI4YmuYa9oijwi3vzDu4hHgoDFYGawOWC8ZF/kxuTPVYwjPGOVP5/vgEBpqtqZNoNROQmoDdQzz2i6we4RhC97E+/JDUmh6yXlDHetwZ4QkSuARCRsiJSFSgFnAb+EJEKQDMHMxqTbdbCMMbLVHWHiMQAa9zDUF8EeuCaM2IXrjkifgQ+dy6lMdlnvaSMMcZ4xC5JGWOM8YgVDGOMMR6xgmGMMcYjVjCMMcZ4xAqGMcYYj1jBMMYY4xErGMYYYzxiBcMYY4xH/g9vjKcxNaEOtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb040f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(CF_Year[1:], DID[1:],'k')\n",
    "plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"DID (Number of Banks)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just DDo It: A Depression Regression\n",
    "The simplest DD calculation involves only four numbers (as in ``delta_DD`` or ``delta_DD2``). In practice, however, the DD recipe is best cooked with regression models fit to samples of more than four data points, such as the 12 points plotted in Figure 5.2.\n",
    "The regression DD recipe associated with Figure 5.2 has three ingredients:\n",
    "1. A dummy for the treatment district, written $TREAT_d$, where the subscript $d$ reminds us that this varies across districts; $TREAT_d$ controls for fixed differences between the units being compared.\n",
    "2. A dummy for post-treatment periods, written $POST_t$, where the subscript $t$ reminds us that this varies over time; $POST_t$ controls for the fact that conditions change over time for everyone, whether treated or not.\n",
    "3. The interaction term, $TREAT_d \\times POST_t$, generated by multiplying these two dummies; the coefficient on this term is the DD causal effect.\n",
    "Regression DD for the Mississippi experiments puts these pieces together by estimating\n",
    "$$Y_{dt}=\\alpha + \\beta TREAT_d+\\gamma POST_t +\\delta_{rDD}(TREAT_d \\times POST_t)+e_{dt}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Treat_d</th>\n",
       "      <th>Post_t</th>\n",
       "      <th>TreatPost</th>\n",
       "      <th>Ydt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Treat_d  Post_t  TreatPost  Ydt\n",
       "1         1       0          0  141\n",
       "2         1       0          0  135\n",
       "3         1       1          1  121\n",
       "4         1       1          1  113\n",
       "5         1       1          1  102\n",
       "6         1       1          1  102\n",
       "7         0       0          0  169\n",
       "8         0       0          0  165\n",
       "9         0       1          0  132\n",
       "10        0       1          0  120\n",
       "11        0       1          0  111\n",
       "12        0       1          0  109"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata = {'Treat_d':[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\\\n",
    "           'Post_t':[0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],\\\n",
    "           'TreatPost':[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],\\\n",
    "           'Ydt': Y6+Y8\n",
    "        }\n",
    "df2 = pd.DataFrame(newdata, columns =['Treat_d', 'Post_t', 'TreatPost', 'Ydt'])\n",
    "df2.index += 1\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Ydt</td>       <th>  R-squared:         </th> <td>   0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   21.71</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 10 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>0.000337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:59:43</td>     <th>  Log-Likelihood:    </th> <td> -40.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    12</td>      <th>  AIC:               </th> <td>   89.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     8</td>      <th>  BIC:               </th> <td>   91.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>  167.0000</td> <td>    6.190</td> <td>   26.980</td> <td> 0.000</td> <td>  152.727</td> <td>  181.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Treat_d</th>   <td>  -29.0000</td> <td>    8.754</td> <td>   -3.313</td> <td> 0.011</td> <td>  -49.186</td> <td>   -8.814</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Post_t</th>    <td>  -49.0000</td> <td>    7.581</td> <td>   -6.464</td> <td> 0.000</td> <td>  -66.481</td> <td>  -31.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TreatPost</th> <td>   20.5000</td> <td>   10.721</td> <td>    1.912</td> <td> 0.092</td> <td>   -4.222</td> <td>   45.222</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.065</td> <th>  Durbin-Watson:     </th> <td>   1.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.587</td> <th>  Jarque-Bera (JB):  </th> <td>   0.856</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.548</td> <th>  Prob(JB):          </th> <td>   0.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.285</td> <th>  Cond. No.          </th> <td>    8.44</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    Ydt   R-squared:                       0.891\n",
       "Model:                            OLS   Adj. R-squared:                  0.850\n",
       "Method:                 Least Squares   F-statistic:                     21.71\n",
       "Date:                Wed, 10 Jun 2020   Prob (F-statistic):           0.000337\n",
       "Time:                        21:59:43   Log-Likelihood:                -40.628\n",
       "No. Observations:                  12   AIC:                             89.26\n",
       "Df Residuals:                       8   BIC:                             91.20\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        167.0000      6.190     26.980      0.000     152.727     181.273\n",
       "Treat_d      -29.0000      8.754     -3.313      0.011     -49.186      -8.814\n",
       "Post_t       -49.0000      7.581     -6.464      0.000     -66.481     -31.519\n",
       "TreatPost     20.5000     10.721      1.912      0.092      -4.222      45.222\n",
       "==============================================================================\n",
       "Omnibus:                        1.065   Durbin-Watson:                   1.668\n",
       "Prob(Omnibus):                  0.587   Jarque-Bera (JB):                0.856\n",
       "Skew:                           0.548   Prob(JB):                        0.652\n",
       "Kurtosis:                       2.285   Cond. No.                         8.44\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm # import statsmodels\n",
    "X = df2[['Treat_d', 'Post_t', 'TreatPost']] ## X usually means our input variables (or independent variables)\n",
    "y = df2['Ydt'] ## Y usually means our output/dependent variable\n",
    "X = sm.add_constant(X) ## let's add an intercept (alpha) to our model\n",
    "\n",
    "# Note the difference in argument order\n",
    "model = sm.OLS(y, X).fit() ## sm.OLS(output, input)\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the coefficients for each model's parameter are:\n",
    "$$\\alpha = 167$$\n",
    "$$\\beta = -29$$\n",
    "$$\\gamma = -49$$\n",
    "$$\\delta_{rDD} = 20.5 $$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Y_{dt}=167 - 29 TREAT_d - 49 POST_t + 20.5(TREAT_d \\times POST_t)+e_{dt}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Refrences\n",
    "- Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton University Press.\n",
    "- http://www.masteringmetrics.com/\n",
    "- https://jrnold.github.io/masteringmetrics/mississippi-bank-failures-in-the-great-depression.html"
   ]
  }
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