07_linear_models/07_logistic_regression_macro_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with Macro Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.425182Z",
     "start_time": "2021-04-15T20:16:57.853104Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.427872Z",
     "start_time": "2021-04-15T20:16:58.426052Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Variable   | Description                                  | Transformation     |\n",
    "|------------|----------------------------------------------|--------------------|\n",
    "| realgdp    | Real gross domestic product                  | Annual Growth Rate |\n",
    "| realcons   | Real personal consumption expenditures       | Annual Growth Rate |\n",
    "| realinv    | Real gross private domestic investment       | Annual Growth Rate |\n",
    "| realgovt   | Real federal expenditures & gross investment | Annual Growth Rate |\n",
    "| realdpi    | Real private disposable income               | Annual Growth Rate |\n",
    "| m1         | M1 nominal money stock                       | Annual Growth Rate |\n",
    "| tbilrate   | Monthly treasury bill rate                 | Level              |\n",
    "| unemp      | Seasonally adjusted unemployment rate (%)    | Level              |\n",
    "| infl       | Inflation rate                               | Level              |\n",
    "| realint    |  Real interest rate                          | Level              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.463263Z",
     "start_time": "2021-04-15T20:16:58.428948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203 entries, 0 to 202\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   year      203 non-null    float64\n",
      " 1   quarter   203 non-null    float64\n",
      " 2   realgdp   203 non-null    float64\n",
      " 3   realcons  203 non-null    float64\n",
      " 4   realinv   203 non-null    float64\n",
      " 5   realgovt  203 non-null    float64\n",
      " 6   realdpi   203 non-null    float64\n",
      " 7   cpi       203 non-null    float64\n",
      " 8   m1        203 non-null    float64\n",
      " 9   tbilrate  203 non-null    float64\n",
      " 10  unemp     203 non-null    float64\n",
      " 11  pop       203 non-null    float64\n",
      " 12  infl      203 non-null    float64\n",
      " 13  realint   203 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 22.3 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(sm.datasets.macrodata.load().data)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.478816Z",
     "start_time": "2021-04-15T20:16:58.464132Z"
    }
   },
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain a binary target variable, we compute the 20-quarter rolling average of the annual growth rate of quarterly real GDP. We then assign 1 if current growth exceeds the moving average and 0 otherwise. Finally, we shift the indicator variables to align next quarter's outcome with the current quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.486407Z",
     "start_time": "2021-04-15T20:16:58.479801Z"
    }
   },
   "outputs": [],
   "source": [
    "data['growth_rate'] = data.realgdp.pct_change(4)\n",
    "data['target'] = (data.growth_rate > data.growth_rate.rolling(20).mean()).astype(int).shift(-1)\n",
    "data.quarter = data.quarter.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.492446Z",
     "start_time": "2021-04-15T20:16:58.487361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    112\n",
       "1.0     90\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.514839Z",
     "start_time": "2021-04-15T20:16:58.493636Z"
    }
   },
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>9267.7</td>\n",
       "      <td>1990.693</td>\n",
       "      <td>991.551</td>\n",
       "      <td>9838.3</td>\n",
       "      <td>216.889</td>\n",
       "      <td>1474.7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>305.270</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>4</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>9195.3</td>\n",
       "      <td>1857.661</td>\n",
       "      <td>1007.273</td>\n",
       "      <td>9920.4</td>\n",
       "      <td>212.174</td>\n",
       "      <td>1576.5</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>305.952</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>8.91</td>\n",
       "      <td>-0.018619</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>9209.2</td>\n",
       "      <td>1558.494</td>\n",
       "      <td>996.287</td>\n",
       "      <td>9926.4</td>\n",
       "      <td>212.671</td>\n",
       "      <td>1592.8</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>306.547</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-0.033026</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9189.0</td>\n",
       "      <td>1456.678</td>\n",
       "      <td>1023.528</td>\n",
       "      <td>10077.5</td>\n",
       "      <td>214.469</td>\n",
       "      <td>1653.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.2</td>\n",
       "      <td>307.226</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-3.19</td>\n",
       "      <td>-0.038297</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9256.0</td>\n",
       "      <td>1486.398</td>\n",
       "      <td>1044.088</td>\n",
       "      <td>10040.6</td>\n",
       "      <td>216.385</td>\n",
       "      <td>1673.9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>308.013</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>-0.025086</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
       "198  2008.0        3  13324.600    9267.7  1990.693   991.551   9838.3   \n",
       "199  2008.0        4  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
       "200  2009.0        1  12925.410    9209.2  1558.494   996.287   9926.4   \n",
       "201  2009.0        2  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
       "202  2009.0        3  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
       "\n",
       "         cpi      m1  tbilrate  unemp      pop  infl  realint  growth_rate  \\\n",
       "198  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33     0.000262   \n",
       "199  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91    -0.018619   \n",
       "200  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71    -0.033026   \n",
       "201  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19    -0.038297   \n",
       "202  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44    -0.025086   \n",
       "\n",
       "     target  \n",
       "198     0.0  \n",
       "199     0.0  \n",
       "200     0.0  \n",
       "201     0.0  \n",
       "202     NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.526092Z",
     "start_time": "2021-04-15T20:16:58.517190Z"
    }
   },
   "outputs": [],
   "source": [
    "pct_cols = ['realcons', 'realinv', 'realgovt', 'realdpi', 'm1']\n",
    "drop_cols = ['year', 'realgdp', 'pop', 'cpi', 'growth_rate']\n",
    "data.loc[:, pct_cols] = data.loc[:, pct_cols].pct_change(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.539431Z",
     "start_time": "2021-04-15T20:16:58.527637Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data.drop(drop_cols, axis=1), columns=['quarter'], drop_first=True).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.555664Z",
     "start_time": "2021-04-15T20:16:58.540770Z"
    }
   },
   "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>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "      <th>target</th>\n",
       "      <th>quarter_2</th>\n",
       "      <th>quarter_3</th>\n",
       "      <th>quarter_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.036957</td>\n",
       "      <td>0.156237</td>\n",
       "      <td>-0.016692</td>\n",
       "      <td>0.036356</td>\n",
       "      <td>-0.000716</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.034147</td>\n",
       "      <td>-0.040877</td>\n",
       "      <td>-0.043426</td>\n",
       "      <td>0.024170</td>\n",
       "      <td>-0.010586</td>\n",
       "      <td>2.68</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.019409</td>\n",
       "      <td>0.024718</td>\n",
       "      <td>-0.033758</td>\n",
       "      <td>0.026821</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>2.36</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.70</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.019673</td>\n",
       "      <td>-0.132257</td>\n",
       "      <td>-0.015738</td>\n",
       "      <td>0.018278</td>\n",
       "      <td>0.007857</td>\n",
       "      <td>2.29</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.009715</td>\n",
       "      <td>-0.196903</td>\n",
       "      <td>0.029544</td>\n",
       "      <td>0.014830</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>2.37</td>\n",
       "      <td>6.8</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   realcons   realinv  realgovt   realdpi        m1  tbilrate  unemp  infl  \\\n",
       "4  0.036957  0.156237 -0.016692  0.036356 -0.000716      3.50    5.2  2.31   \n",
       "5  0.034147 -0.040877 -0.043426  0.024170 -0.010586      2.68    5.2  0.14   \n",
       "6  0.019409  0.024718 -0.033758  0.026821  0.002847      2.36    5.6  2.70   \n",
       "7  0.019673 -0.132257 -0.015738  0.018278  0.007857      2.29    6.3  1.21   \n",
       "8  0.009715 -0.196903  0.029544  0.014830  0.017908      2.37    6.8 -0.40   \n",
       "\n",
       "   realint  target  quarter_2  quarter_3  quarter_4  \n",
       "4     1.19     0.0          0          0          0  \n",
       "5     2.55     0.0          1          0          0  \n",
       "6    -0.34     0.0          0          1          0  \n",
       "7     1.08     0.0          0          0          1  \n",
       "8     2.77     0.0          0          0          0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.569781Z",
     "start_time": "2021-04-15T20:16:58.556761Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 198 entries, 4 to 201\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   realcons   198 non-null    float64\n",
      " 1   realinv    198 non-null    float64\n",
      " 2   realgovt   198 non-null    float64\n",
      " 3   realdpi    198 non-null    float64\n",
      " 4   m1         198 non-null    float64\n",
      " 5   tbilrate   198 non-null    float64\n",
      " 6   unemp      198 non-null    float64\n",
      " 7   infl       198 non-null    float64\n",
      " 8   realint    198 non-null    float64\n",
      " 9   target     198 non-null    float64\n",
      " 10  quarter_2  198 non-null    uint8  \n",
      " 11  quarter_3  198 non-null    uint8  \n",
      " 12  quarter_4  198 non-null    uint8  \n",
      "dtypes: float64(10), uint8(3)\n",
      "memory usage: 17.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use an intercept and convert the quarter values to dummy variables and train the logistic regression model as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This produces the following summary for our model with 198 observations and 13 variables, including intercept:\n",
    "The summary indicates that the model has been trained using maximum likelihood and provides the maximized value of the log-likelihood function at -67.9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:58.627534Z",
     "start_time": "2021-04-15T20:16:58.570782Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.342965\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>target</td>      <th>  No. Observations:  </th>  <td>   198</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   185</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    12</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 15 Apr 2021</td> <th>  Pseudo R-squ.:     </th>  <td>0.5022</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:16:58</td>     <th>  Log-Likelihood:    </th> <td> -67.907</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -136.42</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.375e-23</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>   -8.5881</td> <td>    1.908</td> <td>   -4.502</td> <td> 0.000</td> <td>  -12.327</td> <td>   -4.849</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realcons</th>  <td>  130.1446</td> <td>   26.633</td> <td>    4.887</td> <td> 0.000</td> <td>   77.945</td> <td>  182.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realinv</th>   <td>   18.8414</td> <td>    4.053</td> <td>    4.648</td> <td> 0.000</td> <td>   10.897</td> <td>   26.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realgovt</th>  <td>  -19.0318</td> <td>    6.010</td> <td>   -3.166</td> <td> 0.002</td> <td>  -30.812</td> <td>   -7.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realdpi</th>   <td>  -52.2473</td> <td>   19.912</td> <td>   -2.624</td> <td> 0.009</td> <td>  -91.275</td> <td>  -13.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>m1</th>        <td>   -1.3462</td> <td>    6.177</td> <td>   -0.218</td> <td> 0.827</td> <td>  -13.453</td> <td>   10.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tbilrate</th>  <td>   60.8607</td> <td>   44.350</td> <td>    1.372</td> <td> 0.170</td> <td>  -26.063</td> <td>  147.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>unemp</th>     <td>    0.9487</td> <td>    0.249</td> <td>    3.818</td> <td> 0.000</td> <td>    0.462</td> <td>    1.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>infl</th>      <td>  -60.9647</td> <td>   44.362</td> <td>   -1.374</td> <td> 0.169</td> <td> -147.913</td> <td>   25.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realint</th>   <td>  -61.0453</td> <td>   44.359</td> <td>   -1.376</td> <td> 0.169</td> <td> -147.987</td> <td>   25.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quarter_2</th> <td>    0.1128</td> <td>    0.618</td> <td>    0.182</td> <td> 0.855</td> <td>   -1.099</td> <td>    1.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quarter_3</th> <td>   -0.1991</td> <td>    0.609</td> <td>   -0.327</td> <td> 0.744</td> <td>   -1.393</td> <td>    0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>quarter_4</th> <td>    0.0007</td> <td>    0.608</td> <td>    0.001</td> <td> 0.999</td> <td>   -1.191</td> <td>    1.192</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   No. Observations:                  198\n",
       "Model:                          Logit   Df Residuals:                      185\n",
       "Method:                           MLE   Df Model:                           12\n",
       "Date:                Thu, 15 Apr 2021   Pseudo R-squ.:                  0.5022\n",
       "Time:                        15:16:58   Log-Likelihood:                -67.907\n",
       "converged:                       True   LL-Null:                       -136.42\n",
       "Covariance Type:            nonrobust   LLR p-value:                 2.375e-23\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -8.5881      1.908     -4.502      0.000     -12.327      -4.849\n",
       "realcons     130.1446     26.633      4.887      0.000      77.945     182.344\n",
       "realinv       18.8414      4.053      4.648      0.000      10.897      26.786\n",
       "realgovt     -19.0318      6.010     -3.166      0.002     -30.812      -7.252\n",
       "realdpi      -52.2473     19.912     -2.624      0.009     -91.275     -13.220\n",
       "m1            -1.3462      6.177     -0.218      0.827     -13.453      10.761\n",
       "tbilrate      60.8607     44.350      1.372      0.170     -26.063     147.784\n",
       "unemp          0.9487      0.249      3.818      0.000       0.462       1.436\n",
       "infl         -60.9647     44.362     -1.374      0.169    -147.913      25.984\n",
       "realint      -61.0453     44.359     -1.376      0.169    -147.987      25.896\n",
       "quarter_2      0.1128      0.618      0.182      0.855      -1.099       1.325\n",
       "quarter_3     -0.1991      0.609     -0.327      0.744      -1.393       0.995\n",
       "quarter_4      0.0007      0.608      0.001      0.999      -1.191       1.192\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.Logit(data.target, sm.add_constant(data.drop('target', axis=1)))\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LL-Null value of -136.42 is the result of the maximized log-likelihood function when only an intercept is included. It forms the basis for the pseudo-R2 statistic and the Log-Likelihood Ratio (LLR) test. \n",
    "The pseudo-R2 statistic is a substitute for the familiar R2 available under least squares. It is computed based on the ratio of the maximized log-likelihood function for the null model m0 and the full model m1 as follows:\n",
    "The values vary from 0 (when the model does not improve the likelihood) to 1 where the model fits perfectly and the log-likelihood is maximized at 0. Consequently, higher values indicate a better fit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:59.152819Z",
     "start_time": "2021-04-15T20:16:58.628760Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize=(12, 7))\n",
    "plt.text(0.01, 0.05, str(result.summary()), {'fontsize': 14}, fontproperties = 'monospace')\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)\n",
    "plt.savefig('logistic_example.png', bbox_inches='tight', dpi=300);"
   ]
  }
 ],
 "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": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}