{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantile regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in \n",
    "\n",
    "* Koenker, Roger and Kevin F. Hallock. \"Quantile Regression\". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143–156\n",
    "\n",
    "We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). \n",
    "\n",
    "## Setup\n",
    "\n",
    "We first need to load some modules and to retrieve the data. Conveniently, the Engel dataset is shipped with ``statsmodels``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>income</th>\n",
       "      <th>foodexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420.157651</td>\n",
       "      <td>255.839425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>541.411707</td>\n",
       "      <td>310.958667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>901.157457</td>\n",
       "      <td>485.680014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>639.080229</td>\n",
       "      <td>402.997356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750.875606</td>\n",
       "      <td>495.560775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       income     foodexp\n",
       "0  420.157651  255.839425\n",
       "1  541.411707  310.958667\n",
       "2  901.157457  485.680014\n",
       "3  639.080229  402.997356\n",
       "4  750.875606  495.560775"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = sm.datasets.engel.load_pandas().data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Least Absolute Deviation\n",
    "\n",
    "The LAD model is a special case of quantile regression where q=0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         QuantReg Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                foodexp   Pseudo R-squared:               0.6206\n",
      "Model:                       QuantReg   Bandwidth:                       64.51\n",
      "Method:                 Least Squares   Sparsity:                        209.3\n",
      "Date:                Mon, 24 Feb 2020   No. Observations:                  235\n",
      "Time:                        22:49:06   Df Residuals:                      233\n",
      "                                        Df Model:                            1\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     81.4823     14.634      5.568      0.000      52.649     110.315\n",
      "income         0.5602      0.013     42.516      0.000       0.534       0.586\n",
      "==============================================================================\n",
      "\n",
      "The condition number is large, 2.38e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "mod = smf.quantreg('foodexp ~ income', data)\n",
    "res = mod.fit(q=.5)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the results\n",
    "\n",
    "We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for plotting\n",
    "\n",
    "For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      q           a         b        lb        ub\n",
      "0  0.05  124.880100  0.343361  0.268632  0.418090\n",
      "1  0.15  111.693660  0.423708  0.382780  0.464636\n",
      "2  0.25   95.483539  0.474103  0.439900  0.508306\n",
      "3  0.35  105.841294  0.488901  0.457759  0.520043\n",
      "4  0.45   81.083647  0.552428  0.525021  0.579835\n",
      "5  0.55   89.661370  0.565601  0.540955  0.590247\n",
      "6  0.65   74.033434  0.604576  0.582169  0.626982\n",
      "7  0.75   62.396584  0.644014  0.622411  0.665617\n",
      "8  0.85   52.272216  0.677603  0.657383  0.697823\n",
      "9  0.95   64.103964  0.709069  0.687831  0.730306\n",
      "{'a': 147.47538852370633, 'b': 0.48517842367692343, 'lb': 0.45687381301842317, 'ub': 0.5134830343354236}\n"
     ]
    }
   ],
   "source": [
    "quantiles = np.arange(.05, .96, .1)\n",
    "def fit_model(q):\n",
    "    res = mod.fit(q=q)\n",
    "    return [q, res.params['Intercept'], res.params['income']] + \\\n",
    "            res.conf_int().loc['income'].tolist()\n",
    "    \n",
    "models = [fit_model(x) for x in quantiles]\n",
    "models = pd.DataFrame(models, columns=['q', 'a', 'b', 'lb', 'ub'])\n",
    "\n",
    "ols = smf.ols('foodexp ~ income', data).fit()\n",
    "ols_ci = ols.conf_int().loc['income'].tolist()\n",
    "ols = dict(a = ols.params['Intercept'],\n",
    "           b = ols.params['income'],\n",
    "           lb = ols_ci[0],\n",
    "           ub = ols_ci[1])\n",
    "\n",
    "print(models)\n",
    "print(ols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First plot\n",
    "\n",
    "This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:\n",
    "\n",
    "1. Food expenditure increases with income\n",
    "2. The *dispersion* of food expenditure increases with income\n",
    "3. The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(data.income.min(), data.income.max(), 50)\n",
    "get_y = lambda a, b: a + b * x\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for i in range(models.shape[0]):\n",
    "    y = get_y(models.a[i], models.b[i])\n",
    "    ax.plot(x, y, linestyle='dotted', color='grey')\n",
    "    \n",
    "y = get_y(ols['a'], ols['b'])\n",
    "\n",
    "ax.plot(x, y, color='red', label='OLS')\n",
    "ax.scatter(data.income, data.foodexp, alpha=.2)\n",
    "ax.set_xlim((240, 3000))\n",
    "ax.set_ylim((240, 2000))\n",
    "legend = ax.legend()\n",
    "ax.set_xlabel('Income', fontsize=16)\n",
    "ax.set_ylabel('Food expenditure', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second plot\n",
    "\n",
    "The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confidence interval.\n",
    "\n",
    "In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = models.shape[0]\n",
    "p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')\n",
    "p2 = plt.plot(models.q, models.ub, linestyle='dotted', color='black')\n",
    "p3 = plt.plot(models.q, models.lb, linestyle='dotted', color='black')\n",
    "p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')\n",
    "p5 = plt.plot(models.q, [ols['lb']] * n, linestyle='dotted', color='red')\n",
    "p6 = plt.plot(models.q, [ols['ub']] * n, linestyle='dotted', color='red')\n",
    "plt.ylabel(r'$\\beta_{income}$')\n",
    "plt.xlabel('Quantiles of the conditional food expenditure distribution')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
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