{
 "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:                Thu, 05 Nov 2020   No. Observations:                  235\n",
      "Time:                        07:28:38   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()"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
