{
 "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:                Fri, 10 Jul 2020   No. Observations:                  235\n",
      "Time:                        05:46:36   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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VZebMmXL37l2767x165btvR4VFSXPPvusfPfdd8mK05F4QleSSye4uYq3tzfXr18nd+7cKXJWYXolIly/ft3uWaQq/bp58yYDBgygWbNm1KtXj+bNm+Ph4UHRokUB+yaixcfHs2HDBsaNG8ePP/6Ih4cHrVq1omfPng6ZiFa/fn0sFgv79u3Dy8uL06dP4+Xllaw6XSVNJgZfX1/OnTvH1atX3R2Keoi3tze+vr7uDkOlErGxsXh6epI5c2Z++OEHSpUqRb169ciTJ4/dE0DDwsKYNWsWEyZM4OTJk+TLl49BgwbRpUsX8ufPb3esy5YtY8yYMWzZsgUPDw++/vprfHx8bM+nlqQAaTQxeHp62tW/qJRKOQYOHMj69evZs2cP3t7eHD9+PFkfrr/99hvjx49n9uzZREREUL16dQYPHkyLFi3sGvSNiYlh7dq11K5dm1y5cuHp6UmGDBm4fPkyBQoU4F//+pfdsbqbDj4rpVKM3377jbi4OABKlSpFjRo1iIqKAuz7xm2xWFi1ahX16tWjdOnSfP/997Rs2ZKgoCB27txJ27Ztk5wU7g1wHzt2jObNm7N48WIAmjRpwrZt2yhQoECS40xxHjf4kFoejxp8VkqlPnv27BFA5syZk+y6rl+/LsOHD5fChQsLIL6+vjJ06FC5cuWK3XVaLBZp0qSJfPrpp7bfbd261bZ0RWqDzmNQSqVEQUFBrF27FoDKlSszatQoGjRoYHd9hw4donPnzvj6+vLZZ59RqFAhli5dypkzZ+jXr1+S90uOioqyLVWTIUMGChcuTL58+WzP16lTBw+PtNcjb6yJI/UKCAiQoKAgd4ehlLJD7dq1uXnzJgcPHkzW2kIrV65k3Lhx/O9//yNz5sy0b9+eHj16UK5cuWTF169fP0aMGMHZs2d59tlnk1VXSmOM2S8iAY96TlsMSimXOXjwIM2bN7ctaDdjxgy2b99uV1K4cuUKQ4cOpUiRIrRu3ZrQ0FBGjBjBuXPnmDp1ql1J4dKlS3Tq1ImDBw8C0K1bN9avX5+su5VSI00MSimnEhFiYmIA6508O3futC0FUaxYsSQvLbFv3z7efvttChYsyBdffEHp0qVZvXo1p06donfv3uTKlSvJ8d24cQOwDnCvXr2aQ4cOAVCwYEHq1q2b7uZDpb3OMaVUihEbG8vLL79MjRo1GDZsGJUrV+bPP/+0606g5cuXM3LkSPbs2YOPjw+dO3emR48etlVI7fXKK6/g4eHB+vXryZkzJ6GhoalqzoEzaItBKeVQ8fHxtq4YT09PqlWrRqlSpWzPJyUpxMfHs3TpUsqXL0+rVq24ceMGY8aM4fz584wfP96upHDp0iXGjh3LvfHVDh060L59e1s5vScFQG9XVUo51pdffimenp7y559/2l2HxWKRxYsXS5kyZQSQkiVLyvz585O1JtK99dOmT58uxhgJDg62u660AL1dVSnlLBaLhfnz53P8+HEA3nnnHb7//nu7Bmzj4+NZsmQJ5cqVo3Xr1sTFxTF//nyOHDlC27Zt7VoT6dq1a9StW5cFCxYA8NZbb3H8+HHKly+f5LrSC00MSqlkuXnzJp07d2bGjBkAPPfcc3To0CFJ9/c/nBDi4+NZsGCB3QkhIiLC1p2VK1cuMmTIYOsq8vb2pnjx4kmqL73RwWelVJLNnz/fth1lnjx52Lt37wPjCIl1bwzhyy+/5OjRo5QqVYoFCxbQqlWrZG1l2bZtWw4ePMjvv/+Oh4cHmzZtsruu9EhbDEqpRImOjrZ96z5z5gwHDhwgMjISAD8/vyTtoRwfH8/ixYspV64cb775pq2FcPjwYdq0aZPkpHDixAk6derEnTt3AOjfvz/z58937T7JaYgmBqXUPzp48CBFihSxLQ/x2WefsWvXLrJkyZKkeu4lhLJlyyY7IVgsFltiunXrFosWLbLt6la1alVq1KiR7uYfOIomBqXUI12/fp3Dhw8DUKJECWrWrEm2bNkA622oSfnQtVgsLFq0yJYQRISFCxfa3UKIioqidOnSDBkyBIAqVapw8eJFXnrppSTVox5NxxiUUo/06quvAtaF7ry9vW3LSyeFxWKxjSGEhIRQunRpFi5cSMuWLZOcDM6fP8+OHTto3bo13t7evPnmm1SuXNn2/P2b4qhketx9rKnlofMYlHKM3377Tf7v//7Ptoz0zz//LEeOHLGrrri4OFm4cKGULl1aACldurQsXLgwWfMQunXrJlmyZJGwsDC761B/QecxKKUeRUSwWCyAdQD3+++/t60T9NJLL+Hn55ek+iwWCwsXLqRs2bK0adMGgEWLFnH48GHefPPNJLUSbty4Qe/evW3zIwYOHMiRI0eSvLaSSjpNDEqlU7dv3+bFF19kwoQJADRq1IjQ0FAqVqyY5LruTwht27bFGGNLCK1bt07SHUv3xMXFMW3aNLZu3QpAvnz5dMteF9HEoFQ6Eh0dzYEDBwDInj07zz//vG3zmgwZMpAzZ84k1efohDBy5Ejat28PwNNPP83Zs2fp2rVrkupQyaeDz0qlIx988AHLli0jNDQUHx8f5s6da1c9FouFxYsX8+WXX3Ls2DH8/PxYvHgxLVq0SHIyiIiIIGvWrIB1NdaYmBhiYmLIlCkTTz31lF3xqeTRFoNSadi1a9fo378/ly5dAuDDDz9k4cKFtg/ipLJYLCxYsIAyZcrQrl07MmbMyOLFizl06BCtWrVKclIICgriueees3UXff755yxevDjJy3Irx9LEoFQaJAkzlG/evMnw4cP56aefAChbtiz169dP8sQvRyaEyMhITp06BUCZMmVo1KiRrTtLJ6SlDC7tSjLGvAaMATIC00Xkm0ccUwcYDXgC10SktitjVCo1ExE6duyIt7c3kydPpnjx4oSGhj6wgX1SxMXFsWjRIoYMGcKxY8coU6YMS5YsoXnz5nYNKAPUr1+f8PBwDhw4gLe3N7Nnz7arHuU8LmsxGGMyAhOABkBpoK0xpvRDxzwFTASaiIgf0MpV8SmVWokIwcHBgPUbd758+XjmmWdsz9uTFOLi4pgzZw6lS5emffv2eHh4sGTJEg4ePEjLli2TlBSio6OZNWsWcXFxgPW207Fjx2rrICV73AQHRz+A6sCG+8p9gb4PHdMdGJKUenWCm0rvxo8fL4CEhIQku66YmBiZMWOGFCtWTAApX768LFu2TCwWi911rl69WgBZvXp1suNTjsMTJri5siupABB6X/kcUPWhY14API0x24BswBgRmfNwRcaYQCAQrGu/K5WeREdHM3PmTCpWrEiVKlVo3bo1Xl5eFC1a1O46Y2JimD17Nl9//TV//PEHFStWZOXKlTRp0iTJ3+xFhNmzZ+Pl5UXbtm1p1KgRv/zyCzVr1rQ7PuVarhx8ftS7Sx4qewCVgIZAfWCAMeaFv71IZKqIBIhIwL1BK6XSOkkYULZYLAwYMIBFixYBkDdvXjp16mTXXsXR0dG2sYjAwEDy5s3LDz/8QFBQEE2bNrW7u2fGjBm2HdOMMdSqVUu7jlIRVyaGc0DB+8q+wIVHHLNeRCJE5BrwC6D776l079tvv+W1115DRMiSJQsHDhxg5MiRdtcXFRXF+PHjKVasGN26dePZZ5/lxx9/ZM+ePTRs2DDJH+K//PILtWrV4vbt2xhjWLFiBatWrbI7PuVerkwM+4DixpgixphMQBtg9UPHrAJqGWM8jDFZsHY1/ebCGJVKEUJDQ5k4cSLXrl0DIFu2bOTOnZu7d+8CULBgQbu+gd+9e5cxY8ZQtGhRevbsSZEiRdi0aRM7d+7ktddeS1KdIkJUVBQAWbJk4ebNm4SGWnuLc+fOrS2E1Oxxgw/OeACvAyeA34H+Cb/rCnS975hPgRDgCNDrn+rUwWeVGt24cUPmzp0rf/zxh4iI7Nu3T3x9feXnn38WEZHNmzcLIJs3b3bI+cLDw+Xbb7+VZ555RgCpXbu2bNmyReLj4+2qLyoqSipVqiSfffaZ7Xf21qXcg5SyuqqIrBORF0SkmIgMTfjdZBGZfN8xI0SktIiUEZHRroxPKUeJjo5m9erVHDt2DLDuJXBv2Qiwzkju0KGDbUe0fPnyUbduXdvKodWrV+fChQvUqVMnWXGEh4czfPhwihQpwieffEKZMmXYtm0b27Zt4+WXX05yC+G336wNeC8vL1555RUqVKhge15bCGnI4zJGanloi0G5Q3x8vGzdulUOHTokItbbPKtXry7jxo0TEZGIiAgBZMiQISIicvfuXWnWrJls2LBBRERiY2Pl2LFjEhUV5ZT4wsLC5Ouvv5bcuXMLIK+++qps3749WXV+88034unpKWfPnnVQlMqdSCG3qyqVqhw+fBiLxYK/vz8Ab775Jn5+fgwcOBBjDK1ataJFixZMnjwZT09P8ufPb/vGnyVLFvbv30+xYsUA8Pb2ZsWKFba6PTw8KFGihMNjDgsLY9y4cXz33XfcvHmT119/nQEDBlCtWjW76tu5cyfPPPMMxYoVo127duTIkcPuWdQq9dDEoNRjvPPOO+TPn5+1a9cC1g93T09P2/Nr166lQIECtvKyZcseeL09+xrY6+bNm4wdO5bRo0dz69YtGjduzMCBAwkICLC7zrCwMF599VXatWvH1KlTKViwoC6BnV48rimRWh7alaQcJSIiQr744gsJDw8XEZHdu3c7ZDaxM127dk2++OILyZ49uwDSrFkzOXDggN31HTt2TEaMGGErb9u2zXY9VNrCE7qS3P7BntyHJgblKD///LNkzJhRli9f7u5Q/tHVq1elb9++4uPjI4C0bNlSgoODk13v0KFDxcfHRy5cuOCAKFVK9qTEYKzPp14BAQESFBTk7jBUKnbmzBnblpH3/5wSXblyhZEjRzJx4kQiIyNp3bo1X3zxBWXKlLGrvrCwMAYMGEDz5s2pU6cOERERRERE8PTTTzs4cpXSGGP2i8gj+xp1PwaVrk2aNIlSpUpx9OhRgBSbFC5dusQnn3xC4cKF+fbbb2nWrBlHjx5l4cKFdiWFe18Ivby8WLNmjW27z6xZs2pSUDr4rNK3li1bcu3aNV544W9LcqUIFy5cYPjw4UyZMoXY2Fjeeust+vXrl6w7mqZOncqCBQvYvHkz3t7ehISEkDlzZgdGrVI7TQwq3bm3js/MmTPJmzcvAwYMcHdIxMbGcvLkSY4ePUpISAhHjx7l6NGjnDhxAhHhnXfeoW/fvjz//PN21R8VFYWHhwceHh5kzZqVHDlycPv2bZ566ilNCupvNDGodOfs2bOEhIQQFhbm8s3mn5QA7m1kY4yhaNGi+Pn58cYbb/D+++8nq4vrzz//5MUXX2TIkCG8++67vPXWW7z11luO+pNUGqSJQaULZ8+e5erVqwQEBPDhhx/SvXt3p244fy8B3P/hHxISwokTJ4iNjQUeTABNmjTBz88PPz8/SpQoQZYsWZJ1fovFwpkzZ3j++ecpWLAgr7/+OsWLF3fEn6bSAb0rSaV5IkK1atUIDw/n8OHDdu9V/ChJSQClS5e2ffiXLl2akiVLJjsBPM57773H+vXrOXXqlNPOoVK3J92VpC0GlWbFxcVhjCFjxoxMnz6dzJkz250UkpoAGjdu7JIEcI+IsHnzZqpUqUL27Nnp0qUL9evXx9vb26nnVWmTthhUmhQZGclrr71G3bp1GTRoUJJeKyJs2rSJXbt22RLBwwmgSJEiD3z79/Pzc0kCeJyQkBD8/PwYMWIEvXv3dksMKnXRFoNKdzJnzkzZsmWT3K8eHR1Nz549mTZt2gMJoFGjRrZE4M4EcL/g4GAOHz5Mhw4dKF26NKtXr+bVV191d1gqDdAWg0ozLBYLw4YNo0OHDhQsWPCfX/CQixcv0qJFC3bt2kW/fv3o168fWbNmdUKkjtG+fXu2bdvG6dOnnTqQrtImnfms0oXQ0FD+85//MH/+/CS/dvfu3VSqVIlDhw6xZMkShg4dmuKSwsWLF+ncuTNnz54FYOTIkRw5ckSTgnI4TQwq1Tt9+jQAhQsX5vDhw/Tp0ydJr//++++pXbs23t7e7Nq1i5YtWzojzGSLi4tj8eLF7N69G7Du+ubqeRgqfdDEoFK1jRs38sILL9j2TChcuHCiXxsTE8MHH3xAp9yzDHYAACAASURBVE6dqF27NkFBQZQtW9ZJkdpn7NixdOrUCYCCBQty/vx53nzzTTdHpdI6TQwqVatduzb9+vXjpZdeStLrrly5Qr169Zg4cSKffvop69atI1euXE6K8skiIyNtP0+ZMoWaNWvaFrk7c+YM0dHRWCwWAHx8fNwSo0pfNDGoVGfXrl00btyYu3fv4uXlxZdffkm2bNkS/fqgoCAqVapEUFAQ8+fPZ/jw4Xh4uOYGvcjISHbs2EFMTAwAkydPJnv27Ny6dQuwbgmaJ08e7t69C1jHEebOnUvGjBldEp9SoIlBpUI3btzgt99+48KFC0l+7Zw5c6hZsyYZM2Zkx44dtG3b1gkR/uXy5cuMGzeO8+fPA9btQGvWrMmhQ4cAqFKlCgMHDrS1CDp06MDKlSttt8NqQlBu8bgdfFLLQ3dwSx9u3LghmzdvtpWjo6OT9PrY2Fjp1auXAPLyyy/L1atXHR2iiIhcvHhR3nvvPfnll19ERCQ4OFgAWbRokYiIXLlyRVatWiVhYWFOOb9SicUTdnDTFoNKFXr16kXz5s0JCwsDSNItmteuXaN+/fqMHj2aDz/8kA0bNpAnTx67Y4mOjubGjRsAREREUKFCBcaPHw9YxwDWrl1ru6XUz8+P0NBQWrVqBUDevHlp0qQJ2bNnt/v8SjmbXYnBGDPBGDMr4WedaqmcQkSIjo4G4JtvvmH9+vXkyJEjSXUEBwcTEBDAjh07mD17NqNHj8bT0zPJdezdu9cWk6+vL1988QVg3fGsVKlSPPPMM4A1MVy8eJH27dsD4OHhga+vL8aYJJ1TKXeyd8QtBric8PO/gI2OCUcpKxGhffv2WCwWFixYQP78+cmfP3+S6li4cCHvvfceuXPnZvv27QQEPHKS599ERESwatUq2rVrB0CnTp3ImTMnmzZtwhjDkCFDHlhq4+EJdZoEVGpnb2KIBHIYYzyB5xwYj1KA9cO1fPnytts2k8JisdCvXz+GDx9OzZo1Wbp0qe0bfWL06tWLjRs30qZNGzJkyMCUKVMeuJW1S5cuSY5JqdTErrWSjDEeQCDgD6wWkR8cHVhi6VpJaYeI8OWXX9KgQQOqVKliVx03btygbdu2bNy4ke7duzNq1KgkLxlx6dIlfv31Vxo0aGBXDEqlBs5YK+kF4AzwpTuTgkp9du3axfbt223l2rVr0717d8DaSpg2bRrTpk2zq+7Dhw9TuXJltm3bxrRp05gwYUKik8LatWvp3r07IkK+fPk0Kah0zd6upMHAEiDQGFNIRN5xYEwqFQsLC+Pq1au2Teu/+OILwsLCGDduHAAfffQRWbNmZfPmzQDUqlWL5577qzfy6NGjdt2xs2zZMt555x2yZ8/Otm3bqF69epJef+DAAfbs2cPt27eTPMCtVFpjb4thk4gsFpGBqTop1KkDs2ZZf46NtZbnzbOWIyOt5UWLrOWwMGt5+XJr+do1a3nNGmv50iVref16azk01Fr+6Sdr+fRpa/nnn63l48et5Z07reUjR6zlffus5eBgazk42Fret89aPnLEWt6501o+ftxa/vlnazlhQTl++slaDg21ltevt5YvXbKW16yxlq9ds5aXL7eWE24HZdEia/necg3z5lnLsbHWDWtmzYI6dVixYoX1Dp1p06BePTp16kTDhg1h4kRo0IDIyEgiIiJgzBho0oTp06czc+ZMGDkSWrRgyJAhBAYGwjffQJs25MiRwzp4+9VXkHBnDwADB0LHjn+V+/aFwEAsFgtffPEFZ1q2ZLaPD0FBQdak0KuX9XHPBx/A/RvYBAYS36cPoQnX54vTp9n96qt/JYX27a0x3NOmjTXGe1q0sP4N9zRpYv0b72nQwHoN7qlXz3qN7tH3nl3vPcD23rNJeO/ZJLz3bBLeezYJ7z2bhPeeTSLfeza9e1vfX/ck4r1H375/lTt2tJ7jnqS+95zA3sTwojFmuTFmmjHmY4dGpFKUY8eOMWnSJNsg8JAhQ8idOzfx8fGAtWto7ty5tud79uzJyPvetN999x0zZsywlcuUKfNACyE5omNiaNq0KUOHDqVs2bI0a9aMZ599NtGv//HHH6lVqxbh4eEYSPJtrEqlWY+b+fakB9A74b8eQHl76nDUQ2c+O9aqVavEz89Prl27JiIiY8eOFUAuXrwoIiKbNm2SQYMGyd27d0VEJC4uzi1xhoSEyAsvvCAeHh4yadIkiY+PT3IdQUFBMm3aNCdEp1TKhxNmPjcyxvQEiorIQYdkKOUWoaGhNGnShIMHrf+MOXLk4Pnnn7d2/2DdJezixYu22z3r1avHv//9b9sm8+5Yy2f16tVUrVqVW7dusWXLFrp27ZrouQP79u1jypQpAFSqVMm2pLVS6i/2JoY3gZPAG8aYRN9CYox5zRhz3Bhzyhjz+SOer2OMCTPGBCc8Bj6qHpV8ktD14+Pjw9GjRzl58iRgvUto5cqVtu6enDlzki9fvhQxaSs+Pp7BgwfTtGlTSpQoQVBQELVq1UpSHWPHjmXEiBG21UuVUn9n711JHwClsE50+zoxLzDGZAQmAK8A54B9xpjVIhLy0KH/E5FGdsalEmH06NFs27aNFStWkDNnTo4fP+6yZaftdfv2bd5++21WrVrFO++8w+TJk22tln8iIkRGRpI1a1amTJlCREQEmTNndnLESqVe9rYYcopIK6Az8H+JfE0V4JSInBaRGGAh0NTO86skslgstlZChgwZ8PT0tH1rTulJ4cSJE1SrVo0ffviBMWPGMHPmzEQnBYAePXrQoEEDYmJiyJIlC3nz5nVitEqlfvYmhhhjTEVAgMTumF4ACL2vfC7hdw+rbow5aIz50Rjj96iKjDGBxpggY0zQ1atXkxR4enT27FkqVKhg2/6yZ8+eLFmyxLbmf0q2bt06qlSpwtWrV9m0aRP/93//l+RurVq1alGnTh2960ipRLI3MfQH6gJTgUWJfM2j/m9+eD2OA0AhESkPjANWPqoiEZkqIgEiEqDf/h7vXovg2WefxdfX1/bBmBLGC/6JiPCf//yHRo0aUbRoUYKCgnj55ZcT/fpTp06xdetWANq0acOXX36ZKv5upVICe/sQOonICABjzFOJfM05oOB9ZV/ggS24ROT2fT+vM8ZMNMbkEZFrdsaZbg0ePJhFixZx8OBBPD09WbdunbtDSrTw8HA6duzI0qVLadu2LdOnT09y66Zbt278/vvvHD9+XFsKSiWRvYmh0H0/9wX6JOI1+4DixpgiwHmgDdDu/gOMMfmAyyIixpgqWFs01+2MMd25ffs23t7eZMqUiYCAAMLDw4mNjU1VH4zHjx+nVatWHD16lJEjR/Lxxx8n6Zu+iGCMYebMmURFRaWqv12plMLexJDBGFML2AnkTswLRCTOGNMD2ABkBGaIyFFjTNeE5ycDLYFuxpg44C7QRu6NmKonOn/+PBUqVGDAgAH07NmThg0bWpemSKEsFgunTp0iODj4gcelS5fImTMn69ev55VXXkl0fSLCoEGDuHLlCpMmTcLX19eJ0SuVttm77HZGoBtQAVgpImscHVhi2b3sdq9ef60Fk0oJEB0Vhbe3NwKcPn2ap/PmJVu2bO4O7QEWi4WIiAjCw8P/ekRE2JbVMFh3QvPx8cHHx4c8efPi7eWV5POcPnOGmJgYSrzwgo4nqPTB3x9Gj7brpU9adtveFsN/gTisn021ALclhvTs9OnTXLx4kapVq+Lp4UGxokXdHRIxMTEPJoDwcCLvm0zmkTEjPj4+PJs/vy0RZMmShQwZ7LsPIiY2FktcHJkzZ6ZIkSLAo+9yUEolnr2JYZeIjAEwxiSqKynFsTPLutsff/xBjhw5yJkzJxGHDnF4xw6qduoELu5Lt1gsnDx58m9dQZcvX7YdU7hwYfxr1KB8+fL4+/vj7+9PoUKFHPZtXkR4qXp1Iu/eJXjXLruTi1LqQfYmhqbGmHhgg4iccGRA6vGuXr1K6dKl6d69OyNHjqRcuXKUK1fO6eeNiIjg8OHDDySAQ4cO2W6H9fT0xM/PjwYNGtgSQLly5ciZM6dT4zLGMHr0aDJkyKBJQSkHsneMoQBQPuFRTETcthJZWt/aMyYmhp07d1InYf35mTNn8sorrzhtcPXSpUt/awWcOHHCNmv6qaeesn3433uUKlUqydtnJsfUqVOJj4+na9euLjunUmnNk8YYkpwYjDFfYr2rKBgIFpGTyQ/Rfmk9MfTr148RI0Zw5swZpyWDW7du0b9/f5YtW/b3rqCHksBzzz3n1oFdEaFp06aICKtXr9ZBZqXs5NDEkFDhM1jvSPLH2mLonLwQ7ZcWE8POnTt55plnKFasmO0bfP369Z3yIbh8+XJ69OjB5cuXefPNN6latSr+/v6UL1+ep55K7NxF54uIiCAuLo4cOXIQGRlJpkyZUvwaT0qlZMlKDMaYTkAzYBmwAPgYa4thpYgcdnCsSZbWEkNYWBi+vr60bt2a77//3mnnOX/+PD169GDlypX4+/szffp0KlWq5LTzJUd8fDw1atQgR44c/Pjjj9pKUMoBnpQYEjNi1xv4HKiKdfbyC8BlYJwxJvXu95yCXLt2jWkJ+wHnyJGDtWvXMnbsWKecKz4+nkmTJlG6dGnWr1/PsGHD2Lt3b4pNCmBdDbZ79+707NlTk4JSrvC4rd3uPYBDCf/1BsIBr4SyF7D3n17v7Eda2Npz2LBhkiFDBjlx4oRTzxMSEiI1atQQQOrWrSunTp1y6vmSa/Xq1bJ161Z3h6FUmkQyt/ZcYYxZBTQAuotIdMLvY4E8jk1T6cemTZvYvXs3YF0G+9ChQxQvXtwp54qOjmbw4MH4+/sTEhLCzJkz2bRpE8WKFXPK+RwhLi6O/v3785///MfdoSiV7iRq8NkY8yrQBKiEdQ+Fk1hbDJmB9sBxEYl3YpyPlRrHGGJiYihevDgVK1ZkxYoVTj3Xzp076dy5MyEhIbRt25bRo0fz9NNPO/WcyREXF4cxhowZM3Lu3Dly5cqVKvaNUCq1Se4YAyKyUUR6iEh1rCurfoB1m86fgDHAGUcF61J16sCsWdafY2Ot5XnzrOXISGt5UcJ2E2Fh1vLy5dbytWvW8pqE1UAuXbKW16+3lkNDreWffgIgKiSE8y+8QPzWrWTKlInNEyey7Pp12LnTevyRI9bj9+2zloODreV76znt22ctHzliLe/caS0fP24t//yztXz6NAARq1ZxokAB2tSowZ07d9gzeDDzL1zg6YT1iVizxnr8tYQVzZcvt5bDwqzlRYus5chIa3nePGs5NtZanjXLWr5n2jSoV++v8sSJ0KDBX+UxY6BJk7/KI0dCixaANVHyzTfQpg2vvPIKH3/8MXz1Fb6ff/5XUhg4EDp2/Ov1fftCYOBf5d694YMP/ir36mV93PPBB9Zj7gkMtNZxT8eO1nPc0749fPXVX+U2bawx3tOihfVvuKdJE+vfeE+DBtZrcE+9etZrdI8L33ucPm0t//yztXz8uLXspPceP/1kLYcm7Mu1fr21fOmStZyC3nuA7b1n89VX1n//e1L6e88JkjxdNKF76piILBCRPiLyqogU+udXpm9btmzh5MmTHDhwAIDnn3/eabN1V69ezfvvv8+FCxfo2LEjR48epUqVKk45V1LFx8dz/vx5W7l169a8+uqrtrK/vz8VK1Z0R2hKqXseN/iQWh4pefA5JCRENm7cKCIi8fHxsnfvXqee7+LFi9KyZUsBpGzZsrJ7926nni8xbt++LTt27LCVO3XqJPny5ZP4+HgREZk+fbqMGjXKXeEplW7xhMFnt3+wJ/eRkhNDzZo1pVSpUmKxWJx6nvj4eJk2bZo89dRT4uXlJUOHDpWYmBinnvNxLl26JPPmzbOd/6uvvhJAbt68KSIi27Ztk5kzZ0pcXJxb4lNKWWlicBGLxSLz5s2TO3fuiIjIiRMn5MqVK0495/Hjx6V27doCSO3ateX48eNOPd/Dzp8/LyNGjJBz586JiMiiRYsEsLWOTp06JevXr5eoqCiXxqWUejJNDC6yf/9+AWTcuHFOP1dMTIwMHTpUvLy8JEeOHDJ16lSnt0xERK5evSqffPKJ7Nq1S0REgoODBZClS5eKiMitW7ckODhYWwRKpXCaGJzoypUrsmbNGlv5l19+cfoH9J49e6Rs2bICSMuWLeXChQtOO1dUVJS0a9dO5syZIyIid+7ckaxZs8qUKVNERCQuLk4uXbrktPMrpZzjSYlBF7FPpj59+tCuXTtu374NQK1atZx2t1F4eDi9evWiWrVq3Lhxg5UrV7JkyRLy58/v0PM0a9aMzz77DAAvLy9+//13riXcWujj48OtW7cITLhdL2PGjDzzzDMOPb9Syr00Mdhh27Zttlsuv/rqK/bs2UP27Nmdes5169bh5+fHmDFj6NatGyEhITRt2tQhdW/fvp0+ffrYyr6+vuTNm9dW3r17Nx999JGtrKuaKpW2aWJIoqtXr9KgQQOGDRsGQIECBShVqpTTznflyhXatWtHw4YNyZo1K9u3b2fChAkOTUQ//fQTy5Yt48aNGwCMHz+eTz/91GH1K6VSF7v2Y0hJXLEkRlRUFBs3bqRJwuzJbdu2UbVqVTJnzuy0c4oIc+bM4eOPP+bOnTv079+fzz//HC8vL4fU/+eff3L79m3KlClDXFwcd+/eJVu2bA6pWymV8iV7SYz0bvTo0TRt2pTjCUsA1KlTx6lJ4ffff+fVV1/l3XffpWTJkgQHBzNo0CCHJQURoVGjRrz77ruICB4eHpoUlFI22ln8GCdOnCAuLo7SpUvTo0cPKleuTIkSJZx6zri4OEaNGsWgQYPw8PBg4sSJdOnSxWGD2Xfv3sXb2xtjDNOmTSNPnjy6v4FS6m+0xfAIcXFx1KtXzzbg6uPjQ926dZ16zgMHDlClShU+++wzXn31VUJCQujWrZvDksKVK1eoWLGibQOgqlWrpuhlt5VS7qOJIYGIsG7dOlvXyn//+19mz57t9PNGRkby6aefUrlyZS5evMjSpUtZsWIFvr6+Dj1P3rx5eemllyhbtqxD61VKpUGPm+CQWh6OmuC2dOlSAeSHH35wSH2JER4eLrVq1RJAOnfubFtPyFEuX74s77zzjtOX5VBKpT7oBLdHu379Ovv37wesk7oWL15Mg/vXcXeiyMhIGjVqxI4dO1i4cCFTp07lqaeecug5Ll68yMqVK9m7d69D61VKpW3p+nbVl19+mdDQUI4fP07GjBkdHNnj3b17l8aNG7N161bmzp1Lu3btHFZ3TEwMW7dupX79+gCEhYWRI0cOh9WvlEob9HbVxxg5ciQrVqxwaVKIiorijTfeYMuWLcycOdOhSQFg2LBhvP7665w8eRJAk4JSKsnS9e2qlSpVcun5oqOjadGiBRs2bGDGjBm8/fbbDqlXRIiIiMDHx4ePPvqIihUrUrx4cYfUrZRKf9J1i8GVYmJiaNWqFevWrWPq1Kl0vH8P2WTq3r079evXJy4uDh8fHxo2bOiwupVS6U+6bjG4SmxsLG3atGHNmjVMnDiRzp07O7T+2rVrU6BAAZ2sppRyCE0MThYXF0e7du1YsWIFY8eOpVu3bsmu02Kx8M0331CyZElatGhBmzZtHBCpUkpZubQryRjzmjHmuDHmlDHm8yccV9kYYzHGtHRlfI4WFxdHhw4dWLp0Kd999x09e/Z0SL3x8fGsWrWKn376ySH1KaXU/VzWYjDGZAQmAK8A54B9xpjVIhLyiOOGARtcFZszWCwWOnbsyMKFCxk+fPgD+xnYa8OGDbz00ktkzpyZzZs34+Pj44BIlVLqQa5sMVQBTonIaRGJARYCj9pppiewDLjiwtgcKj4+nvfff5958+bx9ddfO2Rvg99++40GDRowevRoALJly6ZjCkopp3DlGEMBIPS+8jmg6v0HGGMKAG8A/wIqP64iY0wgEAjw3HPPOTzQ5IiPjycwMJDZs2czePBg+vbtm6z67ty5Q7Zs2ShVqhSrVq2yTVxTSilncWWL4VFfbx+edj0a6CMilidVJCJTRSRARALu34LS3USE7t278/333zNgwAAGDhyYrPp++OEHChUqxNGjRwFo3LgxmTJlckSoSin1WK5sMZwDCt5X9gUuPHRMALAwoYskD/C6MSZORFa6JkT7iQg9e/ZkypQp9O3bl8GDBye7zoCAABo2bEhKSn5KqbTPlS2GfUBxY0wRY0wmoA2w+v4DRKSIiBQWkcLAUqB7akkKH330ERMmTKB3794MHTrU7v7/LVu20LNnT0SEfPnyMXfuXJ5++mkHR6yUUo/nssQgInFAD6x3G/0GLBaRo8aYrsaYrq6Kw9FEhE8//ZQxY8bQq1cvhg8fnqxB4X379rFp0yZu3LjhwCiVUirx0vXqqsklIvTt25dhw4bRo0cPxo4da1dSOH36NDdv3qRSpUrEx8cTFRVFlixZnBCxUkpZPWl1VZ35nAwDBw5k2LBhdO3a1e6kICK0aNECsG7vmSFDBk0KSim30sRgpy+//JIhQ4bQqVMnJkyYYHf3kTGGmTNnkjNnTp2XoJRKEXR1VTt8/fXXDBo0iHfffZcpU6aQIUPSL6OIsGHDBkQEf39/ChUq5IRIlVIq6TQxJNHw4cPp378/HTp0YPr06XYlBbDOUXjttddYsWKFgyNUSqnk0cSQBN999x19+vShbdu2zJw5M1k7vzVs2JD58+fTrFkzB0aolFLJp4khkcaOHcsnn3xCq1atmDNnjt1JwWKxcPv2bTJkyEDbtm3tbnEopZSz6KdSIkycOJEPP/yQ5s2b89///hcPD/vH7L/77jvKli3LpUuXHBihUko5jt6V9A+mTp3KBx98QJMmTViwYAGenp7Jqq9WrVpcvHiRZ555xkERKqWUY2lieIIZM2bQpUsXGjZsyOLFix2ygF21atWoVq2aA6JTSinn0K6kx5gzZw6dOnWifv36LF26FC8vr2TVN2DAAL766itS+0xzpVTapy2GR/jvf//Lu+++S926dVmxYgXe3t7Jqk9EOHPmDFmyZNFJbEqpFE8Tw0MWLVrE22+/TZ06dVi1ahWZM2dOdp3GGObNm0dcXJwDIlRKKefSrqT7LFu2jLfeeouaNWuyZs0ah6xZNH78eM6fPw+QrLuZlFLKVTQxJFi5ciVt2rShWrVq/PDDD2TNmjXZdYaGhtKnTx+mTp3qgAiVUso19Css1uUpWrduTUBAAOvWrSNbtmwOqbdgwYIcOnSIggUL/vPBSimVQqT7FsOPP/5IixYt8Pf3Z/369WTPnj3ZdYoI+/fvB6BYsWK6T7NSKlVJ14lh48aNvPHGG5QpU4YNGzaQI0cOh9S7dOlSAgIC2LRpk0PqU0opV0q3iWHr1q00bdqUUqVKsWnTJnLmzOmwuhs3bsy4ceP417/+5bA6lVLKVdJtYsiXLx8vvfQSmzZtIleuXA6p02KxEBMTg7e3Nz169EjW6qtKKeUu6TYxlCpVig0bNpAnTx6H1Tlq1CiqVavGzZs3HVanUkq5WrpNDM7wwgsvUKlSJZ566il3h6KUUnYzqX3tnoCAAAkKCnJ3GEoplaoYY/aLSMCjntMWgwMMHTqUWbNmuTsMpZRyCE0MyRQXF8eWLVvYvn27u0NRSimH0JnPyeTh4cGmTZuIjo52dyhKKeUQ2mJIhjlz5tj2b3bEKqxKKZUSaGKw08mTJ+nYsSPjxo1zdyhKKeVQ2pVkp+LFi7Nnzx7Kli3r7lCUUsqhtMWQRCLC77//DkBAQECyt/xUSqmURhNDEs2fP5+SJUuye/dud4eilFJOoYkhiV577TUGDBhA5cqV3R2KUko5hc58TqT4+HiMMRhjnH4upZRyNp357ACjR4/m9ddfJyIiwt2hKKWUU7k0MRhjXjPGHDfGnDLGfP6I55saYw4ZY4KNMUHGmJqujO9JfHx8yJkzJ1myZHF3KEop5VQu60oyxmQETgCvAOeAfUBbEQm57xgfIEJExBhTDlgsIiWfVK8uoqeUUkmXUrqSqgCnROS0iMQAC4Gm9x8gIuHyV6bKCrh9AOTbb79lw4YN7g5DKaVcxpWJoQAQel/5XMLvHmCMecMYcwxYC7z3qIqMMYEJXU1BV69edUqwANHR0cyaNYvFixc77RxKKZXSuHLm86Nu5/lbi0BEVgArjDEvAV8B9R5xzFRgKli7khwcp42Xlxd79+7FYrE46xRKKZXiuLLFcA4oeF/ZF7jwuINF5BegmDHGcXtvJsGqVauIjY0lc+bM+Pj4uCMEpZRyC1cmhn1AcWNMEWNMJqANsPr+A4wxz5uEiQLGmIpAJuC6C2ME4ODBgzRr1ozx48e7+tRKKeV2LutKEpE4Y0wPYAOQEZghIkeNMV0Tnp8MtADeNsbEAneBN8UNM/DKly/Pjz/+yMsvv+zqUyullNvpzOf7iAhXr17l6aefdkh9SimVUqWU21VTvHnz5lG8eHGOHDni7lCUUsptNDHcp2bNmrz33nuUKlXK3aEopZTbaFcS1i4kXRxPKZWeaFfSPxg9ejTvv/8+MTEx7g5FKaXcThMDcPv2bW7evImnp6e7Q1FKKbfTPZ+BQYMG2fZbUEqp9C5dtxgmTZrEwYMHAciQIV1fCqWUskm3n4YRERF8/fXXOrtZKaUekm67krJmzUpwcLCOKyil1EPSbWIAyJ07t7tDUEqpFCfddiUppZR6NE0MSimlHqCJQSml1AM0MSillHqAJgallFIP0MSglFLqAZoYlFJKPUATg1JKqQek+v0YjDFXgbPujiMFyANcc3cQKYhej7/otXiQXg+rQiKS91FPpPrEoKyMMUGP23QjPdLr8Re9Fg/S6/HPtCtJKaXUAzQxKKWUeoAmhrRjqrsDSGH0evxFr8WD9Hr8Ax1jUEop9QBtMSillHqAJgallFIP0MSQyhhjXjPGHDfGnDLGfP6I598yxhxKeOw0xpR3R5yu8E/X4r7jkqNzSQAAC69JREFUKhtjLMaYlq6Mz9UScz2MMXWMMcHGmKPGmJ9dHaMrJeL/lRzGmDXGmIMJ16OjO+JMkUREH6nkAWQEfgeKApmAg0Dph455EciZ8HMDYI+743bXtbjvuC3AOqClu+N283vjKSAEeC6h/LS743bz9egHDEv4OS9wA8jk7thTwkNbDKlLFeCUiJwWkRhgIdD0/gNEZKeI3Ewo7gZ8XRyjq/zjtUjQE1gGXHFlcG6QmOvRDlguIn8CiEhaviaJuR4CZDPGGMAHa2KIc22YKZMmhtSlABB6X/lcwu8e533gR6dG5D7/eC2MMQWAN4DJLozLXRLz3ngByGmM2WaM2W+Medtl0bleYq7HeKAUcAE4DHwoIvGuCS9l83B3ACpJzCN+98j7jY0xL2NNDDWdGpH7JOZajAb6iIjF+qUwTUvM9fAAKgF1gczALmPMbhE54ezg3CAx16M+EAz8CygGbDLG/E9Ebjs7uJROE0Pqcg4oeF/ZF+u3nQcYY8oB04EGInLdRbG5WmKuRQCwMCEp5AFeN8bEichK14ToUom5HueAayISAUQYY34BygNpMTEk5np0BL4R6yDDKWPMGaAksNc1IaZc2pWUuuwDihtjihhjMgFtgNX3H2CMeQ5YDnRIo98E7/nHayEiRUSksIgUBpYC3dNoUoBEXA9gFVDLGONhjMkCVAV+c3GcrpKY6/En1tYTxphngBLAaZdGmUJpiyEVEZE4Y0wPYAPWuy5miMhRY0zXhOcnAwOB3MDEhG/KcfL/7Z1/rFxFFcc/39ZKf5eUNlgiCiEoaaFppWBaCynGkFhDbaHyjCg+iAE0UitiTUAJoCg/ikRtANsGXoRSGhHKLwWktLS0BKQ/eH0tlCqUqPwIKjQUEKQc/zjnvjd72d23S31sg/NJNjs7d+bOmbnnzpkf9579AHqSbLAt/m9opD3M7AlJ9wCdwDvAYjPrap3UfUeD+vFjoEPSZnzp6Qdmlt1xk11iZDKZTKZEXkrKZDKZTAXZMGQymUymgmwYMplMJlNBNgyZTCaTqSAbhkwmk8lUkA1DA0j6qKTbJW2X9LSkBZL26YNypkmakvw+q3BbIKmjL72DSloaHlm/W4qfKWls8nuVpL3q8de0bSQtLuSVdF4p3bq+LLsUf1h4Md0o6ZA9LONCSefuyTn6EkntkhZEONXZdkkHvE8ydOulpN9L2rdO2rnxHket46kO7WpSjgmSpie/Z9Tz/Lu3kg1DL4SDrVuB5WZ2KHAo7k7g8j4obhruHRXofvb8N31QTgWSPgJMMbPxZnZV6fBMYGyVbHslZvYNM9saP88rHZtSJUtfMRO43cwmmtlf3sdyW0pJZ9uBpgyDpD1+t8rMppvZK3WSzAWqGgZJ/Us61CwTgG7DYGZ3mNml7/FcraPV7l339g/+ZuTqUtxw4GXcI2M7sCA5dhcwLcLXAI8BW4CLkjQ7gIuADbjzrsOAg4AXgL/j/luOAS4Ezo08HYTbaNzfzYPAevwFnjERPwd3q9wJ3FylLgOB66PMjcBxEd8JvFGUm6SfgnucfCaOHQKsAi7D3QY8VaTHXyK6An/jtBM4s0Z7nhrHHwduiLiPAysifgU9bqE7gF8C6/A3Uov6C3eAthW4m8Sldsg3CbgU2B1yL4lju5L8VwBd0RZtET8t8t8CPAksoeddnwuibl34fwarfF2SOk5PruXKiDsn8nYBc5O0teLPB7YB9wNLCz0olTMa9xz7p/h8JuJvB06N8JlJ/Vfh/qPWRXlHR/wQ4Lo4x0bgixHfjg+K7gG2A5cnZZ8W1/9BYBFxDxA6C8wGdkUdNuGDqR3AqEg3CViV5FkI3AfcVKtepboPwj2mdgLLgEeAScn9NSrqdTeua11AG36PvBXXvbg2u4CL4xxTo50mJceuxO/VFcDoVM8iPCrK/DD+NvVLUec2kv6BJvW8pf1eqwXY2z+hSFdVid+Ijw66L3zEp4ZhZHz3D0Uanyju2RH+Fv4GavdNlZyr+3coz2xgQChQoaBt+Fud4L5g9onwvlVk/h5wfYQPCyUeiBulrhr170gVNepxZYSnA/dH+AzghxHeBzeIB5fONQ7vKEaV2udO4OsRPh2fnRVl/xaf2Y7F3SgDnAj8Mdr1AOAVSoYhwrtK5ReG4aQk//7RDmNww7AT96vTD3gYmJrKGuEbgBOqtU+Na3ck3hENwQcTW4CJDcQPxgchf6a6Ybgpke9jwBMR3j/yHIN33iOTtlkU4WOLaw78FPhqoTeRZwiu208DI3A9eRb3PzQm2mw03hmupWQYytci0ftahmE9MKhevUp1P4cevR+Pu8suG4aTivpG/IiyHPHbgJNLOj4pOXZKhC9I6pmmGQXsiHA7lf1Be5KnKT1v5Se7xOgdUd2DaSPuOk+WdAbuemQMftE749it8b0e7+ga5ZPA4bgnSPDO7fk41gkskbQcqOYTaCrwKwAze1LSs7gr5ma9SaayHxTh44HxyXr7CHzZ7Zkk32eBWyzcDpjZvyJ+Mj1tcAOVy3TLzV0hbw1/NuCd2lIz2w08J+mBJuWfmuR/Mf7J7Ci8HR41s78BSNoU9XsIOE7SPLyzHol34nc2Ud5t5s7rkHQr3mmrRny/iH894ss+fgo+B4xNPMcOlzTMzF6UdAGwEpiVtDP47AMzWy1peKzFHw/MSPYxBuIdMsAKM9sZcmzFR72j8E79pYhfhuvRnnCHmb3RS71eTdIfi4+yMbNOSZ28m83AfEmXAXeZ2ZoaZe/GZyjVeAefkQDcSI/uvxea1fOWkQ1D72zBRx7dSBqOj8q24Z10ulczMNIcjE+pjzKzlyV1FMeCN+N7N81dBwFbzGxylWNfwG+YGcCPJI0zs7dLef8XVJNd+Czo3jr5ahnZMmmaN5OwaqRplnrtkJa3G/iQpIHA1fgI8a+SLqTyWr7X8urJ0Uj9+gGTkw415Qjgn7x7jb98Xgs5TjKzbRXCSZ+mSns0IV+Zt+m5V8rt91oSrlevlLoymNlTko7EZ7Y/k3SfmV1cJem/Y5DQCEWZ9erSKI3oeUvIm8+9swIYnDxp0R9fc1wQirsDmCCpn6QD8X+OAl8CeA3YGSOAzzdQ1qvAsF7SbANGS5oc8gyQNE5SP+BAM1sJzMOXBIaW8q4GTol8n8BHhduoTyMyge91fFPSgOL8koaU0qzAZ1H7RZqREb8O935JyPdQL2WtBr4sqb+kMcBxNdL9p5CnSv62yD8aN6b1XC0XN/4/JA3Fl/SaYTUwU9LgaJNZwJpe4mdJGiRpGHBCjfPeB3y7+CFpQnwfjevbRODcGKQUtEWaqcDOmA3cC5wdD1ogaWIv9XkEmCZpv2jfL9VIV9adHfgyGZQGW43Uq0Sqy4fjy0kVxBNRr5vZjcB84FM15KpHP3qu91fo0c0d9NQl1Yd6525Wz1tGNgy9YL4IOAuYLWk7Pgp7x8wuiSRr8eWSzbjybYh8j+P7EFvwjb21DRR3J94hbJJ0TA153sIV8TJJj+ObXFPwJaUb5Z4iN+L7IuUnM64G+keaZUC7mb1JfW4Gvt/AY5eL8c3gDZK6gF9TmgmZ2RbgEuDBkP3ncWgOcFosB3wN+E4vMt2Gb4Zuxjf4a/2p/UKgU9KSKvmLDfAHgHlm9kKtwqIdF0V5y/EN0YYxsw34OvKjeKe62Mw29hK/DL+2v8ONRTXmAJPkjxlvBc6SP0a9CDjdzJ7D95WuKzp94OV4bPda/I+cwL2MDsDbqit+16vP8/i+wMP45viGGkk7gGtDnwfhD1z8QtIafPZRi3fVq0qaa4ChoTPzqG7YjwAejSXB84GfRPxC4A+SVtaRoeA1YJyk9fhSaDHjmI8PhNbhS2sFK/FlsE2S2qrUqxk9bxnZu2qTyN8zWAqcaGbrWy1PJtMoklbhG8OPtVqWzN5N3mNoEjNbh2/AZTKZzAeSPGPIZDKZTAV5jyGTyWQyFWTDkMlkMpkKsmHIZDKZTAXZMGQymUymgmwYMplMJlPBfwFG/wjRlR8skAAAAABJRU5ErkJggg==\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": "3.8.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
