{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Phillips Curve in America\n",
    "\n",
    "### Python Programming and Data Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/delong/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/delong/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "# import libraries...\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "\n",
    "import copy\n",
    "import itertools\n",
    "from itertools import chain, combinations\n",
    "from linearmodels.iv import IV2SLS\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as scipystats\n",
    "import seaborn as sns\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.graphics.regressionplots import *\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "import statsmodels.stats as stats \n",
    "import statsmodels.stats.stattools as stools\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "phillips_curve_df = pd.read_csv(\n",
    "    'https://delong.typepad.com/files/2019-10-23-phillips-curve-annual-1.csv'\n",
    "    )\n",
    "\n",
    "phillips_curve_df['year_index'] = phillips_curve_df['year']\n",
    "phillips_curve_df.set_index('year_index', inplace=True)\n",
    "\n",
    "phillips_curve_dict = {\n",
    "                        'df': phillips_curve_df, \n",
    "                        'csv_url': 'https://delong.typepad.com/files/2019-10-23-phillips-curve-annual-1.csv', \n",
    "                        'fred_url': 'https://research.stlouisfed.org/useraccount/datalists/230761',\n",
    "                        'Title1': 'Consumer Price Index: All Items Less Food and Energy: All Urban Consumers', \n",
    "                        'Source': 'U.S. Bureau of Labor Statistics: Consumer Price Index', \n",
    "                        'Note': 'The \"Consumer Price Index for All Urban Consumers: All Items Less Food \\\n",
    "                            & Energy\" is an aggregate of prices paid by urban consumers for a typical \\\n",
    "                            basket of goods, excluding food and energy. This measurement, known as \"Core \\\n",
    "                            CPI,\" is widely used by economists because food and energy have very volatile \\\n",
    "                            prices. The Bureau of Labor Statistics defines and measures the official CPI, \\\n",
    "                            and more information can be found in the FAQ: \\\n",
    "                            (https://www.bls.gov/cpi/questions-and-answers.htm) or in this article: \\\n",
    "                            (https://www.bls.gov/opub/hom/pdf/cpihom.pdf)', \n",
    "                        'Seasonally Adjusted': True, \n",
    "                        'Title2': 'Unemployment Rate',\n",
    "                        'Source': 'U.S. Bureau of Labor Statistics: Current Population Survey', \n",
    "                        'Note2': 'The unemployment rate represents the number of unemployed as a percentage \\\n",
    "                            of the labor force. Labor force data are restricted to people 16 years of age and \\\n",
    "                            older, who currently reside in 1 of the 50 states or the District of Columbia, \\\n",
    "                            who do not reside in institutions (e.g., penal and mental facilities, homes for \\\n",
    "                            the aged), and who are not on active duty in the Armed Forces. This rate is also\\\n",
    "                            defined as the U-3 measure of labor underutilization.'\n",
    "                        'Seasonally Adjusted': True, \n",
    "                        }\n",
    "\n",
    "# phillips_curve_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "\n",
    "# The Phillips Curve in America\n",
    "\n",
    "In the United States betwee 1957 and 1988—the first half of the last 60 years—the slope of the simplest-possible adaptive-expectations Phillips Curve was -0.54: each one-percentage point fall in unemployment below the estimated natural rate boosted inflation in the subsequent year by 0.54%-points above its contemporary value. Since 1988—in the second half of the past 60 years—the slope of this simplest-possible Phillips curve has been effectively zero: the estimated regression coefficient has been not -0.54 but only -0.03.\n",
    "\n",
    "The most important observations driving the estimated negative slope of the Phillips Curve in the first half of the past sixty years were 1966, 1973, and 1974—inflation jumping up in times of relatively-low unemployment—and 1975, 1981, and 1982—inflation falling in times of relatively-high unemployment. The most important observations driving the estimated zero slope of the Phillips Curve in the second half of the past sixty years have been 2009-2014: the failure of inflation to fall as the economy took its Gtreat-Recession excursion to a high-unemployment labor market with enormous slack.\n",
    "\n",
    "I remember September 2014: That month the U.S. unemployment rate dropped below 6%, and I was assured by very many that that meant that the Phillps Curve predicted that inflation would soon be on the rise, and that it was time for the Federal Reserve to begin to—rapidly—normalize monetary policy—to begin shrinking the monetary base, and raising interest rates back into a \"normal\" range. Today unemployment is 2.5%-points lower than what I was then assured was the \"natural\" rate of unemployment. According to the rule-of-thumb as they stood back when I was an assistant professor in 1990, such a low unemployment rate should lead annual inflation to climb by 1.3%-points every year: if this year inflation were to be 2.0%, next year's would be 3.3%, and—if unemployment stayed this low—the year after that's would be 4.6%, and the year after that 5.9%.\n",
    "\n",
    "But that is not going to happen. Inflation will stay about 2% for the next several years. The old rule-of-thumb no longer applies. And that should drive our monetary policy choices.\n",
    "\n",
    "Now the conventional wisdom among economists as it stood back in 1990 was correct, for then. The fact is that in the United States between 1957 and 1988—the first half of the last 60 years—the slope of the simplest-possible adaptive-expectations Phillips Curve was -0.54. But that was then. This is now. Since 1988—for the second half of the past 60 years—the slope of this simplest-possible Phillips curve has been not -0.54 but only -0.03. Even as unemployment has gotten far below what economists presumed was the natural rate of unemployment recently, inflation in the United States has not accelerated. And even when unemployment got far above what economists presumed was the natural rate of unemployment over 2009-2014, inflation did not fall and deflation did not set in. The estimated regression-analysis coefficient of -0.03 is telling us that as unemployment has fluctuated since the late 1980s, inflation has not. By contrast, in the U.S. from the late 1950s to the late 1980s, when unemployment fluctuated inflation responded.\n",
    "\n",
    "Nevertheless, there are many people who believe that an acceleration of inflation is a significant risk that monetary policymakers need to focus on—that the more important danger is of an outbreak of higher inflation rather than the possibility of recession. The very sharp Peter Hooper, Frederic S. Mishkin, and Amir Sufi, for example <https://delong.typepad.com/files/phillips-hibernating-1.pdf>, argue that the American Phillips Curve is \"just hibernating\", and that the estimates that show the past generation's Phillips Curve as essentially flat are untrustworthy because of \"endogeneity of monetary policy and the lack of variation of the unemployment gap\". But this I do not understand: the computer tells us that the 1988-2018 estimates are three times as precise as the 1957-1987 ones, and standard specifications of the Phillips Curve slope look at too short a window for any monetary policy reaction to be substantial.\n",
    "\n",
    "Yes, an outbreak of inflation could be a threat. Yes, if we had analogues of (a) two presidents, Johnson and Nixon, desperate for a persistent high-pressure economy; (b) a Federal Reserve chair like Arthur Burns eager to accommodate presidential demands; (c) the rise of a global monopoly in the economy's key input able to deliver mammoth supply shocks; and (d) a decade of bad luck; then we might see a return to inflation as it was in the (pre-Iran crisis) early and mid-1970s. But is that really the tail risk we should be focused on? \n",
    "\n",
    "It is long past time to take seriously what the data are telling us: until the structure of the economy and economic policy changes again, there is little risk that over the next five years we will find ourselves facing a painful excessive-inflation problem. Monetary policymakers should be focused on other problems, and other risks. \n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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6WqDBWnsZ8MfAp3NvNMbsAe4Ftuf7GBGRUhiZjNLaVIff58Xr9dAUDGj7cRERyXIzob4c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\nqcD/AEqayGjsmIDPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1081006a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve_df.plot(x='year', \n",
    "    y='unemployment_rate', kind='line')\n",
    "\n",
    "plt.title(\"The Unemployment Rate\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"The Unemployment Rate\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ReU5AvdQMNcCmnlbGUlmmM83/PIiIyMqhgFrEx9InBdSTK6DkYylLXU62sbcVUNmHiIj4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\nurRCLbKoFWQm8MCNKxhyTChKT2QRWfAFdWZqHKVLUni3sUe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Tt62irDA14GO50OqidDJT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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119bfb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve_df.plot(x='year', \n",
    "    y='core_inflation', kind='line')\n",
    "\n",
    "plt.title(\"The Core Inflation Rate\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Annual Inflation Rate\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bWvsd4HJrbQx4IvAXOKdFiswrncly2737mVKRVLJoPEkgAG1N5cxoB8jmcmSz\n/uj+LjQ2Ak5HG3Q6pIiI+NN8B9b0Af8IDAOfxsnPTuBka/8EWFGLBYp/3WsHuOFnj5HL5Xjmeevq\nvRxfiMZTtDdHCAZn7/DOJ5w/hj2VydIYDFV6aRXnFtpbNnTNeZ3WokK7r2v28RIRERGvmi/e7wYg\nBvQCDcaYW4BvAC3AP9RgbeJzR0YTABweSdR5Jf4Rm0jS3dFY1m0jIafQTmeyNEa8XWhnsznsvlGW\ndzXT2zl3Ae2O0KijLSIifjTf6Mgma+1LgecDrwT+F/gmsMVa+61aLE78bTg6CcDgqArtUqQzWeJT\naTrKSBwBiOQ72n5IHtlzOEZiKj1vNxt0OqSIiPjbfB3tKIC1NpZPHXmptfau2ixLjgdDbqE9Nlnn\nlfjDUjK0wcnRBnyRpb2tMDYy93w2FBXa6miLiIgPzdfRLt5RdVhFtizWcHQKgIGxhOLZShDNJ46U\n29EuFNo+6GgXNkKuL63Q1qE1IiLiR/N1tNuNMU/DKcZb838u7NCy1v6q2osT/8rlcoWOdjKVJRZP\nlZUNfSIpHFZT5uM0PaPt7Rc16UyWx/aPsrq3lc62+efR1dEWERE/m6/Q3g98MP/n/qI/g9Ptvqxa\nixL/m5hMHxXrNzCWUKG9gGjc7WiXOToSdl4Hpz0+OrLrQJRkKrtgNxugLd/dV6EtIiJ+NGehba29\ntJYLkeOLuxEyHAqSzmQZGptk0+rOOq/K26IT+VMhj/PRkVLnswFam5xvUeP5FyEiIiJ+UsrJkCKL\n5o6NbFzdAcCAkkcWtOTRkfB0vJ+XPbpnhABg1s+fOALOi4fmxjDjiXT1FyYiIlJhKrSlKtyNkGad\nU0wpeWRhbupIuaMjER90tKdSGXYeGGP9ivbC/PVC2psjjCfU0RYREf9RoS1V4Xa0T813LZWlvTB3\nRrt9iaMjXu5o7+gfI53JzXvs+kytzRHGEykl14iIiO/MtxkSAGPMBuDvgB6OTh15fRXXJT43lO9g\nr17WSkdLhAF1tBcUiyeJhIM0NZR3qmPxEexetZj5bFd7S4R0JsdUKkNTw4LfskRERDyjlJ9a3wZ+\nnf+llpKUZDg6SSgYoLOtgd6uZvYcipHN5ggGAwvf+AQVnUjR0RIhECjvMQqH8qkjae/+N310zwih\nYIDNa0vfGFt8OqQKbRER8ZNSfmpFrLXvrMSdGWOCwDXAVmAKeKO1dkfR5VcAVwJp4Hpr7ZeNMRHg\neuAkoBHIW1XWAAAgAElEQVT4sLX2pkqsR6pnKDpJd3sjwUCA3s4mdh2IMjo+RU9HU72X5km5XI5Y\nPMnq3tayv4bXN0PGJ9PsPhhl0+pOmhtLL5gLhfZkil6aq7U8ERGRiitlRvs3xpgrjDGVCEF+EdBk\nrX0y8C7gk+4F+YL608DlwCXAm4wxK4C/AIastU8Dng18oQLrkCpKZ7KMjSdZli+q+7qc4kjJI3Ob\nSmVIprNLyhqPePwI9sf2j5LLLW5sBI7uaIuIiPhJKYX2y4AfApPGmGz+V2ahG83hIuAnANbau4Hz\nii47DdhhrR2x1iaB3wAXA98B3pu/TgCn2y0eNhybIgeF7nVvp/O7kkfmFs0Xke1lJo5A0WZIj6aO\n2L3usesLx/oV0zHsIiLiVwu+f2utXV3B++sAxoo+zhhjwtba9CyXxYBOa+04gDGmHfgu8K+l3FF3\ndwvhcHmbyiqpr6+93kuouUNjTrTfulUd9PW1c8qGHgDiqawvHo96rHEoX2iv7G0r+/57R5x3DBqa\nIp58nA/l13fematpaSr9BcXqlU4WO6HgvH8vL/6dZWF63vxJz5s/6XmrvVJSR1qA9wHPyF//duC9\n1tqJMu4vChQ/y8F8kT3bZe3AaH4N64DvA9dYa79Vyh2NjMTLWF5l9fW1MzAQq/cyam7n3mEAmsMB\nBgZiRPJ7aPccGPP841Gv52xv/ygAISj7/ifGnRc4Y9GEJx/nxw9EWdbRxERskolY6e9u5FLOt4hD\nR8bn/HudqP/X/E7Pmz/pefMnPW/VM98LmFJGR74AtAKvB14LNAD/UeZafgs8F8AYcyHwUNFljwKb\njTE9+Xnwi4G78nPaPwX+2Vp7fZn3KzXkHr/uzmj3dDQRCChLez6Fw2paKzE64r3UkfFEirGJ8jZ7\nthZthhQREfGTUrb+P9Fau7Xo478zxjxS5v19H3iWMeZOnHnr1xljXgW0WWu/ZIz5R+BWnBcA11tr\n+40xnwW6gfcaY9xZ7edYa1W1edRQ/lRId0Y7HArS096oLO15RCecw2o6yjysBqZTR7y4GfLAoPMG\n2JoyCu12bYYUERGfKqXQDhpjuqy17hhHF2VuSLTWZoE3z/j0tqLLbwZunnGbdwDvKOf+pD6GZnS0\nAXo7m3ls3yipdLZQEMq0pZ4KCUU52h4utJfU0dZmSBER8ZlSCu1PAfcYY27C6UJfAVxV1VWJrw1H\nJ2lrjtBYdMJhb1cTdp9z2YqeljquzpumR0cqEO/nwdSR/iUU2uFQkObGkAptERHxnQVbi9barwAv\nBnYBu4GXaFZa5pLL5RiKTtLT0XjU5/s681naY5r4mY07OrKkeD8PH1gz3dEu70VWW3NEhbaIiPjO\nnIW2Meb5+d9fA5yLE7c3BpyT/5zIMSYm0yRT2aPGRsDpaAMMjmpOezaxeJKWxnBhQ2M5wh7uaB8Y\nnGBZR1PZR6i7hXYu572NniIiInOZ76fe+cD/ApfOclkO+HpVViS+NpTf8DjzqPVedbTnFY2naF/C\n2AgUH8HurWLUTRw5c+Oysr9GW3MDqXSMZCp71EiSiIiIl81ZaFtr35f/47estT8rvswY85Kqrkp8\na7aNkFB0OqQ62sfI5nLE4klWdDcv6eu4M9peGx1ZSuKIq63Z+VYVSyRpbFja4yQiIlIrcxbaxpiX\nA43AB40xV864zb8A36vy2sSHCoV259GFdld7I+FQgEF1tI8xkUiRyy0tcQQgGAwQDAQ8NzqylMQR\nV1uz89hMJNL0dlZkWSIiIlU33+hIB/AUnBMai8dH0sB7qrko8S/3sJqZmyGDgQDLOpoYUEf7GFE3\ncWQJGyFd4XDAcznaFSm0849NLJGsyJpERERqYb7RkS8DXzbGPMNae1sN1yQ+5h5WM3N0BKC3q5nD\nu4eZTKbL3hR3PIpNLD1D2xUJBT03OtK/xMQRcDZDgrK0RUTEX0qpdqaMMT8E2nBytEPABmvtSdVc\nmPjTcHSSUDAwax50X9Gc9trlbbVemme5h9UsJUPbFQ4FSXtwdGQpiSOg0yFFRMSfSskS+0/gBzhF\n+dXAdpyj1EWOMTTmZGgHA4FjLuvtUvLIbNzDapaSoe2KhL3V0XYTR5YyNgI6HVJERPyplEI7kT+0\n5g5gBPhr4JJqLkr8KZXOMjaRnHVsBJQ8Mhf3sJqOCoyOhENBUh6K96tE4ggUdbRVaIuIiI+UUmhP\nGmN6AAtcaK3NAUv7qSnHpZHY7NF+rj51tGcVy4+OLDVHG/KFtodGRyqxERKmN0Oq0BYRET8ppdD+\nFHAjcDPwGmPMn4B7q7oq8SV3I+TMw2pc6mjPrpKpI5FwwFOjIxUrtNXRFhERH1qw0LbWfge43Fob\nA54I/AXw6movTPxneI4MbVdbc4TGhhCDYyq0i0XjSQKB6TnkpXA3Q3rlqHI3cWTVsvITR8D5ezU1\nhLQZUkREfGW+A2u+gnPUuvvxzKu8vkprEp+aPn69cdbLA4EAfZ1NDI4lyOVyBGbZMHkiik0kaW9p\nmHUD6WJFwkFyQCabIxyq/+PrJI400ty49DjHtuYIMXW0RUTER+b76XcnoNMhpGRzHb9erLezmf0D\nE0xMpgvjACe6aDzFsjlenCxWuOgYdvfP9eImjpy5cVlFvl5bc6QwiiIiIuIH8xXab7HWnmuM+YG1\n9kU1W5H41vSpkPMU2l3OZQOjCRXaOEktiak07S3tFfl6kUKhXf/RkUoljrjaWiIk01mmUhkaI6GK\nfE0REZFqmq/QzhhjfgOcZYy5feaF1trLqrcs8aOh6JQzhz1PEdTX6SSPDI5NcvKqjlotzbNiFTys\nBiAcdgptLySPuIX2qiWcCFnMfWE2kUip0BYREV+Yr9C+DDgHuA74QG2WI36Vy+UYjk6yatn83Uu3\noz04qog/qOxhNUBhLjvlgeSR6Y52ZU4BdQvtWDw177smIiIiXjFnoZ1PGfmVMeYp1tqBGq5JfCiW\nSJFMZ+fcCOlyO9oDSh4Bio5fr8BhNVA0OuKBjnalEkdcivgTERG/KSUK4LnGmE8C3fmPA0DOWqv3\nbqVgoWg/lzraRyucClnh0REvZGkfGKpc4gjodEgREfGfUn4Cvg94urX24WovRvxraMw5rGa+xBGA\npoYwbc0RdbTzKj064na06z06MjGZYmy8cokjMJ0zrkJbRKQ6Dg3H+fLNj/DG55+24CiolKaU/K9+\nFdmykOESov1cfV1NDI0lyHrkUJV6qvToSNgjoyP9A5VNHAF1tEVEqu3+xwbYfTDKHzQxXDGldLTv\nNcZ8F/gpUGhDWmu/XrVVie8MlRDt5+rtbGb3wRhj40m62yuTH+1XsfzoSHulU0fq3NE+MFTZxBGA\ntvyLEZ0OKSJSHQeH4gA6s6CCSim0O4EY8OSiz+UAFdpSMH1YzcKFc2/ndJb2iV5oR/NFY0eFR0fS\n6fq+W3BgoLKJI1C0GXJShbaISDUczDdJVGhXzoKFtrX2dbVYiFTWwGiCq755L6999ha2ntJb9fsb\njk4SDgVL6sz2drlZ2glOXde16Pva2T/G57/3EJdsXc0VTz2p7icgLkUsnqQhHKxYLnTEI5shK504\nAtDW7Hy7Go/rwFoRkUrL5XKFjvbBoTjZbI5gMFDnVfnfnIW2MeYrOJ3rWVlrX1+VFUlFPLx7mNHx\nJA/vGq5JoT0UnaKno5FgYOH/lH2dbvJIeRsif/3Hg0Qnktx85+M8vHuYN73gdFZ0V66gq6VYPEl7\nSwOBEh63UnglR7vSiSMAkXCIxkiImGa0RUQqLhpPEZ9KA06z5shogpU9/vzZ6iXz/RS8o1aLkMrb\ndzgGwMBY9WP0UukM0Ykka3q7F74y0x3tctaWy+V4ePcQrU1hztq0jLv+dJj3X38Pr3zmZp521qqK\nFay1kMvliMZTrO2r3IZBL2yGrEbiiKutOcKECm0RkYo7mH8nsjESYiqV4cDghArtCpjvwJqv1XIh\nUll7j4wDzlHn1TYcdaL9FjqsxrWso4kA5XW0DwxOMByd4kmnLeevr3gCZ23q5eu3Wr764208uGOQ\nv3rOFtorlOBRbZPJDKl0tqLrjXhgM6SbOLK6ghshXW0tkcIMofhbOpOt+4iTHP+mUhkawkFfNWHq\n5eCwMzZy1qZl3LPtCP2DE5x7al+dV+V//h1ulTllszn2FwrtBLkqx+gNLSLaD5xisKu9kcEyOtoP\n7RoGKHRLLzh9BR98/ZMw67q4f/sgV17/ex7ePbTor1sPsQpH+4E3Otpu4sjqCkb7udqaIyRTWZKp\nTMW/ttTWx751P+//8l31XoYcxwbHErztM7/m5/fur/dSfMFtYjzROMW1NkRWhgrt49Ch4TjJfKGV\nTGULyRbVsthCG5zkkeHY1KI7Wg/tcoroM07uKXxuWWcT//TKc3jZ0zcxHk/xqRsf5Fs/e4xU2tvF\nmPu8tLdWJnEEpgvtena0q5E44lKW9vEhGk+yo38Mu2ek6o0AOXHtPhgjncly/2PKhC7FofxGyDNO\n7qEhElShXSElF9rGmNIGcKXu9h5x5rPdJItqH3deGB1Z4Pj1Yr2dzeRy0wfdlGIymWb7/lHWr2ij\ns+3oMZVgMMBzL9zAv77mPFYta+Hn9+7ng1/7A/vynX0vcjO0K9nRnk4dqV/xUo3EEZdOhzw+7DoQ\nBZzxqUR+85VIpR3Kj0LsPBDVmFIJDg5N0NXWQEtThFXLWgvJI7I0CxbaxpizjTHbgAeNMWuMMTuM\nMefWYG1Spr2HneLyzE3OeEW1N0SW09Hu68pnaS9ihnzbnlHSmdy8m+w2rGznyr86n0vPXUP/wAQf\n+to93Pr7vZ7smlX6VEgoytGuZ0e7CokjLnW0jw9uoQ0wEpuq40rkeHY4X2in0ln2HIrVeTXeNpXM\nMBSdKhy7vnpZayF5RJamlI7254AXA0PW2n7gLcB/VHVVsiRu4si5m51Yv3Jj9Eo1lC+WexZx+Exv\nZz5LexH/iR/Kz14vlGbRGAnxl5cb3vGys2hpDHPj7Tu414PHyVZldCScj/er04y2mziyugpjI+Bs\nhgQV2n6368BY4c8qtKVa3EIb4LH9o3Vcife53X/3ncg1+TQsjY8sXSmFdou19lH3A2vtz4AT+zg/\nD8vlcuw9Mk5vZxPrVrQDlLXpcDGGo5N0tERoWMShK25Hu9RUlFwux0M7h2huDLFxdUdJt9l6Si//\n9xXnAPD7Rw+XvLZaKRy/3lz5zZD1mtGuZuIIFJ0OqULbt7LZ3FEd7WEV2lIFuVyOQ8NxWpucd9a2\n7xtb4BYnNncjZKGjnd/M3q9Ce8lKKbSHjTFbyR9eY4x5NTBc1VVJ2UbHk8TiKTasaC8cdV7NiL9c\nLpc/rKb0sRGY7mgPlNjRPjySYHBsktNP6lnUSZBr+1pZ0d3MQ7uGPbc5sjA6UsJpmqWK1Dl1pJqJ\nI1BUaFd5g69Uz8GhCSaTGbrz74Cpoy3VMJ5IMTGZZvPaLno7m9jRP0bWgyOEXuGeCLky39F2v4er\no710pVQsbwGuBp5gjBkF/h54c1VXJWXbmx8bWbeijcZIiI7WhqqOjsTiKdKZ7KLmswG62xsJBQMl\nvwh4aGdpYyMzBQIBzjm1j6lUhj89PrKo21ZbzB0daal86ki9ZrSrmTgC04W2Tof0L7eb7UaIjcSq\nn/UvJ57Dw04TZ2VPC5vXdjKeSBWKSTmWm6G9Ot/R7u1sUvJIhSxYaFtrd1prLwJ6gPXW2vOttbb6\nS5NyuAfVrF/ujI30dTYxFJ2s2s5hdyPkYjvawWCAZR1NJc9ou/PZxbF+pXID9+/zWMRTNJ6kpTG8\nqA79QqYPrKlP5+bAUPUSR2C60NbpkP610y208/8vNToi1eDOHK/oaWbzui4AtmtOe06HhiZobAjR\n1ea8wxoMBJQ8UiGlpI6cb4y5EbgJ+IEx5nZjzO3VX5qUw+1or1/hdBR7u5rJZHNVe3vW3Qi5rMRT\nIYv1djURjaeYSs4/0pFMZbB7R1nT17rogh5g4+oOOlsbeGD7oKe+YcQmkrRXcGwE6n9gTf9g9RJH\nQB3t48GuA2M0hINsWtNJa1NYoyNSFYdH8qMQPS1sXpsvtPep0J5NNpvj0HCCVT0tR52g6SaPlDri\nKbMrpZX2deBXwIeADxT9Eg/ad3ictuZIYf5xek67Ov9R3BzsZYvI0HYVkkcWyNK2+0ZJpbOLHhtx\nBQMBztncy3gi5ZmORjabI5ZI0VHBsRGAiJs6UofRkWonjgA0REI0RILaDOlTiak0/QMTnLSynXAo\nyLKuZkaiKrSl8qY72i2sXtZCW3OE7fu1IXI2g2MJ0plsYSOky00e0YbIpSml7ZSw1l5d9ZXIkiWm\n0hwZTXDahu7Cq9K+LnfT4SRmfeXvc8g9rKaMTnMheWQ0wZp5Ns8V5rPLGBtxnXtqH3c8cID7tw9i\n1tf/7KXxyRS5XGUztAFCdexou7N81UoccbU3R7QZ0qcePxQjB2xc0wk4L7b3HooxlczQ2FB6apHI\nQg4Nx2lqCNHZ2kAgEOCUNZ08sGOQ4ehkWT+vjmfu7PrMkb/i5BF3BFMWb85C25hCWXa/MeYfgB8C\nhSO8rLV7q7w2WST3FMQN+Vg/qGFHu4xvXIWO9gIbIh/aPUxjJFSYsyvHlg3dNDeGuO+xAV5+2SlH\nvT1WD4VovwqPjgQDAULBQF02Q/YPVjdxxNXaHClsdBJ/cfOzN+UjOt13wkbGp1jZU90XaHLiyOZy\nHB5OsKavtfC9fvM6p9Devn+MC05XoV1soUL7oDraSzLf6MgvgTuAy4C3A7flP+d+XjymOHHE1VvU\n0a6Gwegk4VCwrOSMXvd0yHnmv46MJjg8HOe0Dd1L2jQYDgU5a1Mvg2OTnjiW3T2sptKjI+BsiKzH\n6Ei1E0dc7c0RplIZz8U1ysJ29jsbITeuzne089+fRhYYHxNZjOHoJOlM9qgXb6eu1YbIubgZ2itn\njI64ySMaHVmaOTva1tqTAYwxPdbao3KzjTEnVXldUobpxJHpQqenvZFAoLod7WUdjWV1iPtK6Gg/\nvCs/NrKpvPnsYuds7uV3jxzmvscGWF/U9a+HWD5Du73CoyPgvKhI1yF1pNqJI662/GM2nkjT3a5x\nA7/I5XLsOjBGd3tjYQ/Jsvz3ACWPSCW573it6G4ufG7DynYawkEe08E1xzg4HCcYCBz1eMF08kj/\nwATZbI5gsL7vBPvVfKMj64AAcIsx5jn5P7u3uQXYUv3lyWLsPRwjEg4WAufBKbp62puqcmhNMpUh\nFk+xbnl5Hcz2lggNkeC8EX+VmM92nblxGeFQgPu3D/Kip21c8tdbiuhE5Q+rcUXCwbrMaFc7ccTV\n1jR9OqRbsMni3PfYAGMTSS49Z03N7nNobJJoPMV5ZnrW031XS8kjUknuRsjijnY4FGTj6g7s3lHi\nkylamir/bqJfHRqK09fdPOu7xquXtbLnUIyB0QQrNN5Vlvnei/8AzpjIZpzUEXds5Fbgx9VfmixG\nOpPlwOAEa/taCQWPflp7O5sYjU2RqnDx5Xahyt1YEggE6O1sZmCOFwGpdJZH946wallL4S3mpWhu\nDHP6ST3sOzJe97iiao6OhEOBmo+OuIkjq6o8nw3Q1uKeDpms+n0dr/7r54/xjVst99ojNbtPNz/b\nHRuB6X0aKrSlkg4XJY4UO2VtFzlgR7+62q5oPMl4IsWqOYpoJY8s3ZyFtrX29fnxkSuttScX/dps\nrf2HGq5RSnBwKE46k2Pd8mNHInq7msgxfbhMpQwtYSOkq7ezicRUmonJY1MkHts/SjKV5YyTlz42\n4nJ3Tt9f58Nrqj06UukXVQtxE0fmS4+pFGVpL018MlVIC/rqj7fVrMjd6W6EXNNR+FxhM6QKbamg\n2TraAKeudV7kKeZv2iF3I+QcaVHFySNSnlJOhvz3WixElsbdCLlhxbFjHIVZ6Ap3cd3DanrKOKzG\nNb22Y18ETM9nL31sxLX1lF4C1P+UyKqOjoSCNU8dqVXiCOh0yKXan9+02tvZxMRkmut+9AjZXPVn\n+ncdiBIKBo5KRWptdsbHhnUMu1TQoeE4na0Nx4yxbVrTSSCgg2uKuRshV/XM/r1bySNLV7mzn6Wu\n9h52NkKum2WTXyHdo8Jz2kuJ9nPNlzzy0K5hGsJBzBJi/WbqbG3glLWdbN8/Vih26yEWTxEMBGhp\nqvw8cyRc+0LbTRypSaHdoo72UuwfcL5XvPCikzlr0zIeeXyEn9+zr6r3mUpn2Xs4xtrlbTREpjew\nBgIButub1NGWikmlswyNTc46T9zcGGbd8jZ2HYzV/F0/r5or2s/V29lEQ9i7ySN7D8e45vsPMTbu\n3e8hKrSPE/uOxAgAa/uOLXR6q9XRXsKpkK65srSHxiY5MDjBlg3dRMKVTZY499Q+csADOwYr+nUX\nIxpP0t4SIViFPG83dSRXgy6ly00cWb2sBoV20WZIWTy3o722r43XPfc02lsifPeXO6sae7n3SIx0\nJlfIzy7W095ILJ5S4SMVcWQ0QQ5Y2TP7vp5T13aRzmR5/FC0tgvzqIUKbTd55OBQnGy29mlWC7nx\n9h38wQ7wo7v21Hspc1qw0DbGdBlj3mqMea8x5kr3Vy0WJ6XJ5XLsPTzOip4WmhqO7ZAWToeseEc7\nvxlyCckPfYVu+9EvAh7a7YyNnFGBtJGZzsnPaddzfCQWT1ZlPhsgHM6fDlnDrnatEkeAQma7Cu3y\n7B8YJxgIsLq3hc7WBl733NNIZ3J86aY/kUxVJ5t8Vz4/e1PRRkiXmxwz6uGOlPjHXBshXe7BZ49p\nfARwRkc6WxvmTWFZ3dtKOpOte4jATLsORHl0zwgAv3rwAFGPbpAvpaP9HeBSIIQT8ef+Eo8YGpsk\nPpVm/Szz2QCdbQ2EQ/PH6JV1v9FJOlobltRx7p1jRvvhXU50eyXys2da3tXM2r42Hnl8hMRUeuEb\nVFgqnSExlaGjtTrxUpF8RFMqXZvuQy0TR8CZ6wV0DHsZcrkc/QPjrOhpLvy/PfuUXi49dw39gxN8\n946dVblfdyPkxlk62m6hrfERqYTDc2yEdG3WhsiCZCrD0NjkgmcfeDV55Ed3PQ7AeaaPZDrLz/+w\nv67rmUsp7aeV1tpnVX0lUjb3oJq58qyDgQDLOiubpZ3N5RiOTpadoe1qaQrT2hQ+6kCddCbLI48P\ns7yrmRXd1cntPPfUXm767eM8vHuY87csr8p9zCVWiParUkc75LwOrlVHu5aJIwCNkRAN4aA62mUY\nik6SmMpwxslH/7/980tPYdueEX5+737O2rSMMzZW9gXurgNRWpvCLO8+9u189x0xbYiUSji4QKHd\n1dbI8q5mduwfI5vLlTW+NzQ2yRd/+DAvftpGnlCFd11r5dBw3BmzWWDkzx0JPDA4UUjuqrf+wQnu\n3z7IxtUdvOH5p7Nt753cfu9+nnPB+pq8s7oYpXS07zfGnFX1lUjZphNH5j7tsK+zifFEqmId3NhE\nknQmV3aGdrHezmYGxyYLM8U7+8eYTGY4s8I/7IvVM+YvWsVoP3A2Q0LtCu1aJo642loiKrTLMD2f\nffRz1RgJ8aYrnkAoGOC6Hz1a0bdgxyaSDI5N5hMfji1qutsV8SeVc3g4TiAwPTI5m81rO4lPpQub\nuBfrR3fvYdeBKD/4za5yl+kJbgziXBnartV904W2V/zkbmcm+3kXbqAxEuJZ560lPpXmlw8cqPPK\njlVKoX0GTrF9wBizyxiz2xjj739dx5n5Ekdc7oEvlepquzm8S0kccfV2NZFKZxnLp4D8MR/rd8bG\n6nUK1i1vY1lHEw/uHKp5Qkd0It/RrtLoiHu6V60OrTlQj0K7SYV2Ofbn3/1aO8s7URtWtvOSSzYy\nNpHkq7dsq9hm2l3zjI1A0ehIVIW2LN3h4Th9nbOfcuhy57S371/8nPbY+BS/+eNBAHb2R6u6ibja\nDi6Qoe3yWvLI0Ngkdz9ymNW9rWzd3AvAZU9cS2NDiFvv2eu5jdWlFNovBjYCT8aZ1X56/nfxiH1H\nYnS2NtA5TyZzXz4ZpFJz2pWI9nPNzNJ+eNcw4VCQLeu7l/y15xIIBDj31D4SU2m27R2p2v3MppqH\n1UDRZsgafbMpFNo1SBxxtbVEmExmPPcN1evcaL+1fbOPfP3Zk9azZX0XD+wY5JcPVqYztOvA3Bsh\nQTPaUjnxyRTReGrBo8LdOe3HypjT/uk9+0hnsmzN7x+644H+xS/UIxbK0HZ5LXnkJ7/fSyab4zkX\nrC+M/rQ2Rbj0nDWMjSf57cMH67zCo81ZaBtjnp//4yVz/BIPGE84p7ytn6ebDdMd7Uolj7jRfhUZ\nHSlKHhmJTbHvyDhmXSeNDZWN9Zvp3FOdV8L3PVbbmD/3bflqzWhHatzRrmXiiMs9tMYrXe0f/HoX\nH/raPTV/d2Sx+gcmaGwIzRnJGQwEeOPzT6elMcx/37a98NbyUuzMH3d98qrZv0e1tUQIhwIMq9D2\nvFg8yf/74p187UeP1Hspszo84jSSVswR7eda2dNCe0tk0R3t+GSKX9zfT2drA29+4Rl0tzdy18OH\nmEzWflN9JRwcitMQCdJdwqFzXkkeicaT/PrBAyzraOKC01ccddmzzltHOBTgJ3fvJZP1zvfi+Tra\n5+d/v3SWX0+v7rKkVPvy89lzJY64eivc0XZPhVzWWX60n6s45/vhfKxfNeezXaes7aStOcL92wdq\ncjKeK5YfHWmv8uhIugapI7VOHHF57XTIR/aMsPtgDOvhyLBUOsuh4Thr+1rn3QDW09HEa55tSKay\nXHvTn5b04iGbzbH7UIxVy1rmjA8LBgJ0tTUyos2QnvezP+xncGyS796+nT/uHKr3co5R6sxxIBBg\n89ouhqNTR23EX8ht9/Uzmcxw+fnraGwIcfHW1UwmM/z+0SNLWnc9ZHM5Dg3HWdUz//cDl1eSR37+\nh30k01mefcH6Y8aDutsbeeqZqzgymuAP2+p7+nOxOQtta+378r+/bpZfr6/dEmU+CyWOuPoqPqNd\nuW2AFgAAACAASURBVI52X9HJlQ/lY/0qnXowm1AwyNmn9DI2nmT3gdodXlDtjrabOlKLjnatE0dc\nbqHtldMhE5NOR6ue2ewLOTg0QSabm3NspNiTTlvBU89YyZ5DMX74m91l3+eBwQmmkpk5x0ZcPe2N\njE0kPdWFkqMlptLcfu9+WpvChENBrr/l0bqerjubQ0PzZ2gXW2zM31Qqw8//sI+WxjBPP2cNAE87\naxWBAPzifv+NjwyNTZJKZxeM9nMVJ4/US2IqzW339tPeEuGis1bNep3nXLCeQABuuXtPTQ9tm49O\nhvSAyWS67H8QpSSOALQ2hWlqCB1zMEy5hqNTNISDtDcvvSvrdtuPjCR4ZPcwyzqaSv7Pv1Tn1uHw\nmqqPjtQwdaQeiSPgvY52PJ/m88D2wZq+O7IY/UUnQpbiVc86ld7OJm65aw+2zH0MhfzsNbNvhHR1\ndzSRy8HYuLcKN5n2ywcOEJ9Kc/mT1vOa555GdCLJV39cuU2zlXB4ZP5ov2KnFjZEllZo/+aPB4nF\nU1z2xDWFMbmejibOPqWXPYdi7D7or5Mm3Y2QK0sttD2QPHLHA/0kptI867x1NEZmHy1d3t3C+VuW\ns+/IeKFxV28qtOvs8Eict3/21/z4d3vLuv3eI+M0RkL0zZJPWywQCDgxeqOTFfnGOBSdpKejada4\nrsWKhEN0tjWwff8o8ak0Z27sqcjXLcXpJ3XTGAlx32MDNfuBEZtI0RAJVm0GPRKq3WbI/iN1KrRb\nvNXRdgvtkdgUjx+M1Xk1s5veCFnac9XcGOZNVzwBAnDdjx4llV78qZE7F9gI6dKGSG9LpbPces9e\nGhtCXHbuGl548SZO29DtbJr1UJzaoeE4DeEgXSWcVrxueRsNkSDbSxj3Smey/OR3e4iEgzzzieuO\nuuySs53u9i99tinykLsRssRN7PVOHkmlM/z09/toyv8bnM9zL9wAwC13PV79hZWg5ELbGFO9CIgT\n2COPj5DO5Ljlrj2LzrhOpTMcHIyzbnlbSTNWfV1NTKUySy5OppIZxhMplpWwgaJUfZ3NuHVuLeaz\nXQ2REGds7OHwSIIDQ0vf+FWKaDxZtW42TKeO1GJ0xO4bJRIOsn6JBxctVmEzpAeO3M1ks0wlM4SC\nzv/B+7d7c3xkX77QXlNiRxucfQyXn7+OwbFJfnrPvkXf5+4DURojoQVHi1Roe9tvHz7I2HiSS89Z\nQ2tThGAwwBuedxqtTc6mWTe9op5yuRyHhxMs724p6edhOBRk0+pO+gcnFtxU/btHDjMUneLis1bT\nMSPd64yTe+jtbOJ3jxwhPumfTZHuwT6lvntc7+SR3z50iLEJ59/gfMfFA6xf0c5Zm5bx2P6xsiIc\nK23BQtsYc7YxZhvwoDFmjTFmhzHm3Bqs7YSwK78jv5yg9f0DE2RzuQU3QrrcTYdDS5zTdk9wq8R8\ntstNHgkFA2zZUNvXdLU8vCaXyxGLJ6sW7QdFOdpV7mjH4kn2D4xzyprOwnHetdLe7Dx+44n6/2BL\nTDmd3i0bumkIBz07p90/MEF3e2PhRUqprnjKybQ1R/jfu/YwNl56IRyfTHNgcIKTV7UTDM5f+Eyf\nDqlC22sy2Sw/uXsv4VCQy8+f7ub2dDTx2mdvIZnO8qWbHql74s7oeJKpVIaVCySOFHPntHfMMz6S\nzeW45e49hIIB/uyCdcdcHgwGuOTs1UylMtz9yKHFL7xODg5OEAjAigXeDS9Wr+SRTDbLj3+355h/\ng/Nxu9o/umtPNZdWklI62p/DydIestb2A28B/qOqqzqB7DwQpakhVFbQuhuUv1C0n6sQo7fE/yRD\nFczQdrkvAjav7az58alnbVpGKBioSYE0mcyQzuRob6lO4ggUjY5U+Qef3et0Cmr9wgigtdn5NzKe\nqH9H230nqqu1gSec3MPBobgnOnzFxhMpRmJTheSAxWhpCvPip53MVDLD939d+llluw9FyQEbFxgb\nAQpv9St5xHv+sG2AI6MJLjpzJV1tR7+Led6W5Vx05ir2HI7xg1+Xv2m2Eg4PL27mGEo7uOaB7YMc\nHIpzwekrCj+nZrrozFWEggHuuL/fUzPr8zmYP9hnMU2SeiWP3LPtCAOjk1x01io620p7J/3UdV2c\nsraTP+4cqvuhQqUU2i3W2kfdD6y1PwMqNzNwApuYTHFoOM7G1R1cerYTtH7nIoLW3Y2QCyWOuAoH\nwyy1o+2eCjlHFm853C7EmZtqNzbiam2KsGV9F48fijEwUt1X6tXeCAnFmyGr+w3/0T3OBrnT6lBo\ne6uj7ayhuTFcl821pejPj42sW8TYSLGLz17Nmt5Wfv3gwcL3nYW479ZtmuNEyGI9Oobdk3L5bm4g\nAM++YP2s13nlMzezvKuZH99d/qbZSjiU3wi5orv0QnvT6g6CgQCPzVFo53K5Qkf0OfkO6Ww62xo5\n59Q+9g9MFPYleNl4IkUsnlrUixKoT/JILpfjlrv2Ov8Gn1RaN9v1PHdW++76drVLKbSHjTFbgdz/\nZ+/O49u6rzvvfy52EiDAXaJE7cvVvnjf7SR2vMVO0mxOmjZpE6ftZJpJUs9Mn06nSWem7dNOlj7t\ntEnHTZumedI46ST1EjuJE++LLNuSrP3KEiVRpCiS4gaS2IE7fwAXhCgSuACx87xfL73icAU34ODc\n8/seAFVVfxWojqOcNc6IlFu7zMcdVyeD1p96rdf0/FPv4BQWRTF9uMnoaC80S9sYPSnm6Mg1m5fw\nG3dv4vYru4v2MfOxO1UgvXaktBulSp2hDeUbHTl2dgynw8rqpeauqBSTw27BbrNURUfbmMtsdNnY\nub4di6KUfQlSLn15Jo7MZrVY+Mg716MDjzxz0lTX7lT6/i13oe1zO7AosrSm2hzqSXYDr9m8hM55\nCtgGp40H79uCoig8/MRRpkOVOaBsRPuZSRwxuBw2Vi7xcGZgkkj08sO+x3vHOT3gZ/eG9pznDG7b\ntQyA52og6s+44pbvNt9KJI8c6hmhbzj77+B8dqxro7vDw95jgwxVcNGOmUL7d4C/AbaqqjoOfB74\n7ZLeqkVi5kS+dyZofSzIG1ru8PuErnNuaIqu9kbTl36MGL2FboecGR0p3oUNm9XCzTuXlX3W17B7\nQ7LQ3lPi1a3l6GjbbMl52FKOjoxNhrkwGkBd0XzZ0oByUBQFT4OdyUDlU0cyO9qeBjvqymZOD/ir\nqjvblz4IWXg6zLa1bWxf28axs2McOJn9iYSu6/Sc99PmdZm61GuxKPg8Dsb81fM9E/Ck0c2dp5tt\nWLfcx/03rmbUH+aff6ZVZHzCGB0xk6GdaeOKZuIJfc54PiO14p7r5+9mGzatamFJSwOvHx+qmo21\n88k32s9gJI+Us9A2rijck+WKwnwUReGe61ei6/DTApPdiiHnI6Smaac0TbsJaAVWapp2taZpWulv\nWv1LZ8ymOj53pYLWf/Jq7qD14bEg4Wg8r7QHl8NGU6N9wR3t0VSh3dJUvI52pbU0OVnT5eXQqZGS\n3kmWZXSkDB3t46mxkU0rKxdG5GmwV6x7limQUWhDxuHaKkof6RuewmpRTEd5zecj71yPRVH4wTMn\nsz6RGx4PMhWMsi5Hfnam1iYn41Phqs0hrzVv940znsfh1bne/0TfBDvWtZk6B3TvDatYv9zH3mND\n7DkyWPDnLdSFsSCeBnveh32NA5EnZh2IPHPBz5EzY2xa2ZwznhKSqRy37lpONJbglcPVfSjS6P7n\nu68inTwyWp7kkRPnxnk79TtodkR2tqs3ddLR7OKlgwN5HeYuJjOpI8+qqvoM8Djwb6qq/lJV1SdU\nVf2fEvlXuISuc/q8n86WhnQCxZI8gtbPplev53fZvt3nYsQfWtCD2Yg/hM/jSM8C14tdG9pJJPT0\nIb9SmExtUivH6EgpO9qVnM82eBrsBMPxiqcdGIV2Y6rQ3r2hHShPio0ZCV2nb3iapa2NC/6bXdbu\n5rbdyxgcC/LMvvkvkZ/KGIszq6XJSTyhV8VVilr3zL4+/uy7+/jv//RGwYff8+0kWi0WPn3fFlwO\nK999WitrMkUsnuDieJAleSSOGNZ3z30g0vj6771+temPdeP2pdisCs8fqO5DkQN5ZmhnWtbuJhor\nT/KIMVt9r4krCvOxWizcde0qYvFEQRGlxWDmXvcocJDkyMjngdeBceA88K18PpmqqhZVVb+pquqr\nqqo+p6rq+lmvv09V1ddTr3/QzPvUqsHRANOh2GXzi2aD1tOJI3k+y2v3NRCL64wXeFk7oeuM+sNF\nTRypFkbM0UK6QLn4U0VESUdHrKXN0dZ1nWNnR3G7bKwwGS1ZCtWyHdJYv97gmtkWt3ppE8d7x6ui\n4z4yESIciS9obCTTe29aQ4PTxmMvnZ736k9P/8xYnFkt6QORkjyyEK8cHuC7Pz+B02FlbDLMV76/\nP+8xpnNDUxw8NcKGbl96g6IZnc0N/OodGwmG4zz8xFHiifI8CR6ZCBFP6HnNZxt8bgdLWhs51T+R\n7tIOjEyzTxtm1dImtqw230xoanRwldrJwEiAEyYW4VTKwEiApsb8u/8Ay9qT3+NSj4/0Dk6mfwc3\ndJv/HZzLTduX4nM7eHZ/P4EK3CebKbSv0zTt85qmHUz9+31A1TTt68CaPD/f+wCXpmnXA78PfNV4\nhaqqduDrwLuBW4HPqKq6JNv71LKeeTamrVzSxPa1uYPWewdTKQL5drSNA5EFzmn7pyPEE3pRD0JW\nC19qEcHEdOkK7cnU6Egpc7RLnToyPBFixB9m08oWU4shSqVatkPO7mhD8nBtPKFz8ORIpW5WmjGf\nXeil19maGh3cf+NqAuEYj740d6Rbz8AEVotiOuMfMpbWyJx2wd7UhvmHnxyn0WnjDz5+JfffuJrh\n8RBffeRA+r7HjIV0Em/YtpSrN3Vysm8iPeNdahdG8z8ImWlDt49gOJ7+W3lqTy86ydSKfLcU37Y7\nubXw2So9FBmNxRmeCNJV4PdqeXvyb7rUEX/F6GYb7DYr7756BaFInF9muRJXKmYKbbuqqluN/5P6\nb6uqqg1AvtXCTcBPATRN2wNclfG6zcBJTdPGNE2LAC8Bt+R4n7LRdZ2XDw2k55MXqifLiXzjFytb\n0Hrv4CRt3vyXTxgRf4Ve9jESR4p5ELJaGIe2JqZKl2ThN0ZHSpijbWyGLNUK9vR8dgXHRgA8rirp\naM9RaKdj/hY4p316wM9bOQ4e5tI3lP9GyFzedWU3nS0NPLuv/7LM8Eg0Tu/gFCuXNOV1uLmlypfW\nvHF8iB+/0GPq37+92FP27N4jp0f5u8cOY7dZ+MKHd7Ki08N7b1rDHVet4PzFab72g7dMbR8eGguw\n99gg3R2egrb0KorCr9+l0tLk5NGXzqQf60rJKLTzifbLtDE9PjLBqD/Eq0cusLS1kSvUjrw/1oZu\nH8va3bypDafv76vJ4FgQXYelBZ7XKEfyyOBYgNePD7Gis7Dfwbnctns5jU4bT79+jvAcCTMLkeuK\nkZnNIJ8DnlJVdRCwAs3ArwFfBr6T5+3xApknDuKqqto0TYvN8bpJwJfjfebV0tKIrYgJFnuPXuBb\nPznGrbu7eejjV5p+v46OuTvOZ4emcNgs7N7SddncZEdHE5tfPsPBUyNMRROsmdX1HvOHmJiOcO3W\npfN+/PmsW9UKQDCayPt9AfadSs6Or+luKej9q5k7dem60O+NGYFIHE+Dna6l5mdX82VxJP+srTZr\nSb6O0xeSBcSNu7sr+juwNNWhVezJr7dSt8W4y16xvDn9ZK293cOydjeHT4/ibW7Eac//vmgqGOUv\nf/gS06Eo3/nSnaYXNcw2nOoQ71SX0FFgF2sun37vdv7023v58Utn+NKnr0u//NjpUeIJnW3r2039\nTIy3WbMiWZSE43rV3bec7BvnG48eJp+x21/u6+evf+8ddOSxea9QR0+P8L9+fAhFUfivn7qWnRtm\nCsTffWA3WBSe3tvL3z56hC8/eB0ux/wP/T98vgddhwferdLZOf/oT7afUQfwHz9+FX/wjZf5Py/0\n8Be/e3NBX5dZE6k8/c3rOwr63bl2p8I/PHmMs0NTTIZjxBM6H7ljI0uyfP3Z3HvTGh7+t8Mc6Bnl\nA+/cUNDHKJXpaLIBs2FVa0Hfq7Y2Dw67laHxUEn+TicDEb757dfRdfjouzdl/R3M1303r+WRX5zg\nqdfP8eB7txflY0ZjCf7ku2/yl1+4bd63yVloa5r2nKqqa4HtJB9TjmmaFlVV9RVN0/K9Nu0HMn8y\nloyCefbrmkjOgmd7n3mNpcLri+XR504C8ObxQQYH/TlXCkPyjmh4+PLlDuFInDPn/axd5mV8bO5n\nhe++qptjZ0b53lPH+Mz9Wy953eGe5OXoJc2uOT9+No5kHDpnz0/k/b4Aew4mL7usaGso6P2rma7r\nOOxWhkcDJfvaxvwhPA32kn7vjEvEU9Phon8eXdc5cGIIn9uBU9Er+zuQmkE/f8EPO5ZV7LaMp65y\nBaZCRDJyvXeua+Op13p54fVedqUOSObjkWfeTv8sn3ntLDft6Cro9p3qG6fBaYVYrKjfo3VL3Gxa\n2cwbxwZ5bu9Ztq5JPol/82gyIrOrJff9U+Z9pCWefMrSP+ivqvsWXdf5xr++ha7DJ+/eZGpl9Ym+\nCX78Qg9/8Z29PPTR3SUdsTp7YZK/+Jd9xGIJPvv+7Syb43HhI7etY8wf4o3jQ/zxw6/yuQ/smDOW\nc2IqzNN7e+lodqEun/vxC+Z/bMu01Odk1/p2Dpy8yMv7zuU1652vM/3JMUu7nijod8em63jdDg6c\nGCYSi9PS5GTryuaCfw93rG7BYbPw5MunuWnbkoqO2GXq6GhCO52sH5qc1oK/vq7WRs4NTZquhcwK\nhmN85fv7OXthkndd2Z31d7AQt+3s4vl9fTz2Qg/ru5rYtmbh3fIfPneSU7MSa2YzkzqyCvgz4N+T\nPAz5d6qq/kMBRTbAy8A9qY97HXAo43XHgA2qqraqquogOTbyao73KYuLE0EOnUr+ck4Fo5y+sLBL\nYWcu+EnoetZFDsmgdTevzRG0XmjiCCQPaikUlqUdTyQ4cmaMdp+r4Fm4aqYoCi1NzpLNaCcSOlOB\nKN4Sjo1AaQ9DDowEmJiOsHlVS96zi8XWlBqbqnRmbSAUw26zXFa4LGRL5OBYgF+80ZceMSp002Q0\nFmdwNMjyDk/Rf16KovDAuzagAN9/5u30wbf5zp/kMrOGvbpGR/adGObEuXF2rW/nlp3LUFe25Pz3\nnutXsXtDO8d7x/nZ3tLl956/OM1XHzlAKBzn0+/ZMu8TOotF4TP3bWHHujYO94zyd48dmfOg4s9f\nP0csnuDua1dhtSw8Vco43J9tDLIYBseCtHmdOAq4cgTJ3+WN3T6mglEi0QR3XrNyQfsB3C47V2/u\nZGg8yLEzlduWOZdCo/0ylSJ5JBKN81f/epDTA5PcuH0pH719Q9Hvs5x2K791/1asFoVvPXEsr3ML\nczl+doyf7umlszn7k28zv0k/ABTgReD5jH+F+DEQUlX1FZIHH7+gqurHVFX9jKZpUeCLwM9IFtj/\noGla/1zvU+DnLtgLbw2gk+xQARzOEb2XS08qGH/d8vkfiBRF4Z7rVs0ZtF5o4ggkD8o1Nzm5OJH/\nH8ipfj/BcIzta9sqXmSVSqvXhX86WpIs36lgFB1ocpfuICRkHoYsfqF9rErms2HmMGSlC+1gOHbJ\nfLZhzTIvPo+DAycv5p2+8MNnTxFP6PzqHRtZ3u7myJlRwpH85wrPXwyQ0PWCN0LmsnJJEzfu6KJ/\neJoX30p2snvOT9DUaE8vyDLLZrXgdTuqakY7Gkvwg2dPYrUofPid5gOvFEXhE3dvwud28KPnezh7\nofgd+ovjQb76yAGmglF+/S6Va7csyfr2NquFf/e+bagrmnlTG+bbTx2/5H4uEIry7P5+fG4HN25f\nWpTbuD6VWnKoZ4TewdJcpQhFYoxNhhfc/DGSLTwNdm7duWzBt8s4FPncgeo6FDkwEsBus9CW599n\npmInj8TiCf723w6jnRvnSrWDT969qWRXAVYtbeL9t6xlYjrCt586XnAM43Qoyt//5CiKovDg/Vuy\nvq2ZGW27pmkPFXRLZtE0LcHlWyWPZ7z+cZJ53bnep2xi8QQvvnWeBqeNT9y9iYf+5hUO9Yzw3pvy\nDVyZYTb66urNnfz4xR5eOjjAe29cnZ7R7B2cotFpK/gPpcPn4u2+CWLxRF7P2g+lRlaKdTihGjU3\nOUnoOlPBaNEj+MqxrAbAalFQKM1hyGo5CAkzhyErXWgHwjHcrsuvUlgUhd0bOnhufz8n+yZQTS73\nOX52jH0nhlnf7ePqTZ30DU/xxCtnOXx6hCvVzrxuWzpxpEjRfnP5lVvW8vqxIX78Yg/qymZG/GF2\nrW8v6Ml4S5OT8xen0XW9Kp7M//LNPobHQ9x+VXfehZy30cFv3ruZr//gLf7340f4o09eXdCs/lzG\nJsP8z1Rs30feuZ5bdy039X4Ou5XPfXAHX/n+fl4+dIEGhy3dOfzlvn5CkTj33bi6qBt6771+FSfO\njfPknrP89nu3Fe3jGobGkk2jfDdCzrZjXRs/fO4k916/Cqdj4V//2i4vKzo97D9xkbHJcPqwbyUl\nEjoDo8lM/YUUspnJI7s35n9gdPZtevjxoxw8NcK2ta185r6tRbmaks1d16zk0KkR9r99kRcPDnBL\nnk+sdF3nn3+mMeoP876b1uS8emfmq3kplW9d2uqgSr118iIT0xFu3LaUZo+T9cu9nD7vL/jBXdd1\nTp6foNnjyPmHN1fQeigSY3A0wMolhV8Kbm9uQGdmlbpZh3tGsVkVNq0q3axdpRk/E38Jkkcmy5A4\nAslums1mIVrkeL+ErnO8d4w2r4uOBXRDiqWqOtquuXsWV6Qu5b9pcvQjoet8/5m3Afjou5IF0MwI\nSv7pI/3DyY5TMRNHZmv2OLnn+lVMBqJ889EjwNxpSma0NjmJxhJMh3KnY5SaPxDh8VdO43bZuP/G\nwhor29e2cfuV3QyMBPjBsyeLcrsmAxG++sgBhsdD3H/jau68Jvt69NkanDa+8OFdLO9w84s3+/jx\ni6cJR+M8/fo5Gp02bjNZtJu1bU0rK5d4eP34EINFPjsFGYkjCyy0l7Q28r8+f0ve38/5KIrCbbuX\nk9B1Xjp4vigfc6EuTgSJRBMLGhuB4iWPJHSdb//0OK8fH2Jjt4/Pvn97WRbhWSwKD963hUanje/9\n4kT6d8isV49cYO+xIdYv93HvDbnjB818RR8EHiU5vpFI/StuNkoVey6VhXlr6jLQtrVt6MDh04Xl\n445NhpmYirB2mc9UoTw7aL1veBodWNFZ+Glf45LuxXHzhfbEVJizg5Ns6G7OemK91rWk8sEnShDL\nlF5WU+LREUheJi726Mi5wSmmQ7GqmM+G5LydzapUdJNgNBYnFtfT69dn27SqhQanlf0nLpq6RPnK\noQv0Dk5x/dYlrOlKFqurljTR6nXy1smLef9Mz6U62t0l7GgD3Hn1Ctq8zvRYWz6LajK1VNGc9qMv\nniYYjnP/TWsKWuxh+OBt61je7ubZff0LjmoMhmN87Qdvcf7iNHdctaLgK6ueBju/95FddLY08MQr\nZ/h6agTlnVcun/d3uVDZxiCLYaEZ2pkKnfGez3VbluC0W3nhrfNlWVmeixH1udDvVbvPhcNmWVCh\nres6j/zyJC8dHGDV0iY+98GdRbviY0ar18Wv36USiSZ4+PEjpu9bh8eDfPfnJ3A5rHz6vi2muu85\n30LTtGWapllm/Svfd6OChsYCHDkzxsZuH8vbkw9UxthEoXPaMweFzD0QZQatP7Ovn3Ppg5CFd6g6\nUoP7w3nMaR8+nfx663lsBGYe6EuxHbJcoyMAdqtCtMijI9Wwdj2Toih4GuwVzdEOhC7P0M5ks1rY\nsa6dEX8ovWRqPqFIjP/zwikcNgsfuHVd+uVKagQlEI6h5bltrm94ijavk8Y5RluKyWG38oHbkrdZ\nAVZ3LbTQrux2yP7hKZ470M/S1kbesXthHV6H3cpn7t+Kzarwj08eK/hJ/PhUmK//8C3OXpjk5h1d\nPPCu9Qt6wtvscfLQA7toaXJyom8Ch83C7VetKPjjZXOV2klnSwMvHxoo+pOowSJ1tEuhwWnjuq1L\nGPGHOdhT3OVVY5NhvvXE0bxyyvtS9cOy9oU98bYoCl1tbgZGAwU/gXj0pdM8/cY5lrW7+eKHd857\nVbCUrtm8hOu3LuX0wCSPvTz38q1M8USCh584SigS51fv2JjzEKTBTOpIp6qqX1BV9b+qqvpHqqr+\nsaqq+eZn16TnDyQv99yacUe7YokHr9vB4Z6Rgg7MnTqfjIHJ59JqOmj9jXOc7E++fyGJI4ZCOtoz\n89mtBX/eWmCsgS7FooGZrZClLXogubSm2B3t473VM59t8DQ4KroZ0tgKma0LaIx+7M+xvOapPb1M\nTEW469qVl21eNUZQ9ueRPjIZiDAxFSnp2EimazcvYdf6dq5UOwruilbL0ppHnjmJrsOH37l+QekT\nhhWdHj546zr8gSj/+OSxvA9g7T8xzB99ay8n+ya4dssSPnHXpqJcVWr3NfAfP7qb5e1u7r9pTcma\nABaLwt3XriQW13k6NQZZLBdGg9isCu1Vuq3YeKL28yKnzzz28mlePnyBrz1ywPRB02J1tCEjeaSA\nYIWfvtbLYy+foaPZxe99ZFdJNyXn8vF3b6Td5+Inr57lRI5GxpOvnuVk3wRXb+rkhm3mDwybuQf5\nEbAL+DjgBu4HSrNyropEYwlePDiAp8HOVRnboSyKwvY1rfgDUc7l6FDN5dR5PxZFYfVS84V2g9PG\nO69czmQgyp4jg9isyoJmrIyOttnkkURC58jpUVqanAt+JlztWlIbL0syOjJdvtERu9VS1Hi/WDyB\ndm6cpa2NVXGox+BpsBEMx0qSsGLGXOvXZ9u2phWb1ZI1om/UH+Jne3tp9ji4+9rLZ/42rmzGgg92\n8QAAIABJREFU7bKx/+2Lpp/g96Xms0uVODKboih87oM7+HfvL3wRhPFEt5Jr2A+eGuHw6VG2rG5J\nJ00Vw+1Xr2DL6hYOnhoxvZ47FInx7aeO8dc/OkQ4muyifea+LUXNLl7a2sh///S16Si+UrlhWxfN\nHgfPHuhnOlScJ8e6rjM4GqCzpbGo35NiWrmkiW1rWjneO86p/ux5y2aNTYZ5+dAAblfy/u9rjxy4\nbEPrXPqGplAoTvc/nTwynN/4yPMH+vnBsydp9jh46IHdFX88aXDaePC+ZGrIw48fTV+lnO3U+Qke\nfekMLU1Ofv0uNa8numYK7XZN0z5BMg3kR8BtwNas71EH9p0YZioY5abtXZedwN6WGp/I91JQLJ7g\n7IVJujvceZ9qvv3KFdhtFnSSzyQX0mVp9jixWhSGTXa0Tw/4mQ7Vd6yfwXigL0WhPdPRLsOMts1S\n1NSRsxcmCUfiVTM2YvCkvpcLzUMtlLHSuiHLZc8Gp40tq1voG55maJ7DYP/6/CkisQQfuHXdnPcN\nVouFXevbGZsMm46KMxJHujtr58lxa4VntGPxBI888zaKAg+8s7g5vhZF4VP3bsHtsvHIMydzzrf2\nnPfz5X98nRfeGmBFp4c/+uTVvOvK7pq9D7bbLLz76pWEI3GeebOvKB9zMhglEI6ZWiJUSfdeX9w8\n8adfP0csrvPB29bx8TtV/IEoX/n+gZzNs76hSdp8rqLMQmcmj5j12tFBvvNTDU+DnYce2J1u+lXa\nhu5m3nP9akb8Ib77tHbZ60ORGA8/fhRd13nwPVvmTJnKxky1ZqSta8BOTdMmgNJf+66w9CHIXZfH\nvmxd04qizGxoNOvc0BTRWIK1WfKz5+N1O7hlR/K2LGRsBJKX8dp8LtMd7cUyNgKkIxQnSjSjbbUo\nZZlFSx6GLN7hm2qbzzYYh9RKMepjRjCcPBeeraMNZE0O6TnvZ8+RQVYtbeL6LJcjd+e5AKc/fRCy\nPB3tYmiu8Iz28wfOMzAS4Jady+guYE9BLi1NTj559yaisQT/+7Ejc56jiCcSPP7yaf70n99keCzI\nXdeu5A9//ar0OaFaduuuZbhdNp5+o49wdOGZCsbylWpfoLZxRTPrlnk5cPJi+glwoaZDUZ490I/P\n4+CGbV28Y/dyPnTbOsYmw3zl+wfmfewKhKKMTYbpaivO71E6eSRHJz2h62i9Y3znp8f5+yeO4nJa\n+b2P7Kq6q+P33biaNV1e9hwZZM/RC5e87l9+8TZDqb/FQkYnzRTaz6iq+kPg58Dvqar6TaCyJ1VK\nbGBkGu3cOJtXtcx5icXTYGdtl5dT/X4CeVwCy/cg5Gz3XL+KTSubuWHrwpcJdPhcTAaihCK5Y7QO\n9YxitShsXlX/hbbdZsHTYC9NR3s6iqfRXpZ1vHarpaiHIY1CW11ZXdGORqE9WaFC2/j7z1VoJ3Ol\nYd+sOW1d1/n+L2fi/LL9bmxd04rDln0EJdO5oWmsFqXqi5BMTrsVt8tWkRnt6VCUR186jcth5X03\nry3Z57lS7eTmHV30Dk3x4xd7Lnnd8HiQP//efn784ml8HgcPfXQ3H37H+rJEnpVDg9PGO6/oZioY\n5YW3Fh55V80HITMpisI9qa72U3sW1tV+5s0+wpE4d169Mv17cfd1q3jPDasYGgvylVSCzGwDRdgI\nmSmdPDLH6Iiu6/QOTvLDZ0/yn77xCn/+vf08d+A8XreDz39oJ6uWLqxZWAo2q4XP3L8Fp93KP//s\nRLoR+aY2xIsHB1i1JLnophBmUkf+C/D7mqadBT5KsrP9/oI+W414bn/yDuC2LKfNt69tI6HrHM1j\nvWpPAQchM7U0OflPH7uiKIfR2tNz2tmfM00GIpwZ8LNuua8ip4Irwed2MFGCHG1/IFKWxBEAm1Uh\noetFiZSKxuKc7J9gRaenoodW5tJUJR3tXIf/vG4HG5b7ONU3ccmTuNePD3Gyf4Ir1Q42rsj+JMZp\nt7J1TSsDI4Gc85gJXef8xWm62hY2ZlYJLU2uioyOPP7yGaaCUd5zw2p8JT5H8dHbN9DZ0sDPXuvl\n2NkxdF3n5UMDfOkf9qYPW/23T11TdVeQiuH2q7px2C38bG/vgs9WXBirjY42wM717Sxvd/Pa0SEu\nFri6PByN8/Qbfbhdtsuutr//5rW868pu+oen+foP3kqPtRmMQntpkQrtuZJHhseDPPHKGf7rt/by\n5X98nade6yUYjnHTji4eemAX//N3bkhv4KxGS1oa+djtGwiGY/z940cZ9Yf49lPHcdiSRXih96Vm\nUkfswEZVVX8N2AaMAHcU9NlqQCQa55XDA3jdDnanTvrPpZA57VPn/TQ6bVXx7Nts8siR06PoLI6x\nEYPX7SAQjhGNFS8uPhKNE4rE8ZYhcQRInysoxoHIU/1+orFEVT7opzvaFZrRDoRTHW0TT0J3b+xA\nBw6kutrRWJwfPnsKm1XhQ+8wt977CpPjIxfHg4Sj8Zqazza0ep2EIvHLCoVSGhwN8Ms3+2j3ubjj\nqu6Sfz6XI3kAS1EU/v6Jo3zj0SN86yfHAPj0ezbz2+/dmvccaK1oanRwy85ljPrD7DkyuKCPNTha\nnK2Q5WBJ5YkndJ2fFphA8sJb55N551d0X/bkXlEUPnr7Bm7ctpTTA37++v8cJJIxnmM8OV9WpNER\nmEkeeSw16vSfv/kqP3qhh6GxIFeqHXz2/dv4y9+9id+8ZzNbVrdW7YHVTDft6OLKjR2c6Jvgj7/9\nOtOhGB955/oFjdyYKc9/CHwZeBfwjtS/2wr+jFXu9eNDTIdi3LyjK+uzl9VdTXga7BzuGTEV1TQZ\niDA0FmTtMm9ZRgdyMZulfahnceRnZ2r2JLtZxRwfMZaqNJUhcQSSHW2gKGkcxtjIJpMrxMvJ2A5Z\nsY52yFxHG2ZmrPe/nZzT/vnr5xjxh7j9qhWm81h3rm/HoijpjzGfcieOFFMlltb84NmTxBM6H3rH\n+qKuH89m3TIf771pNWOTYd44PsT6bh9//JvXcMO2rpo98GjWXdesxGpReOq1swXF5BoGRwM0OG1l\na2As1DVbOmn3uXjx4EDejy+xeIKf7e1N5Z3P/WTQoih88p5NXKl2cLx3nL/9t8Ppx4Bid7RhJnnk\nsZfPcKp/gs2rWviNezbxl797I599/3auVDvL9vdULIqi8Im7N9HscTAZiLJzXVvW6QYzzMwCbNI0\nbdOCPksNef7AeRTglp2XH4LMZFEUtq1pZc/RQfqGp1mR4+DM6YHkfHahYyPF1u5LjY5k6WgndJ3D\np0fwuR05v7564nPPRPwZ36eFKueyGiA9u1eM5JFjvWMoCjlHGyqh0ochjY52gzP3g0lncwPdHR6O\nnhllcDTAE6+epanRznuuX23683ka7Kgrmzl2doyxyfC80VhGXm6pN0KWQmahXY4DU8fOjrH/7Yts\n6PZdEuVaDvdev5rJYJTWJhd3XN1tastcPWj1urh+61JeOjTA/hMXubKA73sioTM4FmRFp7tmnphY\nLRbuvGYl///TJ/jFG+cuWUyVy54jg4z6w9x+ZXfWET6rxcJn7tvKX0cOcvDUCH//xFE+c99WBkYD\nNDXa0+N2xXD91qX0Dk6xbrmPazZ30uypnujXhfA02Pn3v7KDF97q51duXbfg3y8zf9WnVFVduaDP\nUiPODU1xsn+CbWvbTMXOzGyJzD0+cqo/dRCygMSRUmhvTo2OZOlo9w5OMhmIsm1ta83ckRWDkXPt\nL+KcdjmX1QDpqzELHR0JRWKcPu9n9VJvVc7oV7rQnkkdMfdzvWJjO7G4ztd/8BbhSJz33bw27++r\nMdJ2IMsCnL4aTBwxtHiMpTWlP3OfSOg8kjqM+sC7ihvnZ4bFovCx2zdy17UrF02Rbbj7upUowJN7\nzuS9wAdgxB8iFk/UxNhIppt3dOFttPPMvr55M5tnS+g6T712FqtF4c5rcpdjdpuFz/7KdjZ2+9h7\nbIh/fOoYw2NBujubivo73up18Tvv28a7r15RN0W2Ye0yL5+8e3NRmmPz/mWrqvqsqqrPAKuAQ6qq\nvqCq6jPGvwV/5ir03IFkpN9tc0T6zWXrmuTc8iEThbZxEHJNgauJi62pwY7Tbs16GPLQKSPWb/GM\njQD4SjA6kl5WU7bDkKlCe4Ed7bf7Jogn9Kqcz4bKF9qBcAwFcJnoaMPMjPXQeJDl7W5u2dmV9+c0\nM6fdNzxNo9NW8WUQhTCWRhU6OhKJxhkeD5r698t9ffQOTXH91qVVc9+8WHS1ubliYwenBybT42n5\nMBJHlrbUVqHtsFu54+oVBMNxnt1vLk98/4mLDIwEuG7rEtp85jZgOu1WPvfBZMLHy4cukND1kkRW\nityytVK+XK4bUQ1CkRivHr5AS5OTHevNFZZet4PVS5t4u2+CYDg275xmQtfpGfCzpLUxXRhUmqIo\ntDcns7R1XZ/zWe6h06MoCmxZvXgOQgLpxIHxImZppzvaZZrRtqcK7YVmaR+v0vxsg8thxWpRKncY\nMhTD5bSZPnexotNDu8/FxYkQH3nX+oK6mK1eF6uWNnG8d5zpUPSyQ3ORaJzBsQAblvtq8kpUejtk\nAYW2ruv88bdfT8+jmuGwWfjAraWL8xPzu+f6Vbx5YpifvHo278eZCzUS7TeXd+zu5sk9Z3n69XPc\ncdUKHFkWyOi6zpN7zqBA3ts7G102vvjhnfz59/Zz/uI03Z3VF6u3GMxbaGua9ryqqi2AVdO0iwCq\nqt4KHNU0zVyQaw3Ze2yIUCTOu69ekdeD37a1bZy5kHxGbnSaZhsYCRAMx9m9obo6Ju1eF/3D00yH\nYpc9AZgORTnVP8HaZd6qeXJQLkahXcwuqb/MoyPpGe0Fjo4cOzuG1aKwvrs6Rp5mUxQFT6O9gqMj\nMRpNdrNh5qDNhZEA29YUfqXoio0dnL0wycFTI1w/K1f//Mg0uk7Ndq8Wsh3y3NAUAyMBlrW7WdNl\nrqjYtb6dVq+5LqEorjVdXrasbuHomTFOD/jzuqpgJI7UQrTfbI0uW7rYfvnQAO+4Yv6km2Nnxzg9\nMMmVGzsKSr5oanTw0AO7+OWbfbzjqm5ieez+EMUxb6Gtqupu4EngN4Cfpl78buB7qqrerWnawTLc\nvrJ5bn8/ipL7EORsO9a28cQrZzjcMzJvoW2MjRS6qKZUZrK0g5cV00fPjKHri29sBDK2Q9by6Igt\n2clcyOjIdCjK2cFJNnQ3F2Vlb6l4GuyMlyD33IxAOEZbnkXa1tWtbF3gVaIrNnbw4xd62Hdi+LJC\nu2+odhNHIJng4nJYGfXnX2gfPp1MSbr3+lWXfV9Edbr3ulUcPTPGk6+e5bO/st30+xkZ2ktaq2ON\nd77uuHoFP3/9HE+91sstu5bN2+Az1rYbC28K0exx8oFb19HS5GJYCu2yy9a6/QrwUU3TjCLbWF7z\nm8DXSn3DyunMBT9nLkyyc13+nY01y5podNo4lCXmz9gIuXZZdXUFO7JkaS/W+WwAt8uG1aIUOd6v\nvKkjNuvCO9onesfR9eodGzE0NdiZDkaJJ4q3CdOMhK4TyrOjXSzL2hpZ0tLA4Z7RS7JyobYPQhpa\nmpwFrWE/dGoEhZnzM6L6bVrVwpquJvadGM65iCnT4GiAZo8Dl6P6Dmmb4XM7uHlHFxcnQuw9NjTn\n25we8HPs7BibV7XIGYIalq3QbtE07bnZL9Q07WfA/JtcapCZTZDzsVosbF3Tyog/PO9c4Kl+Pw6b\npeqWR7TPk6Wt6zqHTo/gabBX5arUUlMUBZ+nuNsh/YEIDrsFp6M8RZm9CIchj1X5fLbB02iM+pS3\nUxMKx9Exl6FdbIqicMXGDsLR+GXbaY1Ce3kNRvsZWpucTIdilz2JyCYYjnGyf4LVXU1le0IrFk5R\nFO65bjU68Ff/ejAdhZtNJBpnZCJUk2Mjme66diUWReHJPXPniT+Z6mbfu4Butqi8bIW2XVXVy16f\nelnd3IsFQjFeOzpIu8/FtgK7INvWzp8+EorE6L84xeqlTVUX3zTfdshzQ1NMTEXYtra1KpbrVILP\n7WBiOlJQ7NRcJgPRsj74F6Ojfax3DIfNUjXZ7/MxsqLPXMj9AF1MxubCSsUeGgtw9s2K+esbnqbd\n56rIE4BiSR+IzONA8tEzY8QT+oJm30VlXLGxnbuvW8nQWJA//ec3efyVM+m13nMZGg+iU5sHITN1\nNDdwzZZO+oenOXjy0vphYGSafSeGWdPVVPXNDpFdtsrveeBLc7z8D4E3SnNzym/P0QuEo3Fu2bms\n4PWg2fK0zwxMouuwtkryszPNtx3SmHNcjGMjBp/bSSyeKMoaaF3X8U9Hsi4ZKDabbWE52hPTEfqH\np9mwojnrhtRqsC41kmWMaJWL8btRqYJ27TIvPreDA29fTI/N+Kcj+KcjNT02AtBsHIjMY0778OnU\nuNu6xXu/VasUReFDt63noQd24XU7+PELPfy/39vH8Pjcex6MaL8lNRbtNxcjSeQns/LEn9xzFh24\n57rVNZkeJGZkewT9f4B3qqp6UlXVf1FV9fuqqp4geSDy8+W5eaWl6zrP7e/HalG4eUf+ebaGZo+T\nFZ0etHPjhCOXXuo8VaUHISFZILhdtss62jLnOLO0phiH7ILhGPGEXtY1wel4v1hhHXmttzbGRoB0\nukS5C+1AhTvaFkVh94Z2poJRTvYl72fS89lVNqaWr3yTR3Rd51DPCG6XjbUyy1qzNq9u5b996hqu\n3tTJyb4JvvQPe3nl8MBlVxaNaL9aHx2B5FmKXevbOdXv58S5cQBGJkLsOTJIV1sjuzfW1aTuojRv\noa1p2iRwC/AZkh3s14BPaZp2k6Zpo2W6fSU1HYrRNzzNFRs70kkThdq+to1YXOd476XzktV6ENLQ\n3tzAxYlQej5M5hyTmou4tMYfSM4OlytDG2ZSRwodHTHmszetrP5Cu9FlZ8USDz0D/qyXm4stUOGO\nNmQur7kIJMdGoLYPQsLMGnaz2yHPjwQY9YfZuqa14CuTojq4XXZ++71b+dS9mwH4+yeO8c1HjzCd\nkZaRjvZrq/1CG2YSRX6yJzmT/bO9vcQTOvdct2rRjm/Wk6yPEJqm6cAzqX91x9Ng5/d/9YqiHBra\nvraVJ/ec5VDPCDvXJ5+B6rrOqfN+WpqcVbuhrcPn4uyFSSamIrQ0OWXOMcXI0p6YXvjSmnInjkDG\nYcgFFNoNTiurltZGwaaubOXcYG9qKUN5bnMwtT65sYKF9qZVLTQ4rew7McwD71qfcRCyNn5u82nJ\ns6NtpCQt9vuteqEoCjdu72LjimYefvworx8f4mT/BJ9+zxY2r2rhwlgAi6KkzxnVuvXLfWxc0czh\nnlGOnB7lhbfO0+p1cu2WJZW+aaIIqnv4sgw2rmi+bLNaIdYt9+FyWDncM9PsHxoL4p+OVOXYiCEz\nSxtkztHgdScf6P1FGB2ZydAu3+hI+jBkAakjo/4QQ2NB1BUtVXeAdz5qasSlx0RiQbFUQ0fbZrWw\nY107I/4Q54am6BuawmZVWFqj2cIGI2bVbKFt3G8ZB9NFfehobuA//+pu3n/zGiamInzlX/bzg2dP\ncmEkQEezq+rPj+TDSBb5Xz86RCSW4M5rVtbV17eYyU+xSGxWC1tWtzI0Hkwf1DiRuvxerWMjcGmW\ntsw5zvAVcXSk3OvXYWGHIdNjIzUwn20wCu1T/RNl+5zpGe0Kp3sY4yNvaMOcvzjNsjZ3zTxBmo/b\nZcNuszBqotAORWKcODfOyk4PzQscARTVx2qxcN+Na/iDX7uSjpYGfvpaL1PBaM0njsy2bU0rK5d4\nCEfjeBrseS/PE9XL1L2xqqo3qqr626qqOlVVvaXUN6pWbZ8V83e8N9ndXre8eovWzCxtmXOcMTM6\nsvBCezwVUeYtY6FtX0C835mBSQA2VOna9bmsXOrFabeW9UBkOnWkQochDdvWtGKzWnhufz+RWKLm\nx0YgOTqQXFqTu9A+3jtOLK4v+qtw9W7tMi9f/o2ruWVnMrhg5ZL62vGgKAr33bAGgDuvWVHV23hF\nfnIW2qqq/gfgfwBfBDzA36mq+lCpb1gtMuLwDqXGR7SzY1gtCquq+A4hM0tb5hxnpAvtPHJ852NE\nVBlxiuVgtxWeOjKR6sC3Vum5grlYLQprupo4f3GaQGjhkYxmBKuko93gtLFldQtTweSI0ooyzaiX\nWmuTE/90JOeTRaOxUegeBFE7XA4bn7x7M3/y4LXcf+PqSt+cortS7eBPHryWu6+TBTX1xExH+5PA\nncC0pmkjwNUk17CLWVq9Lpa3u9F6xwiEYpzqm6C704Ojip+ZpgvtiaDMOWZw2K00OG1F6WgPj4ew\nKEpZC1fbAg5DTqa+ZndD+WbKi2HtMh86cLpMi2sCVXAY0mCMj8DMAp9aZxyIHM/yZFfXdQ6dGqHB\naWVdFe4qEKXR1eau2/nlrja3JI3UGTO/qXFN0zKrjRBgfi/uIrNtbSuRWIJfvHmOWDxR1QchAew2\nK80eB+dHAjLnOIuxHXKhhsaDtPmcZX1gsFkLj/ebDEZxu2w190Bm/K2Va3yk0gtrMu1a347x0FwP\noyOQsbQmy/jI0FiQixMhtqxqrbnfVyHE4mDmnul5VVW/ArhVVX0f8Bjwy9LerNpljI/8fO85YGZr\nXTVrb25IXaKVOcdMPreDqUB0QWvMQ5EY/ulIWcdGICPer4DUkclAebdYFouxKr6nTAciA+EYVouS\nHtOpJK/bwa4N7Sxvd6cz4Gtda1Pu5JGDPZKSJISobmZaMf8ReBB4C/h14Engm6W8UbVsQ3czTrs1\nnUiwtooPQho6fK70ZjmZc5zh8zjQgclAtOAcdGPrZmeZC20jdSTfJwmJhM5UIFqTG9d8HidtXhen\nzvvRdb3ka4uD4RiNLlvVrEf+d+/fhq5TNbdnodJLa7KsYTfiVOV+SwhRrXK2YjRNSwDfJXkY8vMk\nO9qSOzMPu82SXlvd1Ogoe4FViHZf8jbKnOOljJQQ/wLGR4YqcBASZg5D5tvRngpF0Snvcp1iWrfc\ny1Qwmj6AWkqBUKwqxkYMVoulrsYnci2tiUTjHO8dY3mHO527LYQQ1cZM6siXgEHgBeA54PnU/4p5\nGIcJ1VUtNdFdam9OPkjJnOOljFn1bIexcqlE4ghkLKyJ55c6Mmmsiy/jcp1iMjLryzGnHQzHquIg\nZL1qTRfac69hP3FunGgswXZJSRJCVDEzjxKfBFalEkeECVds7OBne3u5ZffySt8UU9SVLbQ0Obll\nl1yoyFSMLO2KdbQLzNE2Ekc8tdrRTs1pnzrv57qtS0v2eWLxBJFYoqo62vWmye3AalHm7Win57Ml\nJUkIUcXMPEqcB8q3bq0ONHuc/Plv30BHRxPDw5OVvjk5dTY38NXP3ljpm1F1ilFoV6qjbbEoWBQl\n79GRyWD518UX08olHqwWhZ7zpb3LSm+FrPCymnpmURSaPc55t0Me7hnFabeyvru5zLdMCCHMm/dR\nQlXVP0r95zjwqqqqTwHpTRCapv23Et82ISoqPaM9tYBCeyyIp8FekYLMZlPyztFOr4uv0Y623WZl\n5ZImegcniUTjJcuwr6Zov3rW4nXS0+8nkdAv2VY7PB7kwmiAXevbqyL1RQgh5pPtHkpJ/dsLPEEy\nO1vJ+CdEXfOlZrQnpgub0U4kdC5OhMrezTbYrZa8R0eMg5+1OqMNyfGReEKnd3CqZJ+jmpbV1LMW\nj5OErl92VemwjI0IIWpEtkeJM5qm/VPZbokQVaapwY6iFD46MjoZIp7Q6WypTKFts1mIFTw6Upsd\nbUhFar4JPecnWN9dmhSdalm/Xu8yk0cyIzYPGbF+a+UgpBCiumXraP+Hst0KIaqQxaLgdTuYKHB0\nZDiVod3RXJnosUI62rWeOgIzySOnSpg8YnS0ZXSktOZKHonGEhw7O8bS1saKXS0SQgizZLhNiCwW\nsoa9UgchDTarhWi+8X6pr9XdULuFdofPRVOjvaQHIoNyGLIsWlL52JkHIt/uGyccjae38AohRDXL\n9iixVVXVnjlergC6pmlrS3SbhKgaPreT3sEpQpEYLkd+RZVRaFdqaZHNaikodcTtstV0nrqiKKxb\n5uPAyYuMT4XTeejFJIchy2OupTXGNkiZzxZC1IJsjxIngXvKdUOEqEaZEX/5FtpDY5XtaNttSgGj\nI5GaTRzJtHaZlwMnL9Jz3s8VGzuK/vEDMqNdFq1zFNqHekZw2CyoKyXWTwhR/bI9SkQ0TTtbtlsi\nRBXyeVKF9lSEJS2Neb3v8HgQm9VCc1PxO6pm2K3Jw5C6rpvaUJpI6EwFoixtze/rrEZr04trJkpa\naEtHu7S8bgeKAmP+5Iz2qD9E/8Vptq9tw24rTXSjEEIUU7brwy+X7VYIUaXSWdoFzGkPjwfpaHZh\nMVHkloLNZkEH4glzc9pToSg6tZuhnWlNlxcF6OkvzYHIoHEYUma0S8pmteBzO9Iz2ock1k8IUWPm\nLbQ1Tfv35bwhQlQjY753fCq/LO3pUJTpUKyiqQi2PNewG4kjtboVMlOD08ayDjenL/iJJ/IbnzFD\nRkfKp6XJxfhUGF3XM+az5SCkEKI21O6JJyHKoNA17JVOHIHk6AhAzGTyiJE44qmDjjYkF9dEogn6\nh6eL/rFnDkPK+EKptTQ5icV1xqciHD07SmdzA0vqYLxJCLE4SKEtRBaFF9pGhnYFO9qp1dRmk0eM\nZTW1nKGdycjT7ilBnnYgHMPpsGK1yF1oqRnJI28cHyIYjrNNxkaEEDVEHiWEyKLQGe2hsQBQuWg/\nmOloR02PjiS/xlreCpkp80BksQXDMRkbKRMjeeSFt84DMjYihKgtUmgLkYXLYcVht+S9HbLSWyFh\npqNtdg278WSiXjray9rcuBzW0nS0Q1Jol4vR0e6/OI3NqrBpZUuFb5EQQpgnhbYQWSiKktoOmd9h\nSGNGu72ihyGTaSemD0OmR0fqo6NtsSis6fIyMBJgOhQt2sfVdZ1gOC7RfmXSkhGPqa49QN1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mgLIYTImxTaQtQ54wCfkTwyJVshL6EoCq1e17yHIWUrpBBCiEJJoS1EnZs9OpLeCtkgHW1Dm9fJ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a4d18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve_df.plot(x='year', \n",
    "    y='change_in_inflation', kind='line')\n",
    "\n",
    "plt.title(\"The Change in the Inflation Rate\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"The Change in the Inflation Rate\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/delong/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "image/png": 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ECSourrTVudZKTc08bA9Mp3x8o3z5xjNfO9NyGDZrNeW9GIKDYNyATiTERgY2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1bthmpJkiTtUrZ3x8jSrFUhSZIk5ZkPDNoxxguzWYgkSZKUT3rPGkiSJElSHjFoS5IkSSkwaEuS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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119abe7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve2_df = phillips_curve_df[1:]\n",
    "\n",
    "phillips_curve2_df.sort_values(by=['unemployment_rate'], inplace=True)\n",
    "\n",
    "%matplotlib inline \n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "\n",
    "x1 = phillips_curve2_df['unemployment_rate']\n",
    "y1 = phillips_curve2_df['change_in_inflation']\n",
    "\n",
    "lm = sm.OLS(y1, sm.add_constant(x1)).fit()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.scatter(x1, y1)\n",
    "plt.scatter(np.mean(x1), np.mean(y1), color = \"green\")\n",
    "plt.plot(np.sort(x1), lm.predict()[np.argsort(x1)], label = \"regression\")\n",
    "plt.title(\"The Annual Change in the Inflation Rate and the Unemployment Rate since 1957\")\n",
    "plt.xlabel(\"Unemployment Rate\")\n",
    "plt.ylabel(\"The Annual Change in the Inflation Rate\")\n",
    "plt.legend()\n",
    "\n",
    "for i, year in enumerate(phillips_curve2_df['year']):\n",
    "    ax.annotate(year, (x1.iloc[i], y1.iloc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.096\n",
      "Model:                             OLS   Adj. R-squared:                  0.081\n",
      "Method:                  Least Squares   F-statistic:                     6.301\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):             0.0148\n",
      "Time:                         10:15:52   Log-Likelihood:                 174.61\n",
      "No. Observations:                   61   AIC:                            -345.2\n",
      "Df Residuals:                       59   BIC:                            -341.0\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0177      0.007      2.411      0.019       0.003       0.032\n",
      "unemployment_rate    -0.2967      0.118     -2.510      0.015      -0.533      -0.060\n",
      "==============================================================================\n",
      "Omnibus:                       19.052   Durbin-Watson:                   2.002\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               51.120\n",
      "Skew:                           0.799   Prob(JB):                     7.93e-12\n",
      "Kurtosis:                       7.190   Cond. No.                         65.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "phillips_curve_df['const'] = 1\n",
    "\n",
    "reg1 = sm.OLS(endog=phillips_curve_df['change_in_inflation'], \n",
    "    exog=phillips_curve_df[['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results1 = reg1.fit()\n",
    "print(results1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.185\n",
      "Model:                             OLS   Adj. R-squared:                  0.156\n",
      "Method:                  Least Squares   F-statistic:                     6.343\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):             0.0178\n",
      "Time:                         10:52:47   Log-Likelihood:                 78.037\n",
      "No. Observations:                   30   AIC:                            -152.1\n",
      "Df Residuals:                       28   BIC:                            -149.3\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0336      0.014      2.473      0.020       0.006       0.061\n",
      "unemployment_rate    -0.5420      0.215     -2.519      0.018      -0.983      -0.101\n",
      "==============================================================================\n",
      "Omnibus:                        4.991   Durbin-Watson:                   2.026\n",
      "Prob(Omnibus):                  0.082   Jarque-Bera (JB):                3.311\n",
      "Skew:                           0.651   Prob(JB):                        0.191\n",
      "Kurtosis:                       3.976   Cond. No.                         63.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg1a = sm.OLS(endog=phillips_curve_df[:31]['change_in_inflation'], \n",
    "    exog=phillips_curve_df[:31][['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results1a = reg1a.fit()\n",
    "print(results1a.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.005\n",
      "Model:                             OLS   Adj. R-squared:                 -0.029\n",
      "Method:                  Least Squares   F-statistic:                    0.1593\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):              0.693\n",
      "Time:                         10:53:49   Log-Likelihood:                 116.07\n",
      "No. Observations:                   31   AIC:                            -228.1\n",
      "Df Residuals:                       29   BIC:                            -225.3\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0009      0.004      0.213      0.833      -0.008       0.010\n",
      "unemployment_rate    -0.0290      0.073     -0.399      0.693      -0.178       0.120\n",
      "==============================================================================\n",
      "Omnibus:                        4.187   Durbin-Watson:                   2.469\n",
      "Prob(Omnibus):                  0.123   Jarque-Bera (JB):                2.683\n",
      "Skew:                           0.505   Prob(JB):                        0.261\n",
      "Kurtosis:                       4.028   Cond. No.                         68.6\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "phillips_curve_df['const'] = 1\n",
    "\n",
    "reg1b = sm.OLS(endog=phillips_curve_df[31:]['change_in_inflation'], \n",
    "    exog=phillips_curve_df[31:][['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results1b = reg1b.fit()\n",
    "print(results1b.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DjsrMhwAi4svAbRjAJUmSpGlp9y4o/ewd1vuBB2e/HEmSJKm3tXsF/JPANyLi76rt/wT8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\ngEuSJEkFGcAlSZKkggzgkiRJUkH/P4eaPI11LlzDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a9f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve_df[31:].plot(x='unemployment_rate', \n",
    "    y='change_in_inflation', kind='scatter')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         core_inflation   R-squared:                       0.694\n",
      "Model:                            OLS   Adj. R-squared:                  0.684\n",
      "Method:                 Least Squares   F-statistic:                     65.84\n",
      "Date:                Wed, 23 Oct 2019   Prob (F-statistic):           1.20e-15\n",
      "Time:                        10:15:54   Log-Likelihood:                 176.29\n",
      "No. Observations:                  61   AIC:                            -346.6\n",
      "Df Residuals:                      58   BIC:                            -340.3\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0194      0.007      2.679      0.010       0.005       0.034\n",
      "unemployment_rate    -0.2431      0.120     -2.028      0.047      -0.483      -0.003\n",
      "lag_inflation         0.8618      0.075     11.439      0.000       0.711       1.013\n",
      "==============================================================================\n",
      "Omnibus:                       32.419   Durbin-Watson:                   1.857\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               94.607\n",
      "Skew:                           1.511   Prob(JB):                     2.86e-21\n",
      "Kurtosis:                       8.300   Cond. No.                         69.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "phillips_curve_df['lag_inflation'] = phillips_curve_df.shift(+1)['core_inflation']\n",
    "\n",
    "reg2 = sm.OLS(endog=phillips_curve_df['core_inflation'], \n",
    "    exog=phillips_curve_df[['const', 'unemployment_rate', 'lag_inflation']], \n",
    "    missing='drop')\n",
    "results2 = reg2.fit()\n",
    "print(results2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         core_inflation   R-squared:                       0.637\n",
      "Model:                            OLS   Adj. R-squared:                  0.610\n",
      "Method:                 Least Squares   F-statistic:                     23.69\n",
      "Date:                Wed, 23 Oct 2019   Prob (F-statistic):           1.15e-06\n",
      "Time:                        10:15:54   Log-Likelihood:                 78.760\n",
      "No. Observations:                  30   AIC:                            -151.5\n",
      "Df Residuals:                      27   BIC:                            -147.3\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0337      0.014      2.495      0.019       0.006       0.061\n",
      "unemployment_rate    -0.4358      0.234     -1.860      0.074      -0.917       0.045\n",
      "lag_inflation         0.8590      0.126      6.810      0.000       0.600       1.118\n",
      "==============================================================================\n",
      "Omnibus:                        9.065   Durbin-Watson:                   1.865\n",
      "Prob(Omnibus):                  0.011   Jarque-Bera (JB):                7.590\n",
      "Skew:                           0.990   Prob(JB):                       0.0225\n",
      "Kurtosis:                       4.468   Cond. No.                         71.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg3 = sm.OLS(endog=phillips_curve_df[:31]['core_inflation'], \n",
    "    exog=phillips_curve_df[:31][['const', 'unemployment_rate', 'lag_inflation']], \n",
    "    missing='drop')\n",
    "results3 = reg3.fit()\n",
    "print(results3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         core_inflation   R-squared:                       0.647\n",
      "Model:                            OLS   Adj. R-squared:                  0.628\n",
      "Method:                 Least Squares   F-statistic:                     33.96\n",
      "Date:                Wed, 23 Oct 2019   Prob (F-statistic):           4.23e-09\n",
      "Time:                        10:15:55   Log-Likelihood:                 109.77\n",
      "No. Observations:                  40   AIC:                            -213.5\n",
      "Df Residuals:                      37   BIC:                            -208.5\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0323      0.011      2.853      0.007       0.009       0.055\n",
      "unemployment_rate    -0.4376      0.196     -2.230      0.032      -0.835      -0.040\n",
      "lag_inflation         0.8701      0.106      8.176      0.000       0.654       1.086\n",
      "==============================================================================\n",
      "Omnibus:                       16.213   Durbin-Watson:                   1.895\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.667\n",
      "Skew:                           1.200   Prob(JB):                     1.97e-05\n",
      "Kurtosis:                       5.691   Cond. No.                         79.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg4 = sm.OLS(endog=phillips_curve_df[:41]['core_inflation'], \n",
    "    exog=phillips_curve_df[:41][['const', 'unemployment_rate', 'lag_inflation']], \n",
    "    missing='drop')\n",
    "results4 = reg4.fit()\n",
    "print(results4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:         core_inflation   R-squared:                       0.673\n",
      "Model:                            OLS   Adj. R-squared:                  0.659\n",
      "Method:                 Least Squares   F-statistic:                     48.38\n",
      "Date:                Wed, 23 Oct 2019   Prob (F-statistic):           3.88e-12\n",
      "Time:                        10:15:55   Log-Likelihood:                 141.15\n",
      "No. Observations:                  50   AIC:                            -276.3\n",
      "Df Residuals:                      47   BIC:                            -270.6\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0257      0.009      2.811      0.007       0.007       0.044\n",
      "unemployment_rate    -0.3677      0.170     -2.168      0.035      -0.709      -0.027\n",
      "lag_inflation         0.8930      0.094      9.544      0.000       0.705       1.081\n",
      "==============================================================================\n",
      "Omnibus:                       24.138   Durbin-Watson:                   1.906\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.910\n",
      "Skew:                           1.378   Prob(JB):                     2.40e-11\n",
      "Kurtosis:                       6.985   Cond. No.                         84.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg5 = sm.OLS(endog=phillips_curve_df[:51]['core_inflation'], \n",
    "    exog=phillips_curve_df[:51][['const', 'unemployment_rate', 'lag_inflation']], \n",
    "    missing='drop')\n",
    "results5 = reg5.fit()\n",
    "print(results5.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.185\n",
      "Model:                             OLS   Adj. R-squared:                  0.156\n",
      "Method:                  Least Squares   F-statistic:                     6.343\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):             0.0178\n",
      "Time:                         10:15:56   Log-Likelihood:                 78.037\n",
      "No. Observations:                   30   AIC:                            -152.1\n",
      "Df Residuals:                       28   BIC:                            -149.3\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0336      0.014      2.473      0.020       0.006       0.061\n",
      "unemployment_rate    -0.5420      0.215     -2.519      0.018      -0.983      -0.101\n",
      "==============================================================================\n",
      "Omnibus:                        4.991   Durbin-Watson:                   2.026\n",
      "Prob(Omnibus):                  0.082   Jarque-Bera (JB):                3.311\n",
      "Skew:                           0.651   Prob(JB):                        0.191\n",
      "Kurtosis:                       3.976   Cond. No.                         63.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg6 = sm.OLS(endog=phillips_curve_df[:31]['change_in_inflation'], \n",
    "    exog=phillips_curve_df[:31][['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results6 = reg6.fit()\n",
    "print(results6.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.183\n",
      "Model:                             OLS   Adj. R-squared:                  0.161\n",
      "Method:                  Least Squares   F-statistic:                     8.507\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):            0.00591\n",
      "Time:                         10:15:56   Log-Likelihood:                 109.04\n",
      "No. Observations:                   40   AIC:                            -214.1\n",
      "Df Residuals:                       38   BIC:                            -210.7\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0322      0.011      2.830      0.007       0.009       0.055\n",
      "unemployment_rate    -0.5300      0.182     -2.917      0.006      -0.898      -0.162\n",
      "==============================================================================\n",
      "Omnibus:                       10.342   Durbin-Watson:                   2.052\n",
      "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               11.650\n",
      "Skew:                           0.808   Prob(JB):                      0.00295\n",
      "Kurtosis:                       5.093   Cond. No.                         71.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg7 = sm.OLS(endog=phillips_curve_df[:41]['change_in_inflation'], \n",
    "    exog=phillips_curve_df[:41][['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results7 = reg7.fit()\n",
    "print(results7.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.156\n",
      "Model:                             OLS   Adj. R-squared:                  0.139\n",
      "Method:                  Least Squares   F-statistic:                     8.898\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):            0.00448\n",
      "Time:                         10:15:57   Log-Likelihood:                 140.53\n",
      "No. Observations:                   50   AIC:                            -277.1\n",
      "Df Residuals:                       48   BIC:                            -273.2\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0265      0.009      2.896      0.006       0.008       0.045\n",
      "unemployment_rate    -0.4527      0.152     -2.983      0.004      -0.758      -0.148\n",
      "==============================================================================\n",
      "Omnibus:                       17.190   Durbin-Watson:                   2.036\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               30.768\n",
      "Skew:                           0.983   Prob(JB):                     2.08e-07\n",
      "Kurtosis:                       6.302   Cond. No.                         72.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg8 = sm.OLS(endog=phillips_curve_df[:51]['change_in_inflation'], \n",
    "    exog=phillips_curve_df[:51][['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results8 = reg8.fit()\n",
    "print(results8.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "----\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rsquared values is 0.09648724931046992\n"
     ]
    },
    {
     "data": {
      "image/png": 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YRURERES8UMIsIiIiIuKFEmYRERERES+UMIuIiIiIeKGEWURERETEi4DaHoCI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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105360550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "\n",
    "x1 = phillips_curve2_df['unemployment_rate']\n",
    "y1 = phillips_curve2_df['change_in_inflation']\n",
    "\n",
    "lm = sm.OLS(y1, sm.add_constant(x1)).fit()\n",
    "print(\"The rsquared values is \" + str(lm.rsquared))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.scatter(x1, y1)\n",
    "plt.scatter(np.mean(x1), np.mean(y1), color = \"green\")\n",
    "plt.plot(np.sort(x1), lm.predict()[np.argsort(x1)], label = \"regression\")\n",
    "plt.title(\"Linear Regression plots with the regression line\")\n",
    "plt.legend()\n",
    "\n",
    "for i, year in enumerate(phillips_curve2_df['year']):\n",
    "    ax.annotate(year, (x1.iloc[i], y1.iloc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iIiJDoCRaUpLT4eDs4yexaN5Y3t9chWdvI7UN7TiiKJi2FiaW5HDysROYXpaD06H5tCIi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\nfT1fGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a85c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "fig = sm.graphics.influence_plot(lm, alpha  = 0.05, ax = ax, criterion=\"cooks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FnyIi0jhUMMuKlmcw2/qyIxnqMJ/PjpRr95fbYc6mZDhsJKOchAxbe4uymEVE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\nwSdLFxo0IWO21f0d7Nnax9Gz4xzKJRI5jp8f52fHhtm+rqvuXp2W0jT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jP8NcRIc5lc4w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\nQAWziIiIiEgB/wdCCagMpR0RpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c32860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve_df.plot(x='year', \n",
    "    y='change_in_inflation', kind='line')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gMIYBx88utHtZXUeBsYh0rEZLKZyM8cqqMsYi4o6U05Gi77kZ477eMIfGB5icXiaT03Wq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a4b56d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phillips_curve_df.plot(x='year', \n",
    "    y='unemployment_rate', kind='line')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     change_in_inflation   R-squared:                       0.096\n",
      "Model:                             OLS   Adj. R-squared:                  0.081\n",
      "Method:                  Least Squares   F-statistic:                     6.301\n",
      "Date:                 Wed, 23 Oct 2019   Prob (F-statistic):             0.0148\n",
      "Time:                         10:21:33   Log-Likelihood:                 174.61\n",
      "No. Observations:                   61   AIC:                            -345.2\n",
      "Df Residuals:                       59   BIC:                            -341.0\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0177      0.007      2.411      0.019       0.003       0.032\n",
      "unemployment_rate    -0.2967      0.118     -2.510      0.015      -0.533      -0.060\n",
      "==============================================================================\n",
      "Omnibus:                       19.052   Durbin-Watson:                   2.653\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               51.120\n",
      "Skew:                           0.799   Prob(JB):                     7.93e-12\n",
      "Kurtosis:                       7.190   Cond. No.                         65.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/delong/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/delong/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Users/delong/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "phillips_curve2_df['lag_ch_inflation'] = phillips_curve2_df.shift(+1)['change_in_inflation']\n",
    "phillips_curve2_df['lead_ch_inflation'] = phillips_curve2_df.shift(-1)['change_in_inflation']\n",
    "phillips_curve2_df['const'] = 1\n",
    "\n",
    "reg9 = sm.OLS(endog=phillips_curve2_df['change_in_inflation'], \n",
    "    exog=phillips_curve2_df[['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results9 = reg9.fit()\n",
    "print(results9.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       lag_ch_inflation   R-squared:                       0.122\n",
      "Model:                            OLS   Adj. R-squared:                  0.107\n",
      "Method:                 Least Squares   F-statistic:                     8.094\n",
      "Date:                Wed, 23 Oct 2019   Prob (F-statistic):            0.00613\n",
      "Time:                        10:21:44   Log-Likelihood:                 172.17\n",
      "No. Observations:                  60   AIC:                            -340.3\n",
      "Df Residuals:                      58   BIC:                            -336.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0205      0.008      2.731      0.008       0.005       0.036\n",
      "unemployment_rate    -0.3425      0.120     -2.845      0.006      -0.584      -0.102\n",
      "==============================================================================\n",
      "Omnibus:                       20.792   Durbin-Watson:                   2.723\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               52.408\n",
      "Skew:                           0.939   Prob(JB):                     4.17e-12\n",
      "Kurtosis:                       7.176   Cond. No.                         67.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg10 = sm.OLS(endog=phillips_curve2_df['lag_ch_inflation'], \n",
    "    exog=phillips_curve2_df[['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results10 = reg10.fit()\n",
    "print(results10.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      lead_ch_inflation   R-squared:                       0.097\n",
      "Model:                            OLS   Adj. R-squared:                  0.081\n",
      "Method:                 Least Squares   F-statistic:                     6.225\n",
      "Date:                Wed, 23 Oct 2019   Prob (F-statistic):             0.0155\n",
      "Time:                        10:22:03   Log-Likelihood:                 171.51\n",
      "No. Observations:                  60   AIC:                            -339.0\n",
      "Df Residuals:                      58   BIC:                            -334.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const                 0.0186      0.008      2.384      0.020       0.003       0.034\n",
      "unemployment_rate    -0.3190      0.128     -2.495      0.015      -0.575      -0.063\n",
      "==============================================================================\n",
      "Omnibus:                       18.783   Durbin-Watson:                   2.664\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.257\n",
      "Skew:                           0.813   Prob(JB):                     3.32e-11\n",
      "Kurtosis:                       7.082   Cond. No.                         70.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "reg11 = sm.OLS(endog=phillips_curve2_df['lead_ch_inflation'], \n",
    "    exog=phillips_curve2_df[['const', 'unemployment_rate']], \n",
    "    missing='drop')\n",
    "results11 = reg11.fit()\n",
    "print(results11.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<https://songhuiming.github.io/pages/2016/11/27/linear-regression-in-python-outliers-leverage-detect/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "----\n",
    "\n",
    "And how is it, exactly, that \"the difference between national and city/state results in recent decades can be explained by the success that monetary policy has had in quelling inflation and anchoring inflation expectations since the 1980s\"? Neither of those two should affect the estimated coefficient. Much more likely is simply that—at the national level _and_ at the city/state level—the Phillips Curve becomes flat when inflation becomes low.\n",
    "\n",
    "the evidence for \"significant nonlinearity\" in the Phillips Curve is that the curve flattens when _inflation_ is low, not that it steepens when _labor slack_ is low. There is simply no \"strong evidence\" of significant steepening with low labor slack. Yes, you can find specifications with a t-statistic of 2 in which this is the case, but you have to work hard to find such specifications, and your results are fragile. \n",
    "\n",
    "The most important observations driving the estimated zero slope of the Phillips Curve in the second half of the past sixty years have been 2009-2014: the failure of inflation to fall as the economy took its Great-Recession excursion to a high-unemployment labor market with enormous slack."
   ]
  }
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