{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "# 2018-02-20 Econ 101b Problem Set 8 with DRAFT answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem set will be due Th Mar 15 at midnight...\n",
    "\n",
    "#### Sections between Tu AM & Th AM lecture will be problem set section..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the Python/Jupyter environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;}"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ABOVE CELL IS \"NO SCROLLING SUBWINDOWS\" SETUP\n",
    "#\n",
    "# keep output cells from shifting to autoscroll: little scrolling\n",
    "# subwindows within the notebook are an annoyance..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/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": [
    "# THIS CELL LOADS THE LIBRARIES\n",
    "#\n",
    "# set up the environment by reading in every library we might need: \n",
    "# os... graphics... data manipulation... time... math... statistics...\n",
    "\n",
    "import sys\n",
    "import os\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from datetime import datetime\n",
    "\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import math\n",
    "import random\n",
    "\n",
    "import seaborn as sns\n",
    "import statsmodels\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PRETTIER GRAPHICS SETUP\n",
    "#\n",
    "# graphics setup: seaborn-whitegrid and figure size;\n",
    "# graphs in the notebook itself...\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "figure_size = plt.rcParams[\"figure.figsize\"]\n",
    "figure_size[0] = 12\n",
    "figure_size[1] = 10\n",
    "plt.rcParams[\"figure.figsize\"] = figure_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: okpy in /Users/delong/anaconda3/lib/python3.6/site-packages\n",
      "Requirement already up-to-date: requests==2.12.4 in /Users/delong/anaconda3/lib/python3.6/site-packages (from okpy)\n",
      "Requirement already up-to-date: coverage==3.7.1 in /Users/delong/anaconda3/lib/python3.6/site-packages (from okpy)\n",
      "=====================================================================\n",
      "Assignment: PS8 Notebook\n",
      "OK, version v1.13.10\n",
      "=====================================================================\n",
      "\n",
      "\n",
      "Open the following URL:\n",
      "\n",
      "https://okpy.org/client/login/\n",
      "\n",
      "After logging in, copy the code from the web page and paste it into the box.\n",
      "Then press the \"Enter\" key on your keyboard.\n",
      "\n",
      "Paste your code here: FmiyGZenS8AOBCpn8qN0cvidg4R52q\n",
      "Successfully logged in as jbdelong@berkeley.edu\n"
     ]
    }
   ],
   "source": [
    "# THIS CELL IS THE KEY TO THE OKPY.ORG AUTOGRADER SYSTEM\n",
    "#\n",
    "# Don't change this cell; just run it. \n",
    "# The result will give you directions about how to log in to the submission system, called OK.\n",
    "# Once you're logged in, you can run this cell again, but it won't ask you who you are because\n",
    "# it remembers you. However, you will need to log in once per assignment.\n",
    "\n",
    "!pip install -U okpy\n",
    "\n",
    "from client.api.notebook import Notebook\n",
    "\n",
    "ok = Notebook('ps8.ok')\n",
    "_ = ok.auth(force=True, inline=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎⬆︎\n",
    "### RUN THESE CELLS ABOVE FIRST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Set 8 (Due Mar 15 11:59:59 PM)\n",
    "\n",
    "----\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The autograder, both in the tests you run along the way as you work on the problem set and in calculating the final score, looks in the same directory as the problem set notebook for an \"ok.tests\" directory, and then runs the tests in the \"q\\*\\*.py\" files in that directory (where \"\\*\\*\" denotes a two-digit number, possibly with a leading zero). Those tests take the form of comparing a variable that should be in your namespace and seeing if it is close to some desired value that we get when we do the problem set.\n",
    "\n",
    "Thus while the problem set instructions ask you to run simulations and plot graphs, what you are tested on is whether the appropriate variables in your namespace have (close to the) right values. We do not care what code you use in order to get those variables to the right values. \n",
    "\n",
    "You can run simulations and then pick appropriate values out by slicing a series in order to get the right number. \n",
    "\n",
    "You can use your knowledge of the algebraic solution to the model to have Python calculate the answer, having first set the parameters to the right values, as in:\n",
    "\n",
    "    s = 0.24 # (say)\n",
    "    n = 0.01\n",
    "    g = 0.02\n",
    "    delta = 0.03\n",
    "    Delta_n = -0.01\n",
    "    KoverYinitial = s/(n+g+delta)\n",
    "    KoverYalternative = s/(n+Delta_n+g+delta)\n",
    "    \n",
    "You can even do all of the calculations on pen and paper, and simply code up:\n",
    "\n",
    "    KoverYinitial = 4\n",
    "    KoverYalternative = 4.8\n",
    "    \n",
    "Perhaps we should ask you to do all three—start with simulations, or with algebraic equations with set parameter values, or with full pen-and-paper calculations with only the final results entered into the notebook—and then ask you to check your results from one mode by doing the other two. But: _ars longa, vita brevis_. Focus on what works _for you_: the key is to get a sense of how economists' center-of-gravity analyses of long-run growth work, so that when you encounter such an analysis later, outside the university, you have the right intellectual panoply to evaluate it.\n",
    "\n",
    "----\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Programming Dos and Don'ts...\n",
    "\n",
    "### A Running List...\n",
    "\n",
    "1. **Do** restart your kernel and run cells up to your current working point every fifteen minutes or so. Yes, it takes a little time. But if you don't, sooner or later the machine's namespace will get confused, and then you will get confused about the state of the machine's namespace, and by assuming things about it that are false you will lose hours and hours...   \n",
    "&nbsp;\n",
    "\n",
    "2. **Do** reload the page when restarting the kernel does not seem to do the job...   \n",
    "&nbsp;\n",
    "\n",
    "3. **Do** edit code cells by copying them below your current version and then working on the copy: when you break everything in the current cell (as you will), you can then go back to the old cell and start fresh...   \n",
    "&nbsp;\n",
    "\n",
    "4. **Do** exercise agile development practices: if there is a line of code that you have not tested, test it. The best way to test is to ask the machine to echo back to you the thing you have just created in its namespace to make sure that it is what you want it to be. Only after you are **certain** that your namespace contains what you think it does should you write the next line of code. And then you should immediately test it...   \n",
    "&nbsp;\n",
    "\n",
    "5. **Do** take screenshots of your error messages...   \n",
    "&nbsp;\n",
    "\n",
    "6. **Do** google your error messages: Ms. Google is your best friend here...   \n",
    "&nbsp;\n",
    "\n",
    "7. **Do not** confuse assignment (\"=\") and test for equality (\"==\"). In general, if there is an \"if\" anywhere nearby, you should be testing for equality. If there is not, you should be assignment a variable in your namespace to a value. **Do** curse the mathematicians 500 years ago who did not realize that in the twenty-first century it would be very convenient if we had different and not confusable symbols for equals-as-assignment and equals-as-test...   \n",
    "&nbsp;\n",
    "\n",
    "----\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "**Thanks to**: Rachel Grossberg, Christopher Hench, Meghana Krishnakumer, Seth Lloyd, Ronald Walker..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "manual_grade": true,
    "manual_problem_id": "PS6TB"
   },
   "source": [
    "**(Task A) Programming Practices**\n",
    "\n",
    "If it strikes you that anything should be added to this list of programming dos and don'ts, please email it to me at <delong@econ.berkeley.edu>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task B) Consumption Series I**\n",
    "\n",
    "1. Websurf over to http://fred.stlouisfed.org\n",
    "2. Type \"consumption\" into the search box and press return\n",
    "3. Scan down the list of variables, focusing on variables that are \"real\", \"seasonally adjusted\", and \"monthly\".\n",
    "4. Find \"Real Personal Consumption Expenditures: Billions of Chained 2009 Dollars, Monthly, Seasonally Adjusted Annual Rate\". Click on the box to select it. Click on the \"Add to Graph\" button near the top.\n",
    "5. Click the \"DOWNLOAD\" button and then the \"CSV (Data)\" option\n",
    "6. Open the file you have just downloaded, PCEC96.csv, in a text editor. Look at it. Does it look like the right thing? If not, email me at delong@econ.berkeley.edu with the subject line: \"Econ 101b S2018 PS8: PCEC96.csv Does Not Look Right\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task C) Consumption Series II**\n",
    "\n",
    "1. I have put a copy of PCEC96.csv up on the web at URL: http://delong.typepad.com/pcec96.csv\n",
    "2. Run the code cell \"# Consumption Series Import Cell\" below to load the \"Real Personal Consumption Expenditures\" data into your namespace in the cons_df object.\n",
    "3. In the \"# Examine Consumption Series\" code cell further below, write code to examine what is in the \"cons_df\" object.\n",
    "4. Plot the Real Personal Consumption Expenditures series PCEC96:\n",
    "In the \"# Task C Answers\" code cell further below set the variables \"Cons_Jan_2000\" and \"Cons_Jan_2010\" to their correct values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Consumption Series Import Cell\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "Source_URL = 'http://delong.typepad.com/pcec96.csv' \n",
    "\n",
    "cons_df = pd.read_csv(\n",
    "    Source_URL, \n",
    "    converters = {'Source': str, 'Source_URL': str}, \n",
    "    parse_dates = True,\n",
    "    index_col = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            PCEC96\n",
      "DATE              \n",
      "1999-01-01  7582.0\n",
      "1999-02-01  7620.0\n",
      "1999-03-01  7654.1\n",
      "1999-04-01  7697.8\n",
      "1999-05-01  7731.6\n",
      "DATE\n",
      "1999-01-01    7582.0\n",
      "1999-02-01    7620.0\n",
      "1999-03-01    7654.1\n",
      "1999-04-01    7697.8\n",
      "1999-05-01    7731.6\n",
      "Name: PCEC96, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1102ba4a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RehTKMwAAgJcwDEMvvpuljAOlSh4VpftvGieLxWJ2rB6F8gwAAOAlVqUd0todBRoWE6ZH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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11038b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine Consumption Series\n",
    "\n",
    "print(cons_df.head())\n",
    "print(cons_df.PCEC96.head())\n",
    "cons_df.PCEC96.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7988.5\n",
      "9881.7\n"
     ]
    }
   ],
   "source": [
    "# Task C Answers\n",
    "\n",
    "cons_Jan_2000 = cons_df.PCEC96[12]\n",
    "cons_Jan_2010 = cons_df.PCEC96[132]\n",
    "\n",
    "print(cons_Jan_2000)\n",
    "print(cons_Jan_2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failed': 0, 'locked': 0, 'passed': 2}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ok.grade('q01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task D) GDP Series I**\n",
    "\n",
    "1. Websurf over to http://fred.stlouisfed.org\n",
    "2. Type \"real gdp\" into the search box and press return\n",
    "3. Scan down the list of variables, focusing on variables that are \"real\", \"seasonally adjusted\", and \"quarterly\".\n",
    "4. Find \"Real Gross Domestic Product: Billions of Chained 2009 Dollars, Quarterly, Seasonally Adjusted Annual Rate\". Click on the box to select it. Click on the \"Add to Graph\" button near the top.\n",
    "5. Enter '1999-01-01' into the left of the two date boxes to the left of the red \"EDIT GRAPH\" button and press return\n",
    "6. Click the \"DOWNLOAD\" button and then the \"CSV (Data)\" option\n",
    "7. Open the file you have just downloaded, GDPC1.csv, in a text editor. Look at it. Does it look like the right thing? If not, email me at delong@econ.berkeley.edu with the subject line: \"Econ 101b S2018 PS8: GDPC1.csv Does Not Look Right\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task E) GDP Series II**\n",
    "\n",
    "1. I have put a copy of GDPC1.csv up on the web at URL: http://delong.typepad.com/gdpc1.csv\n",
    "2. Run the code cell \"# GDP Series Import Cell\" below to load the \"Real GDP\" data into your namespace in the cons_df object.\n",
    "3. In the \"# Examine GDP Series\" code cell further below, write code to examine what is in the \"cons_df\" object.\n",
    "4. Plot the Real Personal Consumption Expenditures series PCEC96:\n",
    "In the \"# Task D Answers\" code cell further below set the variables \"Cons_Jan_2000\" and \"Cons_Jan_2010\" to their correct values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GDP Series Import Cell\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "Source_URL = 'http://delong.typepad.com/gdpc1.csv' \n",
    "\n",
    "gdp_df = pd.read_csv(\n",
    "    Source_URL, \n",
    "    converters = {'Source': str, 'Source_URL': str}, \n",
    "    parse_dates = True,\n",
    "    index_col = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                GDPC1\n",
      "DATE                 \n",
      "1999-01-01  11864.675\n",
      "1999-04-01  11962.524\n",
      "1999-07-01  12113.075\n",
      "1999-10-01  12323.336\n",
      "2000-01-01  12359.095\n",
      "DATE\n",
      "1999-01-01    11864.675\n",
      "1999-04-01    11962.524\n",
      "1999-07-01    12113.075\n",
      "1999-10-01    12323.336\n",
      "2000-01-01    12359.095\n",
      "Name: GDPC1, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1100e8208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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suFrzX96gimqLfnVDnH4xKtroSIDbojwDAODCCktrNe+lNJVWNmj2NUN1xYV9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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110498be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine GDP Series\n",
    "\n",
    "print(gdp_df.head())\n",
    "print(gdp_df.GDPC1.head())\n",
    "gdp_df.GDPC1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12359.095\n",
      "14604.845\n"
     ]
    }
   ],
   "source": [
    "# Task D Answers\n",
    "\n",
    "gdp_Jan_2000 = gdp_df.GDPC1[4]\n",
    "gdp_Jan_2010 = gdp_df.GDPC1[44]\n",
    "\n",
    "print(gdp_Jan_2000)\n",
    "print(gdp_Jan_2010)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failed': 0, 'locked': 0, 'passed': 2}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ok.grade('q02')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task F) The Consumption Function I**:\n",
    "\n",
    "1. Run the code cell \"Graph consumption vs. income\" immediately below, importing and plotting real consumption spending and real GDP since the early post-WWII period into a single dataframe. Examine the plot. Email me at delong@econ.berkeley.edu with the subject line \"Econ 101b S2018 PS8: gdp_cons_df Does Not Look Right\" if anything looks wrong to you\n",
    "2. Run the code cell \"Graph consumption vs. income\" further below. That code cell performs a regression analysis: if consumption spending C is a simple linear function of GDP—national income—Y of the form $ C = a + bY + \\epsilon $, where $ \\epsilon $ are a host of additional variables not predictable from Y, what are the most likely values of the coefficients a and b, and how good a job do those coefficients and that function do at capturing the determinants of C?\n",
    "3. The regression analysis tells us that the estimated value of the slope coefficient b from the regression of consumption spending C on income Y is 0.6607. What is the relationship between this coefficient and the MPC—the parameter $ c_y $ in our simple flexprice business cycle model? Assume that the tax rate t in the simple model is 0.25. In the code cell further below \"Task F answers\", set the variable c_y equal to your estimate of what the value of b tells us that the MPC is. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         DATE   PCECC96     GDPC1\n",
      "0  1947-01-01  1199.413  1934.471\n",
      "1  1947-04-01  1219.329  1932.281\n",
      "2  1947-07-01  1223.266  1930.315\n",
      "3  1947-10-01  1223.649  1960.705\n",
      "4  1948-01-01  1229.757  1989.535\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114530b38>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZREREROqN/OKzbDu2iw5Ng7gn8rZ6VZyhkuU5KSmJhIQEAHJycnjwwQe5/fbbGTduHEeP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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11058b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph consumption vs. income\n",
    "\n",
    "Source_URL = 'http://delong.typepad.com/gdp_cons.csv'\n",
    "\n",
    "gdp_cons_df = pd.read_csv(\n",
    "    Source_URL, \n",
    "    converters = {'Source': str, 'Source_URL': str}, \n",
    "    parse_dates = True\n",
    "    )\n",
    "\n",
    "print(gdp_cons_df.head())\n",
    "\n",
    "gdp_cons_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                PCECC96   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.999\n",
      "Method:                 Least Squares   F-statistic:                 1.972e+05\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):               0.00\n",
      "Time:                        07:58:05   Log-Likelihood:                -1949.7\n",
      "No. Observations:                 284   AIC:                             3901.\n",
      "Df Residuals:                     283   BIC:                             3905.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "GDPC1          0.6607      0.001    444.064      0.000       0.658       0.664\n",
      "==============================================================================\n",
      "Omnibus:                       71.956   Durbin-Watson:                   0.019\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              121.436\n",
      "Skew:                           1.476   Prob(JB):                     4.27e-27\n",
      "Kurtosis:                       4.245   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.PCECC96, gdp_cons_df.GDPC1).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8809333333333332\n"
     ]
    }
   ],
   "source": [
    "# Task F answers\n",
    "\n",
    "t = 0.25\n",
    "c_y = 0.6607/(1-t)\n",
    "print(c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 1\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failed': 0, 'locked': 0, 'passed': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ok.grade('q03')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task G) Consumption Function II**\n",
    "\n",
    "Task F produced a very high estimate of the marginal propensity to consume, MPC, $ c_y $. It seemed to indicate that households do not smooth spending very much at all—that they take virtually all of a change in their income and spend it. This estimate was obtained because the overwhelming bulk of the identifying variance in the regression analysis of Task F consisted of permanent, long run changes in actual and expected income. Now let us look at shorter run, more temporary changes. We do this by performing a regression analysis in which the dependent and independent variables are not the simple levels of consumption spending C and national income Y, but rather one year changes in those variables scaled by the then-current level of real national income.\n",
    "\n",
    "1. Run the code cell below \"Calculating one year changes\", which calculates and then plots one year changes in consumption spending C and national income Y, both of them as fractions of the then-current level of national income Y. Examine the plot. Email me at delong@econ.berkeley.edu with the subject line \"Econ 101b S2018 PS8: Delta_C and Delta_Y DO Not Look Right\" if anything looks wrong to you.\n",
    "2. Run the code cell further below \"1 year changes regression: full sample\". Use the estimated b coefficient—the number immediately to the right of the \"Delta_Y\" under the heading \"coef\"—to calculate the estimate of the MPC $ c_y $ produced from examining one year changes. Assume once again that the tax rate t is 0.25. In the code cell \"Task G answers\", set the variable c_y_1_year equal to the estimate of the MPC.\n",
    "3. Duplicate the code cell \"Calculating one year changes\". Then paste it immediately below. Alter the first line of the pasted version to read \"# Calculating one year changes: earlier half of the sample\"\n",
    "4. Change the code in \"# Calculating one year changes: earlier half of the sample\" so that it runs the regression analysis for 1 year changes for only the first half of our 280 observation-long post-WWII data. In the code cell \"Task G answers\", set the variable c_y_1_year_early_sample equal to the estimate of the MPC for the first half of the sample.\n",
    "5. Duplicate the code cell \"Calculating one year changes\". Then paste it immediately below. Alter the first line of the pasted version to read \"# Calculating one year changes: later half of the sample\"\n",
    "6. Change the code in \"# Calculating one year changes: later half of the sample\" so that it runs the regression analysis for 1 year changes for only the second half of our 280 observation-long post-WWII data. In the code cell \"Task G answers\", set the variable c_y_1_year_late_sample equal to the estimate of the MPC for the second half of the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         DATE   PCECC96     GDPC1   Delta_C   Delta_Y\n",
      "0  1947-01-01  1199.413  1934.471  0.015686  0.028465\n",
      "1  1947-04-01  1219.329  1932.281  0.012799  0.046355\n",
      "2  1947-07-01  1223.266  1930.315  0.011722  0.053276\n",
      "3  1947-10-01  1223.649  1960.705  0.016416  0.038060\n",
      "4  1948-01-01  1229.757  1989.535  0.014130  0.009041\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x114c23828>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lNxNcjjaLGU9mQsvT1dk6aQFk98dmMWsP1ABKMgAgpIKaCV6zclFBDXP+cTfYVniy4prY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147d7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating one year changes\n",
    "\n",
    "Delta_C = []\n",
    "Delta_Y = []\n",
    "\n",
    "for t in range(280):\n",
    "    Delta_C = Delta_C + [(gdp_cons_df.PCECC96[t+4]-\n",
    "        gdp_cons_df.PCECC96[t])/gdp_cons_df.GDPC1[t]]\n",
    "    Delta_Y = Delta_Y + [(gdp_cons_df.GDPC1[t+4]-\n",
    "        gdp_cons_df.GDPC1[t])/gdp_cons_df.GDPC1[t]]\n",
    "\n",
    "Delta_C = Delta_C + [0] + [0] + [0] + [0]\n",
    "Delta_Y = Delta_Y + [0] + [0] + [0] + [0]\n",
    "\n",
    "gdp_cons_df['Delta_C'] = Delta_C\n",
    "gdp_cons_df['Delta_Y'] = Delta_Y\n",
    "\n",
    "print(gdp_cons_df.head())\n",
    "\n",
    "plt.plot(gdp_cons_df.Delta_Y[:280], gdp_cons_df.Delta_C[:280], \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Delta_C   R-squared:                       0.846\n",
      "Model:                            OLS   Adj. R-squared:                  0.845\n",
      "Method:                 Least Squares   F-statistic:                     1531.\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):          2.66e-115\n",
      "Time:                        07:58:29   Log-Likelihood:                 903.98\n",
      "No. Observations:                 280   AIC:                            -1806.\n",
      "Df Residuals:                     279   BIC:                            -1802.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Delta_Y        0.5452      0.014     39.130      0.000       0.518       0.573\n",
      "==============================================================================\n",
      "Omnibus:                      149.401   Durbin-Watson:                   0.567\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1385.173\n",
      "Skew:                          -1.959   Prob(JB):                    1.63e-301\n",
      "Kurtosis:                      13.168   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 1 year changes regression: full sample\n",
    "\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.Delta_C[:280], gdp_cons_df.Delta_Y[:280]).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Delta_C   R-squared:                       0.808\n",
      "Model:                            OLS   Adj. R-squared:                  0.807\n",
      "Method:                 Least Squares   F-statistic:                     585.5\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):           1.10e-51\n",
      "Time:                        07:58:36   Log-Likelihood:                 428.04\n",
      "No. Observations:                 140   AIC:                            -854.1\n",
      "Df Residuals:                     139   BIC:                            -851.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Delta_Y        0.4933      0.020     24.198      0.000       0.453       0.534\n",
      "==============================================================================\n",
      "Omnibus:                       69.393   Durbin-Watson:                   0.600\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              320.868\n",
      "Skew:                          -1.752   Prob(JB):                     2.11e-70\n",
      "Kurtosis:                       9.537   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 1 year changes regression: early sample\n",
    "\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.Delta_C[:140], gdp_cons_df.Delta_Y[:140]).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Delta_C   R-squared:                       0.932\n",
      "Model:                            OLS   Adj. R-squared:                  0.931\n",
      "Method:                 Least Squares   F-statistic:                     1881.\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):           2.80e-82\n",
      "Time:                        07:58:41   Log-Likelihood:                 515.15\n",
      "No. Observations:                 139   AIC:                            -1028.\n",
      "Df Residuals:                     138   BIC:                            -1025.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Delta_Y        0.6445      0.015     43.368      0.000       0.615       0.674\n",
      "==============================================================================\n",
      "Omnibus:                        6.614   Durbin-Watson:                   0.601\n",
      "Prob(Omnibus):                  0.037   Jarque-Bera (JB):                6.176\n",
      "Skew:                          -0.478   Prob(JB):                       0.0456\n",
      "Kurtosis:                       3.389   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 1 year changes regression: late sample\n",
    "\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.Delta_C[141:280], gdp_cons_df.Delta_Y[141:280]).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7269333333333333\n",
      "0.6577333333333334\n",
      "0.8593333333333333\n"
     ]
    }
   ],
   "source": [
    "# Task G answers\n",
    "\n",
    "t = 0.25\n",
    "c_y_1_year = 0.5452/(1-t)\n",
    "c_y_1_year_early_sample = 0.4933/(1-t)\n",
    "c_y_1_year_late_sample = 0.6445/(1-t)\n",
    "\n",
    "print(c_y_1_year)\n",
    "print(c_y_1_year_early_sample)\n",
    "print(c_y_1_year_late_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 3\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failed': 0, 'locked': 0, 'passed': 3}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ok.grade('q04')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "&nbsp;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(Task H) Consumption Function III**\n",
    "\n",
    "Task F produced a very high estimate of the marginal propensity to consume out of disposable income, MPC, $ c_y $, indicating that households do not smooth spending very much at all—that they take virtually all of a change in their income and spend it. This estimate captures the MPC in response to permanent, long run changes in actual and expected income. But the MPC that is relevant for business cycle analysis is a short run MPC: the response of consumption to surprise, transitory changes in national income.\n",
    "\n",
    "Task G produed somewhat smaller estimates of the MPC by looking at the reaction of consumption spending to one-year changes in national income.\n",
    "\n",
    "Now let us look at one quarter changes. We do this by performing a regression analysis in which the dependent and independent variables are oen quarter changes in those variables scaled by the then-current level of real national income.\n",
    "\n",
    "1. Run the code cell below \"Calculating one quarter changes\", which calculates and then plots one year changes in consumption spending C and national income Y, both of them as fractions of the then-current level of national income Y. Examine the plot. Email me at delong@econ.berkeley.edu with the subject line \"Econ 101b S2018 PS8: Delta_C and Delta_Y DO Not Look Right\" if anything looks wrong to you.\n",
    "2. Run the code cell further below \"1 quarter changes regression: full sample\". Use the estimated b coefficient—the number immediately to the right of the \"Delta_Y\" under the heading \"coef\"—to calculate the estimate of the MPC $ c_y $ produced from examining one year changes. Assume once again that the tax rate t is 0.25. In the code cell \"Task G answers\", set the variable c_y_1_quarter equal to the estimate of the MPC.\n",
    "3. Duplicate the code cell \"Calculating one quarter changes\". Then paste it immediately below. Alter the first line of the pasted version to read \"# Calculating one quarter changes: earlier half of the sample\"\n",
    "4. Change the code in \"# Calculating one quarter changes: earlier half of the sample\" so that it runs the regression analysis for 1 year changes for only the first half of our 280 observation-long post-WWII data. In the code cell \"Task G answers\", set the variable c_y_1_quarter_early_sample equal to the estimate of the MPC for the first half of the sample.\n",
    "5. Duplicate the code cell \"Calculating one quarter changes\". Then paste it immediately below. Alter the first line of the pasted version to read \"# Calculating one quarter changes: later half of the sample\"\n",
    "6. Change the code in \"# Calculating one quarter changes: later half of the sample\" so that it runs the regression analysis for 1 year changes for only the seconc half of our 280 observation-long post-WWII data. In the code cell \"Task G answers\", set the variable c_y_1_quarter_late_sample equal to the estimate of the MPC for the second half of the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x114e5b198>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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qz/7Xh7W4ezin/gxTGUTU6ndXXFirh599c9pxu4DvtkY7LJ9RWNqB0kVgBlAyuKzrrXz7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\nPb+k2ARuJgwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114bd6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating one quarter changes\n",
    "\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "Delta_C1 = []\n",
    "Delta_Y1 = []\n",
    "\n",
    "for t in range(280):\n",
    "    Delta_C1 = Delta_C1 + [(gdp_cons_df.PCECC96[t+1]-\n",
    "        gdp_cons_df.PCECC96[t])/gdp_cons_df.GDPC1[t]]\n",
    "    Delta_Y1 = Delta_Y1 + [(gdp_cons_df.GDPC1[t+1]-\n",
    "        gdp_cons_df.GDPC1[t])/gdp_cons_df.GDPC1[t]]\n",
    "\n",
    "Delta_C1 = Delta_C1 + [0] + [0] + [0] + [0]\n",
    "Delta_Y1 = Delta_Y1 + [0] + [0] + [0] + [0]\n",
    "\n",
    "gdp_cons_df['Delta_C1'] = Delta_C1\n",
    "gdp_cons_df['Delta_Y1'] = Delta_Y1\n",
    "\n",
    "plt.plot(gdp_cons_df.Delta_Y1[:280], gdp_cons_df.Delta_C1[:280], \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Delta_C1   R-squared:                       0.596\n",
      "Model:                            OLS   Adj. R-squared:                  0.595\n",
      "Method:                 Least Squares   F-statistic:                     412.1\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):           6.89e-57\n",
      "Time:                        07:58:59   Log-Likelihood:                 1110.4\n",
      "No. Observations:                 280   AIC:                            -2219.\n",
      "Df Residuals:                     279   BIC:                            -2215.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Delta_Y1       0.4527      0.022     20.300      0.000       0.409       0.497\n",
      "==============================================================================\n",
      "Omnibus:                      140.997   Durbin-Watson:                   2.173\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1853.783\n",
      "Skew:                          -1.681   Prob(JB):                         0.00\n",
      "Kurtosis:                      15.149   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 1 quarter changes regression: full sample\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.Delta_C1[:280], gdp_cons_df.Delta_Y1[:280]).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Delta_C1   R-squared:                       0.541\n",
      "Model:                            OLS   Adj. R-squared:                  0.538\n",
      "Method:                 Least Squares   F-statistic:                     164.1\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):           2.67e-25\n",
      "Time:                        07:59:03   Log-Likelihood:                 528.18\n",
      "No. Observations:                 140   AIC:                            -1054.\n",
      "Df Residuals:                     139   BIC:                            -1051.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Delta_Y1       0.4113      0.032     12.811      0.000       0.348       0.475\n",
      "==============================================================================\n",
      "Omnibus:                       73.432   Durbin-Watson:                   2.290\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              532.818\n",
      "Skew:                          -1.672   Prob(JB):                    2.00e-116\n",
      "Kurtosis:                      11.953   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 1 quarter changes regression: earlier half of the sample\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.Delta_C1[:140], gdp_cons_df.Delta_Y1[:140]).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               Delta_C1   R-squared:                       0.732\n",
      "Model:                            OLS   Adj. R-squared:                  0.730\n",
      "Method:                 Least Squares   F-statistic:                     376.9\n",
      "Date:                Sat, 10 Mar 2018   Prob (F-statistic):           2.74e-41\n",
      "Time:                        07:59:09   Log-Likelihood:                 603.62\n",
      "No. Observations:                 139   AIC:                            -1205.\n",
      "Df Residuals:                     138   BIC:                            -1202.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Delta_Y1       0.5556      0.029     19.415      0.000       0.499       0.612\n",
      "==============================================================================\n",
      "Omnibus:                        4.476   Durbin-Watson:                   1.978\n",
      "Prob(Omnibus):                  0.107   Jarque-Bera (JB):                5.285\n",
      "Skew:                           0.157   Prob(JB):                       0.0712\n",
      "Kurtosis:                       3.902   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 1 quarter changes regression: later half of the sample\n",
    "\n",
    "results = sm.OLS(gdp_cons_df.Delta_C1[141:280], gdp_cons_df.Delta_Y1[141:280]).fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6036\n",
      "0.5484\n",
      "0.7408\n"
     ]
    }
   ],
   "source": [
    "# Task H answers\n",
    "\n",
    "t = 0.25\n",
    "c_y_1_quarter = 0.4527/(1-t)\n",
    "c_y_1_quarter_early_sample = 0.4113/(1-t)\n",
    "c_y_1_quarter_late_sample = 0.5556/(1-t)\n",
    "\n",
    "print(c_y_1_quarter)\n",
    "print(c_y_1_quarter_early_sample)\n",
    "print(c_y_1_quarter_late_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 3\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'failed': 0, 'locked': 0, 'passed': 3}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ok.grade('q05')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.save_checkpoint();"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "IPython.notebook.save_notebook();"
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving notebook... Saved 'PS8.ipynb'.\n",
      "Submit... 100% complete\n",
      "Backup... 100% complete\n",
      "Submission successful for user: jbdelong@berkeley.edu\n",
      "URL: https://okpy.org/cal/econ101b/sp18/PS8/submissions/76Bx48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.submit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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