Reinhart-Rogoff replication

rr.ipynb

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  "name": "reinhart-rogoff"
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Reinhart-Rogoff replication\n",
      "\n",
      "* Replication of Reinhart-Rogoff \"Growth in a Time of Debt.\"\n",
      "* Python port of R code by Thomas Herndon | Michael Ash | Robert Pollin\n",
      "* http://www.peri.umass.edu/236/hash/31e2ff374b6377b2ddec04deaa6388b1/publication/566/\n",
      "* Author: Vincent Arel-Bundock varel@umich.edu\n",
      "* Data: https://gist.github.com/vincentarelbundock/5409893/raw/a623f2f3bae027a0e51dd01ac5b70d44d909a7b9/RR-processed.csv"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "import patsy\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "RR = pd.read_csv('RR-processed.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Number of observations per country"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR.groupby('Country').size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "Country\n",
        "Australia      64\n",
        "Austria        62\n",
        "Belgium        63\n",
        "Canada         64\n",
        "Denmark        56\n",
        "Finland        64\n",
        "France         54\n",
        "Germany        59\n",
        "Greece         40\n",
        "Ireland        63\n",
        "Italy          59\n",
        "Japan          54\n",
        "Netherlands    53\n",
        "New Zealand    64\n",
        "Norway         64\n",
        "Portugal       58\n",
        "Spain          42\n",
        "Sweden         64\n",
        "UK             64\n",
        "US             64"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Bins"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins = [\"0-30%\",\"30-60%\",\"60-90%\",\"Above 90%\"]\n",
      "RR['dgcat'] = np.digitize(RR.debtgdp, [0,30,60,90,np.inf]) - 1\n",
      "RR.dgcat = [bins[x] for x in RR.dgcat]\n",
      "\n",
      "bins = [\"0-30%\",\"30-60%\",\"60-90%\",\"90-120%\",\"Above 120%\"]\n",
      "RR['dgcat2'] = np.digitize(RR.debtgdp, [0,30,60,90,120,np.inf]) - 1\n",
      "RR.dgcat2 = [bins[x] for x in RR.dgcat2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Regression analysis "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y,X = patsy.dmatrices('dRGDP ~ dgcat', data=RR[['dRGDP', 'dgcat']].dropna())\n",
      "print sm.OLS(y,X).fit().summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                  dRGDP   R-squared:                       0.045\n",
        "Model:                            OLS   Adj. R-squared:                  0.042\n",
        "Method:                 Least Squares   F-statistic:                     18.36\n",
        "Date:                Thu, 18 Apr 2013   Prob (F-statistic):           1.22e-11\n",
        "Time:                        08:05:54   Log-Likelihood:                -2927.9\n",
        "No. Observations:                1175   AIC:                             5864.\n",
        "Df Residuals:                    1171   BIC:                             5884.\n",
        "Df Model:                           3                                         \n",
        "======================================================================================\n",
        "                         coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "--------------------------------------------------------------------------------------\n",
        "Intercept              4.1735      0.142     29.413      0.000         3.895     4.452\n",
        "dgcat[T.30-60%]       -1.0814      0.199     -5.429      0.000        -1.472    -0.691\n",
        "dgcat[T.60-90%]       -0.9869      0.251     -3.931      0.000        -1.479    -0.494\n",
        "dgcat[T.Above 90%]    -2.0056      0.313     -6.403      0.000        -2.620    -1.391\n",
        "==============================================================================\n",
        "Omnibus:                      208.322   Durbin-Watson:                   1.385\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1757.480\n",
        "Skew:                           0.558   Prob(JB):                         0.00\n",
        "Kurtosis:                       8.887   Cond. No.                         4.57\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y2,X2 = patsy.dmatrices('dRGDP ~ dgcat2', data=RR[['dRGDP', 'dgcat2']].dropna())\n",
      "print sm.OLS(y2,X2).fit().summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                  dRGDP   R-squared:                       0.046\n",
        "Model:                            OLS   Adj. R-squared:                  0.043\n",
        "Method:                 Least Squares   F-statistic:                     14.24\n",
        "Date:                Thu, 18 Apr 2013   Prob (F-statistic):           2.36e-11\n",
        "Time:                        08:05:54   Log-Likelihood:                -2926.9\n",
        "No. Observations:                1175   AIC:                             5864.\n",
        "Df Residuals:                    1170   BIC:                             5889.\n",
        "Df Model:                           4                                         \n",
        "========================================================================================\n",
        "                           coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "----------------------------------------------------------------------------------------\n",
        "Intercept                4.1735      0.142     29.423      0.000         3.895     4.452\n",
        "dgcat2[T.30-60%]        -1.0814      0.199     -5.431      0.000        -1.472    -0.691\n",
        "dgcat2[T.60-90%]        -0.9869      0.251     -3.933      0.000        -1.479    -0.495\n",
        "dgcat2[T.90-120%]       -1.7676      0.359     -4.929      0.000        -2.471    -1.064\n",
        "dgcat2[T.Above 120%]    -2.6120      0.545     -4.796      0.000        -3.680    -1.543\n",
        "==============================================================================\n",
        "Omnibus:                      210.356   Durbin-Watson:                   1.388\n",
        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1756.317\n",
        "Skew:                           0.570   Prob(JB):                         0.00\n",
        "Kurtosis:                       8.880   Cond. No.                         7.10\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Table 3 Corrected"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Country-Year average by debtgdp (\"correct weights\")\n",
      "RR.dRGDP.groupby(RR.dgcat).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "dgcat\n",
        "0-30%        4.173523\n",
        "30-60%       3.092145\n",
        "60-90%       3.186575\n",
        "Above 90%    2.167972"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Averaged Country averages by debtgdp (\"equal weights\")\n",
      "RR.dRGDP.groupby([RR.Country, RR.dgcat]).mean().unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>dgcat</th>\n",
        "      <th>0-30%</th>\n",
        "      <th>30-60%</th>\n",
        "      <th>60-90%</th>\n",
        "      <th>Above 90%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Australia</th>\n",
        "      <td> 3.205885</td>\n",
        "      <td> 4.947205</td>\n",
        "      <td>  4.042175</td>\n",
        "      <td> 3.774250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Austria</th>\n",
        "      <td> 5.207527</td>\n",
        "      <td> 3.256526</td>\n",
        "      <td> -3.824000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Belgium</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 4.191655</td>\n",
        "      <td>  3.079868</td>\n",
        "      <td> 2.566828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Canada</th>\n",
        "      <td> 2.515704</td>\n",
        "      <td> 3.525446</td>\n",
        "      <td>  4.523574</td>\n",
        "      <td> 2.956640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Denmark</th>\n",
        "      <td> 3.518584</td>\n",
        "      <td> 1.700034</td>\n",
        "      <td>  2.391147</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Finland</th>\n",
        "      <td> 3.817029</td>\n",
        "      <td> 2.418535</td>\n",
        "      <td>  5.488887</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>France</th>\n",
        "      <td> 5.058476</td>\n",
        "      <td> 2.616159</td>\n",
        "      <td>  3.019631</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 3.873759</td>\n",
        "      <td> 0.875803</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Greece</th>\n",
        "      <td> 4.001282</td>\n",
        "      <td> 0.340200</td>\n",
        "      <td>  2.696000</td>\n",
        "      <td> 2.910632</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ireland</th>\n",
        "      <td> 4.209251</td>\n",
        "      <td> 4.452167</td>\n",
        "      <td>  3.950139</td>\n",
        "      <td> 2.428571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Italy</th>\n",
        "      <td> 5.352632</td>\n",
        "      <td> 2.054284</td>\n",
        "      <td>  1.771529</td>\n",
        "      <td> 1.028900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Japan</th>\n",
        "      <td> 7.331001</td>\n",
        "      <td> 3.957143</td>\n",
        "      <td>  1.008411</td>\n",
        "      <td> 0.687258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Netherlands</th>\n",
        "      <td> 4.082614</td>\n",
        "      <td> 2.620772</td>\n",
        "      <td>  1.070436</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Zealand</th>\n",
        "      <td> 2.465556</td>\n",
        "      <td> 2.889572</td>\n",
        "      <td>  3.883683</td>\n",
        "      <td> 2.574727</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Norway</th>\n",
        "      <td> 3.400122</td>\n",
        "      <td> 5.108289</td>\n",
        "      <td> 10.201270</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Portugal</th>\n",
        "      <td> 4.451419</td>\n",
        "      <td> 3.549482</td>\n",
        "      <td>  1.893899</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spain</th>\n",
        "      <td> 1.549332</td>\n",
        "      <td> 3.398669</td>\n",
        "      <td>  4.156250</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sweden</th>\n",
        "      <td> 3.567385</td>\n",
        "      <td> 2.932237</td>\n",
        "      <td>  2.665824</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UK</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 2.231213</td>\n",
        "      <td>  2.522133</td>\n",
        "      <td> 2.399096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>US</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 3.370208</td>\n",
        "      <td>  3.264068</td>\n",
        "      <td>-1.988893</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "dgcat           0-30%    30-60%     60-90%  Above 90%\n",
        "Country                                              \n",
        "Australia    3.205885  4.947205   4.042175   3.774250\n",
        "Austria      5.207527  3.256526  -3.824000        NaN\n",
        "Belgium           NaN  4.191655   3.079868   2.566828\n",
        "Canada       2.515704  3.525446   4.523574   2.956640\n",
        "Denmark      3.518584  1.700034   2.391147        NaN\n",
        "Finland      3.817029  2.418535   5.488887        NaN\n",
        "France       5.058476  2.616159   3.019631        NaN\n",
        "Germany      3.873759  0.875803        NaN        NaN\n",
        "Greece       4.001282  0.340200   2.696000   2.910632\n",
        "Ireland      4.209251  4.452167   3.950139   2.428571\n",
        "Italy        5.352632  2.054284   1.771529   1.028900\n",
        "Japan        7.331001  3.957143   1.008411   0.687258\n",
        "Netherlands  4.082614  2.620772   1.070436        NaN\n",
        "New Zealand  2.465556  2.889572   3.883683   2.574727\n",
        "Norway       3.400122  5.108289  10.201270        NaN\n",
        "Portugal     4.451419  3.549482   1.893899        NaN\n",
        "Spain        1.549332  3.398669   4.156250        NaN\n",
        "Sweden       3.567385  2.932237   2.665824        NaN\n",
        "UK                NaN  2.231213   2.522133   2.399096\n",
        "US                NaN  3.370208   3.264068  -1.988893"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Country-Year average by debtgdp (\"correct weights\") expanded categories\n",
      "RR.dRGDP.groupby(RR.dgcat2).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "dgcat2\n",
        "0-30%         4.173523\n",
        "30-60%        3.092145\n",
        "60-90%        3.186575\n",
        "90-120%       2.405934\n",
        "Above 120%    1.561553"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## Averaged Country averages by debtgdp (\"equal weights\")\n",
      "RR.dRGDP.groupby([RR.Country, RR.dgcat2]).mean().unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>dgcat2</th>\n",
        "      <th>0-30%</th>\n",
        "      <th>30-60%</th>\n",
        "      <th>60-90%</th>\n",
        "      <th>90-120%</th>\n",
        "      <th>Above 120%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Australia</th>\n",
        "      <td> 3.205885</td>\n",
        "      <td> 4.947205</td>\n",
        "      <td>  4.042175</td>\n",
        "      <td> 6.920201</td>\n",
        "      <td>  2.987763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Austria</th>\n",
        "      <td> 5.207527</td>\n",
        "      <td> 3.256526</td>\n",
        "      <td> -3.824000</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Belgium</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 4.191655</td>\n",
        "      <td>  3.079868</td>\n",
        "      <td> 2.702629</td>\n",
        "      <td> -0.692378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Canada</th>\n",
        "      <td> 2.515704</td>\n",
        "      <td> 3.525446</td>\n",
        "      <td>  4.523574</td>\n",
        "      <td> 4.544839</td>\n",
        "      <td>  0.574341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Denmark</th>\n",
        "      <td> 3.518584</td>\n",
        "      <td> 1.700034</td>\n",
        "      <td>  2.391147</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Finland</th>\n",
        "      <td> 3.817029</td>\n",
        "      <td> 2.418535</td>\n",
        "      <td>  5.488887</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>France</th>\n",
        "      <td> 5.058476</td>\n",
        "      <td> 2.616159</td>\n",
        "      <td>  3.019631</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 3.873759</td>\n",
        "      <td> 0.875803</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Greece</th>\n",
        "      <td> 4.001282</td>\n",
        "      <td> 0.340200</td>\n",
        "      <td>  2.696000</td>\n",
        "      <td> 2.910632</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ireland</th>\n",
        "      <td> 4.209251</td>\n",
        "      <td> 4.452167</td>\n",
        "      <td>  3.950139</td>\n",
        "      <td> 2.428571</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Italy</th>\n",
        "      <td> 5.352632</td>\n",
        "      <td> 2.054284</td>\n",
        "      <td>  1.771529</td>\n",
        "      <td> 1.028900</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Japan</th>\n",
        "      <td> 7.331001</td>\n",
        "      <td> 3.957143</td>\n",
        "      <td>  1.008411</td>\n",
        "      <td> 1.359564</td>\n",
        "      <td>  0.537857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Netherlands</th>\n",
        "      <td> 4.082614</td>\n",
        "      <td> 2.620772</td>\n",
        "      <td>  1.070436</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Zealand</th>\n",
        "      <td> 2.465556</td>\n",
        "      <td> 2.889572</td>\n",
        "      <td>  3.883683</td>\n",
        "      <td>-2.256588</td>\n",
        "      <td>  9.821699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Norway</th>\n",
        "      <td> 3.400122</td>\n",
        "      <td> 5.108289</td>\n",
        "      <td> 10.201270</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Portugal</th>\n",
        "      <td> 4.451419</td>\n",
        "      <td> 3.549482</td>\n",
        "      <td>  1.893899</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spain</th>\n",
        "      <td> 1.549332</td>\n",
        "      <td> 3.398669</td>\n",
        "      <td>  4.156250</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sweden</th>\n",
        "      <td> 3.567385</td>\n",
        "      <td> 2.932237</td>\n",
        "      <td>  2.665824</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>       NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UK</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 2.231213</td>\n",
        "      <td>  2.522133</td>\n",
        "      <td> 3.303428</td>\n",
        "      <td>  1.871568</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>US</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 3.370208</td>\n",
        "      <td>  3.264068</td>\n",
        "      <td> 0.995529</td>\n",
        "      <td>-10.942159</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "dgcat2          0-30%    30-60%     60-90%   90-120%  Above 120%\n",
        "Country                                                         \n",
        "Australia    3.205885  4.947205   4.042175  6.920201    2.987763\n",
        "Austria      5.207527  3.256526  -3.824000       NaN         NaN\n",
        "Belgium           NaN  4.191655   3.079868  2.702629   -0.692378\n",
        "Canada       2.515704  3.525446   4.523574  4.544839    0.574341\n",
        "Denmark      3.518584  1.700034   2.391147       NaN         NaN\n",
        "Finland      3.817029  2.418535   5.488887       NaN         NaN\n",
        "France       5.058476  2.616159   3.019631       NaN         NaN\n",
        "Germany      3.873759  0.875803        NaN       NaN         NaN\n",
        "Greece       4.001282  0.340200   2.696000  2.910632         NaN\n",
        "Ireland      4.209251  4.452167   3.950139  2.428571         NaN\n",
        "Italy        5.352632  2.054284   1.771529  1.028900         NaN\n",
        "Japan        7.331001  3.957143   1.008411  1.359564    0.537857\n",
        "Netherlands  4.082614  2.620772   1.070436       NaN         NaN\n",
        "New Zealand  2.465556  2.889572   3.883683 -2.256588    9.821699\n",
        "Norway       3.400122  5.108289  10.201270       NaN         NaN\n",
        "Portugal     4.451419  3.549482   1.893899       NaN         NaN\n",
        "Spain        1.549332  3.398669   4.156250       NaN         NaN\n",
        "Sweden       3.567385  2.932237   2.665824       NaN         NaN\n",
        "UK                NaN  2.231213   2.522133  3.303428    1.871568\n",
        "US                NaN  3.370208   3.264068  0.995529  -10.942159"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Selective treatment of early years"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "idx = (RR.Country == 'New Zealand') & (RR.Year < 1950) | (RR.Country == 'Australia') & (RR.Year < 1951) | (RR.Country == 'Canada') & (RR.Year < 1951) \n",
      "RR_selective = RR[idx == False]\n",
      "RR_selective.dRGDP.groupby(RR_selective.dgcat).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "dgcat\n",
        "0-30%        4.173523\n",
        "30-60%       3.092145\n",
        "60-90%       3.186575\n",
        "Above 90%    1.919934"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Equal weights\n",
      "## Table 3 Weights,Exclusion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR_selective.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "Unnamed: 0    5.916408e+02\n",
        "Year          1.979633e+03\n",
        "Debt          1.621458e+07\n",
        "RGDP          2.369173e+05\n",
        "GDP           1.957611e+05\n",
        "dRGDP         3.408270e+00\n",
        "GDPI          5.034180e+01\n",
        "GDP1          1.470725e+07\n",
        "GDP2          1.824865e+07\n",
        "RGDP1         1.425590e+07\n",
        "RGDP2         3.072470e+07\n",
        "GDPI1         5.589542e+02\n",
        "GDPI2         8.690705e+01\n",
        "Infl          5.632643e+00\n",
        "Debt1         5.625405e+05\n",
        "Debt2         1.050730e+05\n",
        "Debtalt       1.006665e+07\n",
        "GDP2alt       4.450790e+05\n",
        "GDPalt        2.079659e+06\n",
        "RGDP2alt      1.033331e+05\n",
        "debtgdp       4.530375e+01\n",
        "GDP3          7.689113e+04\n",
        "GNI           5.156239e+08\n",
        "lRGDP         2.351587e+05\n",
        "lRGDP1        1.405853e+07\n",
        "lRGDP2        3.047113e+07"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Correct weights\n",
      "## Table 3 Selective years exclusion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR_selective.dRGDP.groupby([RR_selective.Country, RR_selective.dgcat]).mean().unstack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>dgcat</th>\n",
        "      <th>0-30%</th>\n",
        "      <th>30-60%</th>\n",
        "      <th>60-90%</th>\n",
        "      <th>Above 90%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Country</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Australia</th>\n",
        "      <td> 3.205885</td>\n",
        "      <td> 4.947205</td>\n",
        "      <td>  4.042175</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Austria</th>\n",
        "      <td> 5.207527</td>\n",
        "      <td> 3.256526</td>\n",
        "      <td> -3.824000</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Belgium</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 4.191655</td>\n",
        "      <td>  3.079868</td>\n",
        "      <td> 2.566828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Canada</th>\n",
        "      <td> 2.515704</td>\n",
        "      <td> 3.525446</td>\n",
        "      <td>  4.523574</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Denmark</th>\n",
        "      <td> 3.518584</td>\n",
        "      <td> 1.700034</td>\n",
        "      <td>  2.391147</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Finland</th>\n",
        "      <td> 3.817029</td>\n",
        "      <td> 2.418535</td>\n",
        "      <td>  5.488887</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>France</th>\n",
        "      <td> 5.058476</td>\n",
        "      <td> 2.616159</td>\n",
        "      <td>  3.019631</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td> 3.873759</td>\n",
        "      <td> 0.875803</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Greece</th>\n",
        "      <td> 4.001282</td>\n",
        "      <td> 0.340200</td>\n",
        "      <td>  2.696000</td>\n",
        "      <td> 2.910632</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ireland</th>\n",
        "      <td> 4.209251</td>\n",
        "      <td> 4.452167</td>\n",
        "      <td>  3.950139</td>\n",
        "      <td> 2.428571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Italy</th>\n",
        "      <td> 5.352632</td>\n",
        "      <td> 2.054284</td>\n",
        "      <td>  1.771529</td>\n",
        "      <td> 1.028900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Japan</th>\n",
        "      <td> 7.331001</td>\n",
        "      <td> 3.957143</td>\n",
        "      <td>  1.008411</td>\n",
        "      <td> 0.687258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Netherlands</th>\n",
        "      <td> 4.082614</td>\n",
        "      <td> 2.620772</td>\n",
        "      <td>  1.070436</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>New Zealand</th>\n",
        "      <td> 2.465556</td>\n",
        "      <td> 2.889572</td>\n",
        "      <td>  3.883683</td>\n",
        "      <td>-7.635102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Norway</th>\n",
        "      <td> 3.400122</td>\n",
        "      <td> 5.108289</td>\n",
        "      <td> 10.201270</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Portugal</th>\n",
        "      <td> 4.451419</td>\n",
        "      <td> 3.549482</td>\n",
        "      <td>  1.893899</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spain</th>\n",
        "      <td> 1.549332</td>\n",
        "      <td> 3.398669</td>\n",
        "      <td>  4.156250</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sweden</th>\n",
        "      <td> 3.567385</td>\n",
        "      <td> 2.932237</td>\n",
        "      <td>  2.665824</td>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UK</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 2.231213</td>\n",
        "      <td>  2.522133</td>\n",
        "      <td> 2.399096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>US</th>\n",
        "      <td>      NaN</td>\n",
        "      <td> 3.370208</td>\n",
        "      <td>  3.264068</td>\n",
        "      <td>-1.988893</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "dgcat           0-30%    30-60%     60-90%  Above 90%\n",
        "Country                                              \n",
        "Australia    3.205885  4.947205   4.042175        NaN\n",
        "Austria      5.207527  3.256526  -3.824000        NaN\n",
        "Belgium           NaN  4.191655   3.079868   2.566828\n",
        "Canada       2.515704  3.525446   4.523574        NaN\n",
        "Denmark      3.518584  1.700034   2.391147        NaN\n",
        "Finland      3.817029  2.418535   5.488887        NaN\n",
        "France       5.058476  2.616159   3.019631        NaN\n",
        "Germany      3.873759  0.875803        NaN        NaN\n",
        "Greece       4.001282  0.340200   2.696000   2.910632\n",
        "Ireland      4.209251  4.452167   3.950139   2.428571\n",
        "Italy        5.352632  2.054284   1.771529   1.028900\n",
        "Japan        7.331001  3.957143   1.008411   0.687258\n",
        "Netherlands  4.082614  2.620772   1.070436        NaN\n",
        "New Zealand  2.465556  2.889572   3.883683  -7.635102\n",
        "Norway       3.400122  5.108289  10.201270        NaN\n",
        "Portugal     4.451419  3.549482   1.893899        NaN\n",
        "Spain        1.549332  3.398669   4.156250        NaN\n",
        "Sweden       3.567385  2.932237   2.665824        NaN\n",
        "UK                NaN  2.231213   2.522133   2.399096\n",
        "US                NaN  3.370208   3.264068  -1.988893"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## And dropping because of spreadsheet error"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drop = [\"Australia\",\"Austria\",\"Belgium\",\"Canada\",\"Denmark\"]\n",
      "idx = [False if x in drop else True for x in RR_selective.Country]\n",
      "RR_selective_spreadsheet = RR_selective[idx]\n",
      "RR_selective_spreadsheet.dRGDP.groupby(RR.dgcat).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "dgcat\n",
        "0-30%        4.236391\n",
        "30-60%       2.958902\n",
        "60-90%       3.160164\n",
        "Above 90%    1.692155"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## New Zealand transcription error"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR_selective_spreadsheet_transcription = RR_selective_spreadsheet.copy()\n",
      "RR_selective_spreadsheet_transcription.RGDP[RR_selective_spreadsheet_transcription.Country=='New Zealand'] = -7.9\n",
      "RR_selective_spreadsheet_transcription.dRGDP.groupby(RR.dgcat).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "dgcat\n",
        "0-30%        4.236391\n",
        "30-60%       2.958902\n",
        "60-90%       3.160164\n",
        "Above 90%    1.692155"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = RR_selective_spreadsheet_transcription.Country\n",
      "b = RR_selective_spreadsheet_transcription.dgcat\n",
      "RR_selective_spreadsheet_transcription.dRGDP.groupby(b).mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "dgcat\n",
        "0-30%        4.236391\n",
        "30-60%       2.958902\n",
        "60-90%       3.160164\n",
        "Above 90%    1.692155"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "published_means = RR_selective_spreadsheet_transcription.dRGDP.groupby([a,b]).mean().unstack()\n",
      "published_means.ix['New Zealand', 'Above 90%'] = -7.9\n",
      "published_means.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "dgcat\n",
        "0-30%        4.089220\n",
        "30-60%       2.854316\n",
        "60-90%       3.399440\n",
        "Above 90%   -0.062062"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Medians"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR.dRGDP.groupby(RR.dgcat).median() # Correct, equal weight"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "dgcat\n",
        "0-30%        4.145376\n",
        "30-60%       3.104629\n",
        "60-90%       2.897829\n",
        "Above 90%    2.335324"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR.dRGDP.groupby(RR.dgcat2).median() # Correct, expanded categories, equal weight"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "dgcat2\n",
        "0-30%         4.145376\n",
        "30-60%        3.104629\n",
        "60-90%        2.897829\n",
        "90-120%       2.373340\n",
        "Above 120%    2.039469"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Counts of years"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR.Country.groupby([RR.Country, RR.dgcat]).size().unstack().sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "dgcat\n",
        "0-30%        426\n",
        "30-60%       439\n",
        "60-90%       200\n",
        "Above 90%    110"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR_selective.Country.groupby([RR.Country, RR.dgcat]).size().unstack().sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "dgcat\n",
        "0-30%        426\n",
        "30-60%       439\n",
        "60-90%       200\n",
        "Above 90%     96"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "RR_selective_spreadsheet.Country.groupby([RR.Country, RR.dgcat]).size().unstack().sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "dgcat\n",
        "0-30%        329\n",
        "30-60%       324\n",
        "60-90%       138\n",
        "Above 90%     71"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Categorical scatterplot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = [\"0-30%\",\"30-60%\",\"60-90%\",\"Above 90%\"]\n",
      "dat = [np.array(RR.dRGDP[RR.dgcat==x]) for x in labels]\n",
      "print sm.graphics.violinplot(dat, labels=labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Figure(480x320)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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a75wnE8nNtW86Xk2NxS0/KOW4u5cxdmxqq/ebmpqPp2cQ9vatHy3YnKKiIo4f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\nwiakoAshhE1IQRdCCJv4BvFEpik+2WxEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110662350>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "labels = [\"0-30%\",\"30-60%\",\"60-90%\",\"90-120%\",\"Above 120%\"]\n",
      "dat = [np.array(RR.dRGDP[RR.dgcat2==x]) for x in labels]\n",
      "print sm.graphics.violinplot(dat, labels=labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Figure(480x320)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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l/Zrde/duIT//AiEhDkRF+bXbyc3W1ppRo3oRFFRNQsJ2PvhgJ6GhU5kwIYqu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\n4hJsYoLkBRmBHVK5CAKyl/fvD4bzAwlc/KaFs68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       "text": [
        "<matplotlib.figure.Figure at 0x110bfc450>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Country-Year average by debtgdp for more recent samples\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = range(1950, 2001, 10)\n",
      "f = lambda x: (x, RR[RR.Year >= x].dRGDP.groupby(RR[RR.Year >= x].dgcat).mean())\n",
      "[f(x) for x in years]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "[(1950,\n",
        "  dgcat\n",
        "0-30%        4.135295\n",
        "30-60%       2.980839\n",
        "60-90%       3.100982\n",
        "Above 90%    2.121852),\n",
        " (1960,\n",
        "  dgcat\n",
        "0-30%        3.895619\n",
        "30-60%       2.909601\n",
        "60-90%       2.779663\n",
        "Above 90%    2.074064),\n",
        " (1970,\n",
        "  dgcat\n",
        "0-30%        3.145224\n",
        "30-60%       2.644951\n",
        "60-90%       2.559289\n",
        "Above 90%    1.959229),\n",
        " (1980,\n",
        "  dgcat\n",
        "0-30%        2.541408\n",
        "30-60%       2.451346\n",
        "60-90%       2.435681\n",
        "Above 90%    1.959229),\n",
        " (1990,\n",
        "  dgcat\n",
        "0-30%        2.669334\n",
        "30-60%       2.403622\n",
        "60-90%       2.457587\n",
        "Above 90%    1.823201),\n",
        " (2000,\n",
        "  dgcat\n",
        "0-30%        2.747593\n",
        "30-60%       1.881735\n",
        "60-90%       1.290506\n",
        "Above 90%    1.745087)]"
       ]
      }
     ],
     "prompt_number": 24
    }
   ],
   "metadata": {}
  }
 ]
}