production_function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Consolidated number of employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>210709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>215648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>246702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>264096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>264410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Consolidated number of employees\n",
       "Year                                  \n",
       "2000                            210709\n",
       "2001                            215648\n",
       "2002                            246702\n",
       "2003                            264096\n",
       "2004                            264410"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the consolidated number of employees\n",
    "empl = pd.read_csv('data/toyota_number_of_employees.csv', usecols = [0, 2], index_col = 0)\n",
    "\n",
    "empl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Consolidated number of employees</th>\n",
       "      <th>worldwide_production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>210709</td>\n",
       "      <td>5180651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>215648</td>\n",
       "      <td>5134629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>246702</td>\n",
       "      <td>5640389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>264096</td>\n",
       "      <td>6078282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>264410</td>\n",
       "      <td>6723674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Consolidated number of employees  worldwide_production\n",
       "Year                                                        \n",
       "2000                            210709               5180651\n",
       "2001                            215648               5134629\n",
       "2002                            246702               5640389\n",
       "2003                            264096               6078282\n",
       "2004                            264410               6723674"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From the production data we only need the year and the worldwide production numbers\n",
    "prod = pd.read_csv('data/toyota_production_by_country.csv', usecols = [0, 32], index_col = 0)\n",
    "empl_prod = empl.assign(worldwide_production = prod)\n",
    "\n",
    "empl_prod.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Consolidated number of employees</th>\n",
       "      <th>worldwide_production</th>\n",
       "      <th>total_assets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>210709</td>\n",
       "      <td>5180651</td>\n",
       "      <td>16469.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>215648</td>\n",
       "      <td>5134629</td>\n",
       "      <td>17519.427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>246702</td>\n",
       "      <td>5640389</td>\n",
       "      <td>19305.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>264096</td>\n",
       "      <td>6078282</td>\n",
       "      <td>20152.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>264410</td>\n",
       "      <td>6723674</td>\n",
       "      <td>22040.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>265753</td>\n",
       "      <td>7360885</td>\n",
       "      <td>24335.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2006</td>\n",
       "      <td>285977</td>\n",
       "      <td>8093163</td>\n",
       "      <td>28731.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007</td>\n",
       "      <td>299394</td>\n",
       "      <td>8534690</td>\n",
       "      <td>32574.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008</td>\n",
       "      <td>316121</td>\n",
       "      <td>8210818</td>\n",
       "      <td>32458.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009</td>\n",
       "      <td>320808</td>\n",
       "      <td>6371291</td>\n",
       "      <td>29062.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>320590</td>\n",
       "      <td>7623349</td>\n",
       "      <td>30349.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011</td>\n",
       "      <td>317716</td>\n",
       "      <td>6928813</td>\n",
       "      <td>29818.166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012</td>\n",
       "      <td>325905</td>\n",
       "      <td>8736529</td>\n",
       "      <td>30650.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013</td>\n",
       "      <td>333498</td>\n",
       "      <td>8892095</td>\n",
       "      <td>35483.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014</td>\n",
       "      <td>338875</td>\n",
       "      <td>9004825</td>\n",
       "      <td>41437.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>344109</td>\n",
       "      <td>8929075</td>\n",
       "      <td>47729.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016</td>\n",
       "      <td>348877</td>\n",
       "      <td>8973988</td>\n",
       "      <td>47427.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017</td>\n",
       "      <td>364445</td>\n",
       "      <td>9007511</td>\n",
       "      <td>48750.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018</td>\n",
       "      <td>369124</td>\n",
       "      <td>8885573</td>\n",
       "      <td>50308.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019</td>\n",
       "      <td>370870</td>\n",
       "      <td>9053517</td>\n",
       "      <td>51936.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Consolidated number of employees  worldwide_production  total_assets\n",
       "Year                                                                      \n",
       "2000                            210709               5180651     16469.054\n",
       "2001                            215648               5134629     17519.427\n",
       "2002                            246702               5640389     19305.730\n",
       "2003                            264096               6078282     20152.974\n",
       "2004                            264410               6723674     22040.228\n",
       "2005                            265753               7360885     24335.011\n",
       "2006                            285977               8093163     28731.595\n",
       "2007                            299394               8534690     32574.779\n",
       "2008                            316121               8210818     32458.320\n",
       "2009                            320808               6371291     29062.037\n",
       "2010                            320590               7623349     30349.287\n",
       "2011                            317716               6928813     29818.166\n",
       "2012                            325905               8736529     30650.965\n",
       "2013                            333498               8892095     35483.317\n",
       "2014                            338875               9004825     41437.473\n",
       "2015                            344109               8929075     47729.830\n",
       "2016                            348877               8973988     47427.597\n",
       "2017                            364445               9007511     48750.186\n",
       "2018                            369124               8885573     50308.249\n",
       "2019                            370870               9053517     51936.900"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read total assets\n",
    "assets = pd.read_csv('data/toyota_total_assets.csv', index_col = 0)\n",
    "empl_prod_assets = empl_prod.assign(total_assets = assets)\n",
    "\n",
    "empl_prod_assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Consolidated number of employees</th>\n",
       "      <th>worldwide_production</th>\n",
       "      <th>total_assets</th>\n",
       "      <th>logL</th>\n",
       "      <th>logK</th>\n",
       "      <th>logQ</th>\n",
       "      <th>logQ_K</th>\n",
       "      <th>logL_K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>2000</td>\n",
       "      <td>210709</td>\n",
       "      <td>5180651</td>\n",
       "      <td>16469.054</td>\n",
       "      <td>12.258233</td>\n",
       "      <td>9.709238</td>\n",
       "      <td>15.460441</td>\n",
       "      <td>5.751203</td>\n",
       "      <td>2.548995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>215648</td>\n",
       "      <td>5134629</td>\n",
       "      <td>17519.427</td>\n",
       "      <td>12.281403</td>\n",
       "      <td>9.771066</td>\n",
       "      <td>15.451518</td>\n",
       "      <td>5.680452</td>\n",
       "      <td>2.510337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>246702</td>\n",
       "      <td>5640389</td>\n",
       "      <td>19305.730</td>\n",
       "      <td>12.415936</td>\n",
       "      <td>9.868157</td>\n",
       "      <td>15.545464</td>\n",
       "      <td>5.677306</td>\n",
       "      <td>2.547779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>264096</td>\n",
       "      <td>6078282</td>\n",
       "      <td>20152.974</td>\n",
       "      <td>12.484068</td>\n",
       "      <td>9.911107</td>\n",
       "      <td>15.620233</td>\n",
       "      <td>5.709125</td>\n",
       "      <td>2.572961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>264410</td>\n",
       "      <td>6723674</td>\n",
       "      <td>22040.228</td>\n",
       "      <td>12.485256</td>\n",
       "      <td>10.000625</td>\n",
       "      <td>15.721145</td>\n",
       "      <td>5.720521</td>\n",
       "      <td>2.484632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Consolidated number of employees  worldwide_production  total_assets  \\\n",
       "Year                                                                         \n",
       "2000                            210709               5180651     16469.054   \n",
       "2001                            215648               5134629     17519.427   \n",
       "2002                            246702               5640389     19305.730   \n",
       "2003                            264096               6078282     20152.974   \n",
       "2004                            264410               6723674     22040.228   \n",
       "\n",
       "           logL       logK       logQ    logQ_K    logL_K  \n",
       "Year                                                       \n",
       "2000  12.258233   9.709238  15.460441  5.751203  2.548995  \n",
       "2001  12.281403   9.771066  15.451518  5.680452  2.510337  \n",
       "2002  12.415936   9.868157  15.545464  5.677306  2.547779  \n",
       "2003  12.484068   9.911107  15.620233  5.709125  2.572961  \n",
       "2004  12.485256  10.000625  15.721145  5.720521  2.484632  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The production function will be estimated using the Cobb-Douglas production function\n",
    "# The Cobb-Douglas production function is not linear, so it needs to be converted to linear form before fitting\n",
    "# We convert it to linear form by taking the natural logarithm of it\n",
    "\n",
    "empl_prod_assets['logL'] = np.log(empl_prod_assets['Consolidated number of employees'])\n",
    "empl_prod_assets['logK'] = np.log(empl_prod_assets['total_assets'])\n",
    "empl_prod_assets['logQ'] = np.log(empl_prod_assets['worldwide_production'])\n",
    "empl_prod_assets['logQ_K'] = np.log(empl_prod_assets['worldwide_production']/empl_prod_assets['total_assets'])\n",
    "empl_prod_assets['logL_K'] = np.log(empl_prod_assets['Consolidated number of employees']/empl_prod_assets['total_assets'])\n",
    "\n",
    "empl_prod_assets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>logQ</td>       <th>  R-squared:         </th> <td>   0.830</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   41.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 24 Feb 2020</td> <th>  Prob (F-statistic):</th> <td>2.84e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:15:15</td>     <th>  Log-Likelihood:    </th> <td>  22.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>  -38.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    17</td>      <th>  BIC:               </th> <td>  -36.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    8.1955</td> <td>    3.084</td> <td>    2.657</td> <td> 0.017</td> <td>    1.688</td> <td>   14.703</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>logL</th>      <td>    0.3199</td> <td>    0.376</td> <td>    0.851</td> <td> 0.406</td> <td>   -0.473</td> <td>    1.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>logK</th>      <td>    0.3485</td> <td>    0.174</td> <td>    2.005</td> <td> 0.061</td> <td>   -0.018</td> <td>    0.715</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.548</td> <th>  Durbin-Watson:     </th> <td>   1.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.760</td> <th>  Jarque-Bera (JB):  </th> <td>   0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.007</td> <th>  Prob(JB):          </th> <td>   0.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.161</td> <th>  Cond. No.          </th> <td>2.67e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.67e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   logQ   R-squared:                       0.830\n",
       "Model:                            OLS   Adj. R-squared:                  0.810\n",
       "Method:                 Least Squares   F-statistic:                     41.57\n",
       "Date:                Mon, 24 Feb 2020   Prob (F-statistic):           2.84e-07\n",
       "Time:                        00:15:15   Log-Likelihood:                 22.494\n",
       "No. Observations:                  20   AIC:                            -38.99\n",
       "Df Residuals:                      17   BIC:                            -36.00\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      8.1955      3.084      2.657      0.017       1.688      14.703\n",
       "logL           0.3199      0.376      0.851      0.406      -0.473       1.113\n",
       "logK           0.3485      0.174      2.005      0.061      -0.018       0.715\n",
       "==============================================================================\n",
       "Omnibus:                        0.548   Durbin-Watson:                   1.230\n",
       "Prob(Omnibus):                  0.760   Jarque-Bera (JB):                0.587\n",
       "Skew:                           0.007   Prob(JB):                        0.746\n",
       "Kurtosis:                       2.161   Cond. No.                     2.67e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.67e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Ordinary least squares for simple linear regression - allowing variable returns\n",
    "model = smf.ols(formula = 'logQ ~ logL + logK', data = empl_prod_assets).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>logQ_K</td>      <th>  R-squared:         </th> <td>   0.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   82.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 24 Feb 2020</td> <th>  Prob (F-statistic):</th> <td>3.95e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:15:15</td>     <th>  Log-Likelihood:    </th> <td>  21.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>  -38.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>  -36.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    3.5003</td> <td>    0.221</td> <td>   15.828</td> <td> 0.000</td> <td>    3.036</td> <td>    3.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>logL_K</th>    <td>    0.8754</td> <td>    0.097</td> <td>    9.066</td> <td> 0.000</td> <td>    0.673</td> <td>    1.078</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.723</td> <th>  Durbin-Watson:     </th> <td>   1.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.423</td> <th>  Jarque-Bera (JB):  </th> <td>   0.768</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.473</td> <th>  Prob(JB):          </th> <td>   0.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.170</td> <th>  Cond. No.          </th> <td>    30.5</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 logQ_K   R-squared:                       0.820\n",
       "Model:                            OLS   Adj. R-squared:                  0.810\n",
       "Method:                 Least Squares   F-statistic:                     82.19\n",
       "Date:                Mon, 24 Feb 2020   Prob (F-statistic):           3.95e-08\n",
       "Time:                        00:15:15   Log-Likelihood:                 21.210\n",
       "No. Observations:                  20   AIC:                            -38.42\n",
       "Df Residuals:                      18   BIC:                            -36.43\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      3.5003      0.221     15.828      0.000       3.036       3.965\n",
       "logL_K         0.8754      0.097      9.066      0.000       0.673       1.078\n",
       "==============================================================================\n",
       "Omnibus:                        1.723   Durbin-Watson:                   1.229\n",
       "Prob(Omnibus):                  0.423   Jarque-Bera (JB):                0.768\n",
       "Skew:                          -0.473   Prob(JB):                        0.681\n",
       "Kurtosis:                       3.170   Cond. No.                         30.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ordinary least squares for simple linear regression - assuming constant returns\n",
    "model_constant_growth = smf.ols(formula = 'logQ_K ~ logL_K', data = empl_prod_assets).fit()\n",
    "\n",
    "model_constant_growth.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Consolidated number of employees</th>\n",
       "      <th>worldwide_production</th>\n",
       "      <th>total_assets</th>\n",
       "      <th>logL</th>\n",
       "      <th>logK</th>\n",
       "      <th>logQ</th>\n",
       "      <th>logQ_K</th>\n",
       "      <th>logL_K</th>\n",
       "      <th>pred_var_ret</th>\n",
       "      <th>pred_con_ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>2000</td>\n",
       "      <td>210709</td>\n",
       "      <td>5180651</td>\n",
       "      <td>16469.054</td>\n",
       "      <td>12.258233</td>\n",
       "      <td>9.709238</td>\n",
       "      <td>15.460441</td>\n",
       "      <td>5.751203</td>\n",
       "      <td>2.548995</td>\n",
       "      <td>15.500708</td>\n",
       "      <td>5.731812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2001</td>\n",
       "      <td>215648</td>\n",
       "      <td>5134629</td>\n",
       "      <td>17519.427</td>\n",
       "      <td>12.281403</td>\n",
       "      <td>9.771066</td>\n",
       "      <td>15.451518</td>\n",
       "      <td>5.680452</td>\n",
       "      <td>2.510337</td>\n",
       "      <td>15.529667</td>\n",
       "      <td>5.697969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2002</td>\n",
       "      <td>246702</td>\n",
       "      <td>5640389</td>\n",
       "      <td>19305.730</td>\n",
       "      <td>12.415936</td>\n",
       "      <td>9.868157</td>\n",
       "      <td>15.545464</td>\n",
       "      <td>5.677306</td>\n",
       "      <td>2.547779</td>\n",
       "      <td>15.606542</td>\n",
       "      <td>5.730748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2003</td>\n",
       "      <td>264096</td>\n",
       "      <td>6078282</td>\n",
       "      <td>20152.974</td>\n",
       "      <td>12.484068</td>\n",
       "      <td>9.911107</td>\n",
       "      <td>15.620233</td>\n",
       "      <td>5.709125</td>\n",
       "      <td>2.572961</td>\n",
       "      <td>15.643306</td>\n",
       "      <td>5.752793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2004</td>\n",
       "      <td>264410</td>\n",
       "      <td>6723674</td>\n",
       "      <td>22040.228</td>\n",
       "      <td>12.485256</td>\n",
       "      <td>10.000625</td>\n",
       "      <td>15.721145</td>\n",
       "      <td>5.720521</td>\n",
       "      <td>2.484632</td>\n",
       "      <td>15.674882</td>\n",
       "      <td>5.675466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Consolidated number of employees  worldwide_production  total_assets  \\\n",
       "Year                                                                         \n",
       "2000                            210709               5180651     16469.054   \n",
       "2001                            215648               5134629     17519.427   \n",
       "2002                            246702               5640389     19305.730   \n",
       "2003                            264096               6078282     20152.974   \n",
       "2004                            264410               6723674     22040.228   \n",
       "\n",
       "           logL       logK       logQ    logQ_K    logL_K  pred_var_ret  \\\n",
       "Year                                                                      \n",
       "2000  12.258233   9.709238  15.460441  5.751203  2.548995     15.500708   \n",
       "2001  12.281403   9.771066  15.451518  5.680452  2.510337     15.529667   \n",
       "2002  12.415936   9.868157  15.545464  5.677306  2.547779     15.606542   \n",
       "2003  12.484068   9.911107  15.620233  5.709125  2.572961     15.643306   \n",
       "2004  12.485256  10.000625  15.721145  5.720521  2.484632     15.674882   \n",
       "\n",
       "      pred_con_ret  \n",
       "Year                \n",
       "2000      5.731812  \n",
       "2001      5.697969  \n",
       "2002      5.730748  \n",
       "2003      5.752793  \n",
       "2004      5.675466  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the predictions and record these predictions to the data frame\n",
    "empl_prod_assets['pred_var_ret'] = model.predict()\n",
    "empl_prod_assets['pred_con_ret'] = model_constant_growth.predict()\n",
    "\n",
    "empl_prod_assets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data and model\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_data_model(pos, size, elev, azim):\n",
    "    x_surf = np.linspace(empl_prod_assets.logL.min(), empl_prod_assets.logL.max(), 8)\n",
    "    y_surf = np.linspace(empl_prod_assets.logK.min(), empl_prod_assets.logK.max(), 8)\n",
    "    x_surf, y_surf = np.meshgrid(x_surf, y_surf)\n",
    "\n",
    "    onlyX = pd.DataFrame({'logL': x_surf.ravel(), 'logK': y_surf.ravel()})\n",
    "    fittedY = model.predict(exog = onlyX)\n",
    "\n",
    "    fig = plt.figure(figsize=size)\n",
    "    ax = fig.add_subplot(pos, projection = '3d')\n",
    "    ax.scatter(empl_prod_assets['logL'],\n",
    "               empl_prod_assets['logK'],\n",
    "               empl_prod_assets['logQ'],\n",
    "               c = 'blue', marker = 'o', alpha = 0.5, label = 'data')\n",
    "    ax.plot_surface(x_surf,\n",
    "                    y_surf,\n",
    "                    fittedY.values.reshape(x_surf.shape),\n",
    "                    color = 'Red', edgecolor = 'black', alpha = 0.1, linewidth = 0.2)\n",
    "    ax.set_xlabel(r'$\\log{L}$')\n",
    "    ax.set_ylabel(r'$\\log{K}$')\n",
    "    ax.set_zlabel(r'$\\log{Q}$')\n",
    "    ax.legend()\n",
    "    ax.view_init(elev, azim)\n",
    "    return ax\n",
    "\n",
    "plot_data_model(131, (30, 10), 20, 90)\n",
    "plot_data_model(132, (30, 10), 20, 30)\n",
    "plot_data_model(133, (30, 10), 20, 0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(empl_prod_assets['logL_K'], empl_prod_assets['logQ_K'], label = \"data\")\n",
    "ax.plot(empl_prod_assets['logL_K'], empl_prod_assets['pred_con_ret'], color = 'red', label = \"predictions\")\n",
    "ax.set_xlabel(r'$\\log{(L/K)}$')\n",
    "ax.set_ylabel(r'$\\log{(Q/K)}$')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    8.195524\n",
       "logL         0.319914\n",
       "logK         0.348494\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Values of regression coefficients - variable returns\n",
    "model.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We therefore get\n",
    "\n",
    "$$\\ln{Q} = 8.195524 + 0.319914\\ln{L} + 0.348494\\ln{K}$$\n",
    "\n",
    "$$Q = e^{8.195524}L^{0.319914}K^{0.348494}$$\n",
    "\n",
    "$$Q = 3625L^{0.319914}K^{0.348494}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    3.500330\n",
       "logL_K       0.875436\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Values of regression coefficients - constant returns\n",
    "model_constant_growth.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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