StatsModelLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression using StatsModels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import StatsModels for TimeSeries Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.0'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import StatsModels load data and parse into Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.980</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.150</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.350</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.370</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.540</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>9267.7</td>\n",
       "      <td>1990.693</td>\n",
       "      <td>991.551</td>\n",
       "      <td>9838.3</td>\n",
       "      <td>216.889</td>\n",
       "      <td>1474.7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>305.270</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>9195.3</td>\n",
       "      <td>1857.661</td>\n",
       "      <td>1007.273</td>\n",
       "      <td>9920.4</td>\n",
       "      <td>212.174</td>\n",
       "      <td>1576.5</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>305.952</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>9209.2</td>\n",
       "      <td>1558.494</td>\n",
       "      <td>996.287</td>\n",
       "      <td>9926.4</td>\n",
       "      <td>212.671</td>\n",
       "      <td>1592.8</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>306.547</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9189.0</td>\n",
       "      <td>1456.678</td>\n",
       "      <td>1023.528</td>\n",
       "      <td>10077.5</td>\n",
       "      <td>214.469</td>\n",
       "      <td>1653.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.2</td>\n",
       "      <td>307.226</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9256.0</td>\n",
       "      <td>1486.398</td>\n",
       "      <td>1044.088</td>\n",
       "      <td>10040.6</td>\n",
       "      <td>216.385</td>\n",
       "      <td>1673.9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>308.013</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-3.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
       "0    1959.0      1.0   2710.349    1707.4   286.898   470.045   1886.9   \n",
       "1    1959.0      2.0   2778.801    1733.7   310.859   481.301   1919.7   \n",
       "2    1959.0      3.0   2775.488    1751.8   289.226   491.260   1916.4   \n",
       "3    1959.0      4.0   2785.204    1753.7   299.356   484.052   1931.3   \n",
       "4    1960.0      1.0   2847.699    1770.5   331.722   462.199   1955.5   \n",
       "..      ...      ...        ...       ...       ...       ...      ...   \n",
       "198  2008.0      3.0  13324.600    9267.7  1990.693   991.551   9838.3   \n",
       "199  2008.0      4.0  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
       "200  2009.0      1.0  12925.410    9209.2  1558.494   996.287   9926.4   \n",
       "201  2009.0      2.0  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
       "202  2009.0      3.0  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
       "\n",
       "         cpi      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0     28.980   139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1     29.150   141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2     29.350   140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3     29.370   140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4     29.540   139.6      3.50    5.2  180.007  2.31     1.19  \n",
       "..       ...     ...       ...    ...      ...   ...      ...  \n",
       "198  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33  \n",
       "199  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91  \n",
       "200  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71  \n",
       "201  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19  \n",
       "202  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44  \n",
       "\n",
       "[203 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = sm.datasets.macrodata.load_pandas().data\n",
    "raw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::\n",
      "    Number of Observations - 203\n",
      "\n",
      "    Number of Variables - 14\n",
      "\n",
      "    Variable name definitions::\n",
      "\n",
      "        year      - 1959q1 - 2009q3\n",
      "        quarter   - 1-4\n",
      "        realgdp   - Real gross domestic product (Bil. of chained 2005 US$,\n",
      "                    seasonally adjusted annual rate)\n",
      "        realcons  - Real personal consumption expenditures (Bil. of chained\n",
      "                    2005 US$, seasonally adjusted annual rate)\n",
      "        realinv   - Real gross private domestic investment (Bil. of chained\n",
      "                    2005 US$, seasonally adjusted annual rate)\n",
      "        realgovt  - Real federal consumption expenditures & gross investment\n",
      "                    (Bil. of chained 2005 US$, seasonally adjusted annual rate)\n",
      "        realdpi   - Real private disposable income (Bil. of chained 2005\n",
      "                    US$, seasonally adjusted annual rate)\n",
      "        cpi       - End of the quarter consumer price index for all urban\n",
      "                    consumers: all items (1982-84 = 100, seasonally adjusted).\n",
      "        m1        - End of the quarter M1 nominal money stock (Seasonally\n",
      "                    adjusted)\n",
      "        tbilrate  - Quarterly monthly average of the monthly 3-month\n",
      "                    treasury bill: secondary market rate\n",
      "        unemp     - Seasonally adjusted unemployment rate (%)\n",
      "        pop       - End of the quarter total population: all ages incl. armed\n",
      "                    forces over seas\n",
      "        infl      - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)\n",
      "        realint   - Real interest rate (tbilrate - infl)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.macrodata.NOTE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Export to_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df.to_csv('./data/sm_macro_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import Csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_df = pd.read_csv('./data/sm_macro_data.csv', parse_dates=['year'])\n",
    "macro_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203 entries, 0 to 202\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   year      203 non-null    object \n",
      " 1   quarter   203 non-null    float64\n",
      " 2   realgdp   203 non-null    float64\n",
      " 3   realcons  203 non-null    float64\n",
      " 4   realinv   203 non-null    float64\n",
      " 5   realgovt  203 non-null    float64\n",
      " 6   realdpi   203 non-null    float64\n",
      " 7   cpi       203 non-null    float64\n",
      " 8   m1        203 non-null    float64\n",
      " 9   tbilrate  203 non-null    float64\n",
      " 10  unemp     203 non-null    float64\n",
      " 11  pop       203 non-null    float64\n",
      " 12  infl      203 non-null    float64\n",
      " 13  realint   203 non-null    float64\n",
      "dtypes: float64(13), object(1)\n",
      "memory usage: 22.3+ KB\n"
     ]
    }
   ],
   "source": [
    "macro_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Need to convert to float then int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203 entries, 0 to 202\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   year      203 non-null    float64\n",
      " 1   quarter   203 non-null    float64\n",
      " 2   realgdp   203 non-null    float64\n",
      " 3   realcons  203 non-null    float64\n",
      " 4   realinv   203 non-null    float64\n",
      " 5   realgovt  203 non-null    float64\n",
      " 6   realdpi   203 non-null    float64\n",
      " 7   cpi       203 non-null    float64\n",
      " 8   m1        203 non-null    float64\n",
      " 9   tbilrate  203 non-null    float64\n",
      " 10  unemp     203 non-null    float64\n",
      " 11  pop       203 non-null    float64\n",
      " 12  infl      203 non-null    float64\n",
      " 13  realint   203 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 22.3 KB\n"
     ]
    }
   ],
   "source": [
    "macro_df['year'] = macro_df['year'].astype(float)\n",
    "macro_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 203 entries, 0 to 202\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   year      203 non-null    int32  \n",
      " 1   quarter   203 non-null    float64\n",
      " 2   realgdp   203 non-null    float64\n",
      " 3   realcons  203 non-null    float64\n",
      " 4   realinv   203 non-null    float64\n",
      " 5   realgovt  203 non-null    float64\n",
      " 6   realdpi   203 non-null    float64\n",
      " 7   cpi       203 non-null    float64\n",
      " 8   m1        203 non-null    float64\n",
      " 9   tbilrate  203 non-null    float64\n",
      " 10  unemp     203 non-null    float64\n",
      " 11  pop       203 non-null    float64\n",
      " 12  infl      203 non-null    float64\n",
      " 13  realint   203 non-null    float64\n",
      "dtypes: float64(13), int32(1)\n",
      "memory usage: 21.5 KB\n"
     ]
    }
   ],
   "source": [
    "macro_df['year'] = macro_df['year'].astype(int)\n",
    "macro_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.Index( sm.tsa.datetools.dates_from_range('1959Q1', '2009Q3'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "macro_df.index = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-09-30</th>\n",
       "      <td>2008</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>9267.7</td>\n",
       "      <td>1990.693</td>\n",
       "      <td>991.551</td>\n",
       "      <td>9838.3</td>\n",
       "      <td>216.889</td>\n",
       "      <td>1474.7</td>\n",
       "      <td>1.17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>305.270</td>\n",
       "      <td>-3.16</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>2008</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>9195.3</td>\n",
       "      <td>1857.661</td>\n",
       "      <td>1007.273</td>\n",
       "      <td>9920.4</td>\n",
       "      <td>212.174</td>\n",
       "      <td>1576.5</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>305.952</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-31</th>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>9209.2</td>\n",
       "      <td>1558.494</td>\n",
       "      <td>996.287</td>\n",
       "      <td>9926.4</td>\n",
       "      <td>212.671</td>\n",
       "      <td>1592.8</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>306.547</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>2009</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9189.0</td>\n",
       "      <td>1456.678</td>\n",
       "      <td>1023.528</td>\n",
       "      <td>10077.5</td>\n",
       "      <td>214.469</td>\n",
       "      <td>1653.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.2</td>\n",
       "      <td>307.226</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-30</th>\n",
       "      <td>2009</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9256.0</td>\n",
       "      <td>1486.398</td>\n",
       "      <td>1044.088</td>\n",
       "      <td>10040.6</td>\n",
       "      <td>216.385</td>\n",
       "      <td>1673.9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>308.013</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-3.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
       "2008-09-30  2008      3.0  13324.600    9267.7  1990.693   991.551   9838.3   \n",
       "2008-12-31  2008      4.0  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
       "2009-03-31  2009      1.0  12925.410    9209.2  1558.494   996.287   9926.4   \n",
       "2009-06-30  2009      2.0  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
       "2009-09-30  2009      3.0  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
       "\n",
       "                cpi      m1  tbilrate  unemp      pop  infl  realint  \n",
       "2008-09-30  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33  \n",
       "2008-12-31  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91  \n",
       "2009-03-31  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71  \n",
       "2009-06-30  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19  \n",
       "2009-09-30  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macro_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "macro_df.to_csv('./data/macro_df_index.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_df = pd.read_csv('../PractiseFiles/data/macro_df_index.csv', index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>realinv</th>\n",
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       "    <tr>\n",
       "      <th>2008-09-30</th>\n",
       "      <td>2008</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13324.600</td>\n",
       "      <td>9267.7</td>\n",
       "      <td>1990.693</td>\n",
       "      <td>991.551</td>\n",
       "      <td>9838.3</td>\n",
       "      <td>216.889</td>\n",
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       "      <td>-3.16</td>\n",
       "      <td>4.33</td>\n",
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       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>2008</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13141.920</td>\n",
       "      <td>9195.3</td>\n",
       "      <td>1857.661</td>\n",
       "      <td>1007.273</td>\n",
       "      <td>9920.4</td>\n",
       "      <td>212.174</td>\n",
       "      <td>1576.5</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.9</td>\n",
       "      <td>305.952</td>\n",
       "      <td>-8.79</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-31</th>\n",
       "      <td>2009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12925.410</td>\n",
       "      <td>9209.2</td>\n",
       "      <td>1558.494</td>\n",
       "      <td>996.287</td>\n",
       "      <td>9926.4</td>\n",
       "      <td>212.671</td>\n",
       "      <td>1592.8</td>\n",
       "      <td>0.22</td>\n",
       "      <td>8.1</td>\n",
       "      <td>306.547</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>2009</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12901.504</td>\n",
       "      <td>9189.0</td>\n",
       "      <td>1456.678</td>\n",
       "      <td>1023.528</td>\n",
       "      <td>10077.5</td>\n",
       "      <td>214.469</td>\n",
       "      <td>1653.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>9.2</td>\n",
       "      <td>307.226</td>\n",
       "      <td>3.37</td>\n",
       "      <td>-3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-30</th>\n",
       "      <td>2009</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12990.341</td>\n",
       "      <td>9256.0</td>\n",
       "      <td>1486.398</td>\n",
       "      <td>1044.088</td>\n",
       "      <td>10040.6</td>\n",
       "      <td>216.385</td>\n",
       "      <td>1673.9</td>\n",
       "      <td>0.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>308.013</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-3.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
       "2008-09-30  2008      3.0  13324.600    9267.7  1990.693   991.551   9838.3   \n",
       "2008-12-31  2008      4.0  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
       "2009-03-31  2009      1.0  12925.410    9209.2  1558.494   996.287   9926.4   \n",
       "2009-06-30  2009      2.0  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
       "2009-09-30  2009      3.0  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
       "\n",
       "                cpi      m1  tbilrate  unemp      pop  infl  realint  \n",
       "2008-09-30  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33  \n",
       "2008-12-31  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91  \n",
       "2009-03-31  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71  \n",
       "2009-06-30  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19  \n",
       "2009-09-30  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 203 entries, 1959-03-31 to 2009-09-30\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   year      203 non-null    int64  \n",
      " 1   quarter   203 non-null    float64\n",
      " 2   realgdp   203 non-null    float64\n",
      " 3   realcons  203 non-null    float64\n",
      " 4   realinv   203 non-null    float64\n",
      " 5   realgovt  203 non-null    float64\n",
      " 6   realdpi   203 non-null    float64\n",
      " 7   cpi       203 non-null    float64\n",
      " 8   m1        203 non-null    float64\n",
      " 9   tbilrate  203 non-null    float64\n",
      " 10  unemp     203 non-null    float64\n",
      " 11  pop       203 non-null    float64\n",
      " 12  infl      203 non-null    float64\n",
      " 13  realint   203 non-null    float64\n",
      "dtypes: float64(13), int64(1)\n",
      "memory usage: 23.8+ KB\n"
     ]
    }
   ],
   "source": [
    "index_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Mark\\anaconda3\\envs\\finance-venv\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1235: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Trend of Private Domestic Investment and Government Expenditure')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "index_df[['realgovt', 'realinv']].plot(figsize=(15,7))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Value in $USD')\n",
    "plt.title('Trend of Private Domestic Investment and Government Expenditure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression of realgovt and realinv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Is there a correlation between realinv and realgovt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>realgovt</th>\n",
       "      <th>realinv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>realgovt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.794797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>realinv</th>\n",
       "      <td>0.794797</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          realgovt   realinv\n",
       "realgovt  1.000000  0.794797\n",
       "realinv   0.794797  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = index_df[['realgovt', 'realinv']].corr()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### -a 0.79 correlation hence a 79% correlation between realgovt and realinv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.title(\"Correlation of Private Domestic Investment and Federal Expenditure and Investment\", \n",
    "         loc='center')\n",
    "sns.heatmap(data=data, annot=True)\n",
    "plt.savefig('./data/images/heatmap.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('./data/images/heatmap.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Can we performa a linear regression?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "capture federal consumption expenditureand gross investment index_df['realgovt'] from X of index_df['realinv'] i.e private domestic investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "::\n",
      "    Number of Observations - 203\n",
      "\n",
      "    Number of Variables - 14\n",
      "\n",
      "    Variable name definitions::\n",
      "\n",
      "        year      - 1959q1 - 2009q3\n",
      "        quarter   - 1-4\n",
      "        realgdp   - Real gross domestic product (Bil. of chained 2005 US$,\n",
      "                    seasonally adjusted annual rate)\n",
      "        realcons  - Real personal consumption expenditures (Bil. of chained\n",
      "                    2005 US$, seasonally adjusted annual rate)\n",
      "        realinv   - Real gross private domestic investment (Bil. of chained\n",
      "                    2005 US$, seasonally adjusted annual rate)\n",
      "        realgovt  - Real federal consumption expenditures & gross investment\n",
      "                    (Bil. of chained 2005 US$, seasonally adjusted annual rate)\n",
      "        realdpi   - Real private disposable income (Bil. of chained 2005\n",
      "                    US$, seasonally adjusted annual rate)\n",
      "        cpi       - End of the quarter consumer price index for all urban\n",
      "                    consumers: all items (1982-84 = 100, seasonally adjusted).\n",
      "        m1        - End of the quarter M1 nominal money stock (Seasonally\n",
      "                    adjusted)\n",
      "        tbilrate  - Quarterly monthly average of the monthly 3-month\n",
      "                    treasury bill: secondary market rate\n",
      "        unemp     - Seasonally adjusted unemployment rate (%)\n",
      "        pop       - End of the quarter total population: all ages incl. armed\n",
      "                    forces over seas\n",
      "        infl      - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)\n",
      "        realint   - Real interest rate (tbilrate - infl)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sm.datasets.macrodata.NOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sm.add_constant(index_df['realgovt'])\n",
    "y = index_df['realinv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>const</th>\n",
       "      <th>realgovt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-03-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>470.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>481.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>491.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>484.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>462.199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            const  realgovt\n",
       "1959-03-31    1.0   470.045\n",
       "1959-06-30    1.0   481.301\n",
       "1959-09-30    1.0   491.260\n",
       "1959-12-31    1.0   484.052\n",
       "1960-03-31    1.0   462.199"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959-03-31     286.898\n",
       "1959-06-30     310.859\n",
       "1959-09-30     289.226\n",
       "1959-12-31     299.356\n",
       "1960-03-31     331.722\n",
       "                ...   \n",
       "2008-09-30    1990.693\n",
       "2008-12-31    1857.661\n",
       "2009-03-31    1558.494\n",
       "2009-06-30    1456.678\n",
       "2009-09-30    1486.398\n",
       "Name: realinv, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.OLS at 0x2b1d67c87f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                realinv   R-squared:                       0.632\n",
      "Model:                            OLS   Adj. R-squared:                  0.630\n",
      "Method:                 Least Squares   F-statistic:                     344.8\n",
      "Date:                Wed, 23 Sep 2020   Prob (F-statistic):           1.78e-45\n",
      "Time:                        15:18:03   Log-Likelihood:                -1479.6\n",
      "No. Observations:                 203   AIC:                             2963.\n",
      "Df Residuals:                     201   BIC:                             2970.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -1177.0037    120.558     -9.763      0.000   -1414.724    -939.284\n",
      "realgovt       3.3013      0.178     18.568      0.000       2.951       3.652\n",
      "==============================================================================\n",
      "Omnibus:                        8.118   Durbin-Watson:                   0.032\n",
      "Prob(Omnibus):                  0.017   Jarque-Bera (JB):                6.521\n",
      "Skew:                           0.342   Prob(JB):                       0.0384\n",
      "Kurtosis:                       2.449   Cond. No.                     3.27e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.27e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>      <td>0.630</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>      <td>realinv</td>            <td>AIC:</td>          <td>2963.2626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2020-09-23 15:18</td>        <td>BIC:</td>          <td>2969.8890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>         <td>203</td>         <td>Log-Likelihood:</td>     <td>-1479.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>1</td>           <td>F-statistic:</td>        <td>344.8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>           <td>201</td>       <td>Prob (F-statistic):</td>  <td>1.78e-45</td> \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.632</td>            <td>Scale:</td>        <td>1.2671e+05</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>const</th>    <td>-1177.0037</td> <td>120.5577</td> <td>-9.7630</td> <td>0.0000</td> <td>-1414.7238</td> <td>-939.2836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>realgovt</th>   <td>3.3013</td>    <td>0.1778</td>  <td>18.5676</td> <td>0.0000</td>   <td>2.9507</td>    <td>3.6519</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>8.118</td>  <td>Durbin-Watson:</td>   <td>0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td> <td>0.017</td> <td>Jarque-Bera (JB):</td> <td>6.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>     <td>0.342</td>     <td>Prob(JB):</td>     <td>0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>   <td>2.449</td>  <td>Condition No.:</td>   <td>3272</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                  Results: Ordinary least squares\n",
       "===================================================================\n",
       "Model:              OLS              Adj. R-squared:     0.630     \n",
       "Dependent Variable: realinv          AIC:                2963.2626 \n",
       "Date:               2020-09-23 15:18 BIC:                2969.8890 \n",
       "No. Observations:   203              Log-Likelihood:     -1479.6   \n",
       "Df Model:           1                F-statistic:        344.8     \n",
       "Df Residuals:       201              Prob (F-statistic): 1.78e-45  \n",
       "R-squared:          0.632            Scale:              1.2671e+05\n",
       "-------------------------------------------------------------------\n",
       "              Coef.    Std.Err.    t    P>|t|    [0.025     0.975] \n",
       "-------------------------------------------------------------------\n",
       "const       -1177.0037 120.5577 -9.7630 0.0000 -1414.7238 -939.2836\n",
       "realgovt        3.3013   0.1778 18.5676 0.0000     2.9507    3.6519\n",
       "-------------------------------------------------------------------\n",
       "Omnibus:                8.118        Durbin-Watson:           0.032\n",
       "Prob(Omnibus):          0.017        Jarque-Bera (JB):        6.521\n",
       "Skew:                   0.342        Prob(JB):                0.038\n",
       "Kurtosis:               2.449        Condition No.:           3272 \n",
       "===================================================================\n",
       "* The condition number is large (3e+03). This might indicate\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7835845286436183e-45"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.f_pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3271.867803473453"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.condition_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>student_resid</th>\n",
       "      <th>unadj_p</th>\n",
       "      <th>bonf(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-03-31</th>\n",
       "      <td>-0.248053</td>\n",
       "      <td>0.804348</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-30</th>\n",
       "      <td>-0.285173</td>\n",
       "      <td>0.775807</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-30</th>\n",
       "      <td>-0.438902</td>\n",
       "      <td>0.661206</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-31</th>\n",
       "      <td>-0.343242</td>\n",
       "      <td>0.731777</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31</th>\n",
       "      <td>-0.048414</td>\n",
       "      <td>0.961435</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09-30</th>\n",
       "      <td>-0.301211</td>\n",
       "      <td>0.763567</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>-0.830380</td>\n",
       "      <td>0.407314</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-03-31</th>\n",
       "      <td>-1.587071</td>\n",
       "      <td>0.114076</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-30</th>\n",
       "      <td>-2.153191</td>\n",
       "      <td>0.032501</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-30</th>\n",
       "      <td>-2.270782</td>\n",
       "      <td>0.024226</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            student_resid   unadj_p  bonf(p)\n",
       "1959-03-31      -0.248053  0.804348      1.0\n",
       "1959-06-30      -0.285173  0.775807      1.0\n",
       "1959-09-30      -0.438902  0.661206      1.0\n",
       "1959-12-31      -0.343242  0.731777      1.0\n",
       "1960-03-31      -0.048414  0.961435      1.0\n",
       "...                   ...       ...      ...\n",
       "2008-09-30      -0.301211  0.763567      1.0\n",
       "2008-12-31      -0.830380  0.407314      1.0\n",
       "2009-03-31      -1.587071  0.114076      1.0\n",
       "2009-06-30      -2.153191  0.032501      1.0\n",
       "2009-09-30      -2.270782  0.024226      1.0\n",
       "\n",
       "[203 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.outlier_test(method='bonf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const      -1177.003679\n",
       "realgovt       3.301331\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const       -9.762990\n",
       "realgovt    18.567561\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.tvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Test for Constraints                             \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "c0         -1177.0037    120.558     -9.763      0.000   -1414.724    -939.284\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(results.t_test([1,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 374.77054816,  411.93033223,  444.80828972,  421.01229438,\n",
       "        348.86830351,  342.92920866,  390.05901298,  395.86275324,\n",
       "        393.94798114,  408.71813697,  453.28610827,  481.98458045,\n",
       "        542.85782658,  549.81043011,  601.87902591,  592.2193308 ,\n",
       "        549.31853176,  533.44242998,  628.47124878,  580.56893297,\n",
       "        571.6652427 ,  560.07426883,  546.31762169,  521.8712641 ,\n",
       "        500.09238212,  503.14611349,  570.87292321,  619.31995868,\n",
       "        660.4941615 ,  709.28123408,  785.68394221,  802.23681689,\n",
       "        938.09980139,  907.55588506,  940.81349565,  936.62080501,\n",
       "        973.41083999,  956.13497378,  937.8786122 ,  925.04963913,\n",
       "        913.47847325,  880.25387598,  881.74937901,  826.57423055,\n",
       "        786.91864008,  725.41153836,  697.30400446,  687.14580833,\n",
       "        614.65847901,  588.89489027,  581.43718307,  526.94541016,\n",
       "        533.7197418 ,  561.05806553,  467.44221648,  462.2393185 ,\n",
       "        489.63376486,  463.86687478,  394.08993839,  395.00440713,\n",
       "        444.09190085,  441.23294803,  449.18585491,  461.03763394,\n",
       "        442.63931512,  429.68489146,  469.81917495,  474.12741218,\n",
       "        459.03042457,  455.61024544,  451.01809373,  455.22398968,\n",
       "        468.73963965,  495.76433691,  505.37121072,  488.79522673,\n",
       "        494.50652972,  534.29417343,  540.34551353,  555.47551446,\n",
       "        557.92510221,  579.43657636,  576.76910075,  576.41915964,\n",
       "        632.50547552,  677.99781956,  652.89779839,  658.96564515,\n",
       "        696.89133806,  752.75646475,  750.61059946,  771.19439954,\n",
       "        774.22502159,  791.92015686,  821.5231938 ,  880.73917167,\n",
       "        905.71704358,  950.11664699, 1009.83772853,  933.19732455,\n",
       "        951.14996366, 1017.85005937, 1009.44817145, 1082.03784204,\n",
       "       1106.23990112, 1162.06871316, 1240.57767061, 1239.6565992 ,\n",
       "       1226.77810616, 1303.39870215, 1397.27535637, 1357.33255009,\n",
       "       1372.43944169, 1407.81320558, 1410.59292646, 1449.09635233,\n",
       "       1377.73477695, 1351.74999902, 1332.81686454, 1408.15324269,\n",
       "       1355.1965888 , 1412.14785345, 1437.05309608, 1423.93030453,\n",
       "       1463.00816204, 1466.17083733, 1442.64555115, 1465.79448557,\n",
       "       1486.42780562, 1494.04067539, 1444.21038214, 1392.67990332,\n",
       "       1393.30715625, 1390.6462833 , 1419.95220043, 1421.35526619,\n",
       "       1341.58520023, 1306.11899907, 1279.93614126, 1292.72879969,\n",
       "       1204.2069047 , 1190.70115873, 1255.71427419, 1189.06699978,\n",
       "       1184.52436804, 1175.1683954 , 1159.18004836, 1071.47028084,\n",
       "       1118.29636269, 1151.00265096, 1109.96380272, 1103.3710443 ,\n",
       "       1072.6719654 , 1112.55204639, 1105.07783253, 1101.94156788,\n",
       "       1030.87050965, 1091.6183052 , 1072.76110134, 1094.79748715,\n",
       "       1079.78963548, 1079.76652616, 1127.11421834, 1178.31456404,\n",
       "       1085.12128538, 1175.66029375, 1130.05570445, 1119.39240465,\n",
       "       1168.2719145 , 1218.51817548, 1234.59565846, 1263.72990636,\n",
       "       1321.82343162, 1379.57361843, 1420.06444569, 1463.95234276,\n",
       "       1464.70834761, 1592.07040426, 1594.78739984, 1615.00475215,\n",
       "       1650.81759309, 1666.89507607, 1716.23347097, 1680.61540857,\n",
       "       1692.52661157, 1695.29972978, 1762.48181985, 1713.49996873,\n",
       "       1795.88138768, 1770.55357465, 1767.79036043, 1775.72015799,\n",
       "       1737.2992654 , 1789.94559417, 1856.86357774, 1877.09083405,\n",
       "       1937.37974456, 1996.49998382, 2096.4345808 , 2148.33811004,\n",
       "       2112.06968539, 2202.00124881, 2269.87661844])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = results.predict()\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_df['pred_realinv'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 203 entries, 1959-03-31 to 2009-09-30\n",
      "Data columns (total 15 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   year          203 non-null    int64  \n",
      " 1   quarter       203 non-null    float64\n",
      " 2   realgdp       203 non-null    float64\n",
      " 3   realcons      203 non-null    float64\n",
      " 4   realinv       203 non-null    float64\n",
      " 5   realgovt      203 non-null    float64\n",
      " 6   realdpi       203 non-null    float64\n",
      " 7   cpi           203 non-null    float64\n",
      " 8   m1            203 non-null    float64\n",
      " 9   tbilrate      203 non-null    float64\n",
      " 10  unemp         203 non-null    float64\n",
      " 11  pop           203 non-null    float64\n",
      " 12  infl          203 non-null    float64\n",
      " 13  realint       203 non-null    float64\n",
      " 14  pred_realinv  203 non-null    float64\n",
      "dtypes: float64(14), int64(1)\n",
      "memory usage: 35.4+ KB\n"
     ]
    }
   ],
   "source": [
    "index_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Mark\\anaconda3\\envs\\finance-venv\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1235: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n",
      "<ipython-input-56-86f20b0f417e>:9: MatplotlibDeprecationWarning: savefig() got unexpected keyword argument \"papertype\" which is no longer supported as of 3.3 and will become an error two minor releases later\n",
      "  plt.savefig('./data/images/final_plot_dpi.jpg', format='jpg',dpi=150.5,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "index_df[['realinv','realgovt', 'pred_realinv']].plot(figsize=(15, 7))\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Value in $USD')\n",
    "plt.title('Government Expenditure & Investment vs Private Domestic Investment and its Predicted Values')\n",
    "plt.legend()\n",
    "#plt.show()\n",
    "plt.savefig('./data/images/final_plot.png')\n",
    "plt.savefig('./data/images/final_plot_dpi.jpg', format='jpg',dpi=150.5,\n",
    "            pad_inches=0.1, transparent=False,\n",
    "            papertype='letter' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are trying to predict private domestic investment based on government expenditure. <br>\n",
    "We can see the prediction values of private domestic investment matches the curve of the actual government expenditure. <br>\n",
    "There are subtle dips in goverment expenditure where there is a spike in actual government expenses. <br>\n",
    "From the financial crash, there was a great dip in private domestic investment. <br>\n",
    "The prediction values fail to capture this dip as they are only considering the government expenditure which had a subtle increase during that period.<br>\n",
    "The model is therefore <b>Linear</b> in that it follows closely with its Independent X to predict the dependent Y inorder to get the values for the yhat"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}