Data Modeling - VARMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f06aa0fb",
   "metadata": {},
   "source": [
    "# Drilling Activity Prediction: Data Modeling - VARMA\n",
    "\n",
    "- Load datasets and resample to monthly frequency\n",
    "- Use oil dataset to make predictions on Oil Rig Count:\n",
    "    - Get second order differencing data frame (stationary features dataframe) \n",
    "    - Train/test split\n",
    "    - VARMA Model Hyperparameter selection\n",
    "    - Fit the model\n",
    "    - Invert Transformation\n",
    "    - Generate forecast values and plot results\n",
    "    - Evaluation Metrics\n",
    "    - Fit the model again but with 100% of the data\n",
    "    - Forecast real future values\n",
    "\n",
    "\n",
    "- Use gas dataset to make predictions on Gas Rig Count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7da6af7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.statespace.varmax import VARMAX, VARMAXResults\n",
    "from statsmodels.tsa.stattools import adfuller, grangercausalitytests\n",
    "from statsmodels.tools.eval_measures import rmse, mse, meanabs\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "import time\n",
    "from pylab import rcParams\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# Size of all plots\n",
    "rcParams['figure.figsize'] = 6,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56adb9cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfoil = pd.read_csv('dfoil.csv',index_col=0,parse_dates=True)\n",
    "dfoil = dfoil.resample('M').mean()\n",
    "dfoil.index.freq = 'M'\n",
    "\n",
    "\n",
    "dfgas = pd.read_csv('dfgas.csv',index_col=0,parse_dates=True)\n",
    "dfgas = dfgas.resample('M').mean()\n",
    "dfgas.index.freq = 'M'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5765c48e",
   "metadata": {},
   "source": [
    "A $K$-dimensional VARMA model of order $(p,q)$ considers each variable $y_K$ in the system.<br>\n",
    "\n",
    "For example, the system of equations for a 2-dimensional VARMA(1,1) model is:\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{1,t} = c_1 + \\phi_{11,1}y_{1,t-1} + \\phi_{12,1}y_{2,t-1} + \\theta_{11,1}\\varepsilon_{1,t-1} + \\theta_{12,1}\\varepsilon_{2,t-1} + \\varepsilon_{1,t}$<br>\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{2,t} = c_2 + \\phi_{21,1}y_{1,t-1} + \\phi_{22,1}y_{2,t-1} + \\theta_{21,1}\\varepsilon_{1,t-1} + \\theta_{22,1}\\varepsilon_{2,t-1} + \\varepsilon_{2,t}$\n",
    "\n",
    "where the coefficient $\\theta_{ii,l}$ captures the influence of the $l$th lag of error $\\varepsilon_i$ on itself,<br>\n",
    "the coefficient $\\theta_{ij,l}$ captures the influence of the $l$th lag of error $\\varepsilon_j$ on $\\varepsilon_i$,<br>\n",
    "and $\\varepsilon_{1,t}$ and $\\varepsilon_{2,t}$ are residual white noise.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3513e896",
   "metadata": {},
   "source": [
    "The order (2,2) is the one that we have been using so far. This means differencing to second level order to guarantee \n",
    "stationarity of all features (We used Augmented Dicky Fuller Tests for this)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6696fd30",
   "metadata": {},
   "source": [
    "#### Get second order differencing data frame (stationary features dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8f2a8c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>oilrig_count</th>\n",
       "      <th>oil_price</th>\n",
       "      <th>oilconsumption</th>\n",
       "      <th>oilproduction</th>\n",
       "      <th>oilstorage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-03-31</th>\n",
       "      <td>-7.056452</td>\n",
       "      <td>1.312871</td>\n",
       "      <td>481.072565</td>\n",
       "      <td>-72.177714</td>\n",
       "      <td>-6540.096313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-30</th>\n",
       "      <td>-8.104032</td>\n",
       "      <td>0.182258</td>\n",
       "      <td>-152.485198</td>\n",
       "      <td>92.726362</td>\n",
       "      <td>2345.221198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-31</th>\n",
       "      <td>10.490323</td>\n",
       "      <td>2.344355</td>\n",
       "      <td>424.190083</td>\n",
       "      <td>-43.429157</td>\n",
       "      <td>-3579.112903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-06-30</th>\n",
       "      <td>-9.318280</td>\n",
       "      <td>-2.697634</td>\n",
       "      <td>-138.891237</td>\n",
       "      <td>-14.576469</td>\n",
       "      <td>-2234.476344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-07-31</th>\n",
       "      <td>3.823656</td>\n",
       "      <td>1.878333</td>\n",
       "      <td>-50.252335</td>\n",
       "      <td>-5.380888</td>\n",
       "      <td>-9928.515054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            oilrig_count  oil_price  oilconsumption  oilproduction  \\\n",
       "1997-03-31     -7.056452   1.312871      481.072565     -72.177714   \n",
       "1997-04-30     -8.104032   0.182258     -152.485198      92.726362   \n",
       "1997-05-31     10.490323   2.344355      424.190083     -43.429157   \n",
       "1997-06-30     -9.318280  -2.697634     -138.891237     -14.576469   \n",
       "1997-07-31      3.823656   1.878333      -50.252335      -5.380888   \n",
       "\n",
       "             oilstorage  \n",
       "1997-03-31 -6540.096313  \n",
       "1997-04-30  2345.221198  \n",
       "1997-05-31 -3579.112903  \n",
       "1997-06-30 -2234.476344  \n",
       "1997-07-31 -9928.515054  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfoil_transformed = dfoil.diff().diff()\n",
    "dfoil_transformed = dfoil_transformed.dropna()\n",
    "dfoil_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017d7f8d",
   "metadata": {},
   "source": [
    "#### Train/test split\n",
    "\n",
    "- We will evaluate the model forecasting 12 months ahead (1 year)\n",
    "- Train test split. Test df will have 12 data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc31eb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "nobs is the variable that will be used to determine the number of months to predict.\n",
    "\"\"\"\n",
    "\n",
    "nobs=12\n",
    "train, test = dfoil_transformed[0:-nobs], dfoil_transformed[-nobs:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d582867c",
   "metadata": {},
   "source": [
    "#### Fit VARMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03dd5cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['oilrig_count', 'oil_price', 'oilconsumption', 'oilproduction', 'oilstorage']</td> <th>  No. Observations:  </th>    <td>284</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                             <td>VARMA(2,2)</td>                                   <th>  Log Likelihood     </th> <td>-9057.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                   <td>+ intercept</td>                                  <th>  AIC                </th> <td>18375.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                                           <td>Sun, 21 Nov 2021</td>                                <th>  BIC                </th> <td>18849.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                               <td>11:22:32</td>                                    <th>  HQIC               </th> <td>18565.776</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                                            <td>03-31-1997</td>                                   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                  <td>- 10-31-2020</td>                                  <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                                       <td>opg</td>                                      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.83, 0.34, 4.51, 0.50, 1.34</td> <th>  Jarque-Bera (JB):  </th> <td>1895.97, 37.73, 803.86, 953.23, 0.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.36, 0.56, 0.03, 0.48, 0.25</td> <th>  Prob(JB):          </th>     <td>0.00, 0.00, 0.00, 0.00, 0.95</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>4.99, 3.84, 2.31, 2.58, 1.97</td> <th>  Skew:              </th>    <td>-1.68, -0.14, -1.08, 0.03, 0.03</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00, 0.00, 0.00, 0.00, 0.00</td> <th>  Kurtosis:          </th>    <td>15.21, 4.77, 10.95, 11.98, 2.92</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilrig_count</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>    0.0144</td> <td>    4.203</td> <td>    0.003</td> <td> 0.997</td> <td>   -8.223</td> <td>    8.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td> 9.495e-06</td> <td>    0.000</td> <td>    0.090</td> <td> 0.929</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>    0.1097</td> <td>    1.002</td> <td>    0.110</td> <td> 0.913</td> <td>   -1.853</td> <td>    2.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>    0.6419</td> <td>    0.327</td> <td>    1.965</td> <td> 0.049</td> <td>    0.002</td> <td>    1.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>    0.4176</td> <td>    6.186</td> <td>    0.068</td> <td> 0.946</td> <td>  -11.707</td> <td>   12.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -0.0045</td> <td>    0.025</td> <td>   -0.179</td> <td> 0.858</td> <td>   -0.053</td> <td>    0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -0.0033</td> <td>    0.043</td> <td>   -0.075</td> <td> 0.940</td> <td>   -0.088</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>   -0.0001</td> <td>    0.001</td> <td>   -0.108</td> <td> 0.914</td> <td>   -0.003</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -0.4715</td> <td>    0.529</td> <td>   -0.890</td> <td> 0.373</td> <td>   -1.509</td> <td>    0.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>    0.2003</td> <td>    0.977</td> <td>    0.205</td> <td> 0.837</td> <td>   -1.714</td> <td>    2.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -0.0047</td> <td>    0.004</td> <td>   -1.208</td> <td> 0.227</td> <td>   -0.012</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.0017</td> <td>    0.026</td> <td>   -0.067</td> <td> 0.946</td> <td>   -0.053</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td> -2.18e-06</td> <td>    0.001</td> <td>   -0.003</td> <td> 0.997</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>    0.1287</td> <td>    0.317</td> <td>    0.406</td> <td> 0.685</td> <td>   -0.493</td> <td>    0.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>    0.0785</td> <td>    6.217</td> <td>    0.013</td> <td> 0.990</td> <td>  -12.107</td> <td>   12.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>    0.0006</td> <td>    0.024</td> <td>    0.026</td> <td> 0.979</td> <td>   -0.047</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>   -0.0027</td> <td>    0.043</td> <td>   -0.062</td> <td> 0.951</td> <td>   -0.088</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td> 7.593e-05</td> <td>    0.001</td> <td>    0.053</td> <td> 0.958</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>   -0.2823</td> <td>    0.279</td> <td>   -1.011</td> <td> 0.312</td> <td>   -0.829</td> <td>    0.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>    0.3685</td> <td>    2.539</td> <td>    0.145</td> <td> 0.885</td> <td>   -4.609</td> <td>    5.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.0005</td> <td>    0.019</td> <td>   -0.027</td> <td> 0.978</td> <td>   -0.037</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>   -0.0002</td> <td>    0.035</td> <td>   -0.007</td> <td> 0.994</td> <td>   -0.069</td> <td>    0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>-1.131e-05</td> <td>    0.001</td> <td>   -0.013</td> <td> 0.990</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oil_price</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>   -0.0055</td> <td>    0.073</td> <td>   -0.076</td> <td> 0.939</td> <td>   -0.148</td> <td>    0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>   48.0843</td> <td>  119.999</td> <td>    0.401</td> <td> 0.689</td> <td> -187.110</td> <td>  283.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>   -0.0004</td> <td>    0.003</td> <td>   -0.131</td> <td> 0.896</td> <td>   -0.007</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>   -0.0509</td> <td>    0.072</td> <td>   -0.705</td> <td> 0.481</td> <td>   -0.192</td> <td>    0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>   -0.3941</td> <td>    2.351</td> <td>   -0.168</td> <td> 0.867</td> <td>   -5.001</td> <td>    4.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -0.0004</td> <td>    0.011</td> <td>   -0.032</td> <td> 0.975</td> <td>   -0.022</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>    0.0017</td> <td>    0.014</td> <td>    0.125</td> <td> 0.900</td> <td>   -0.025</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>-3.447e-05</td> <td>    0.001</td> <td>   -0.059</td> <td> 0.953</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -0.0505</td> <td>    0.196</td> <td>   -0.257</td> <td> 0.797</td> <td>   -0.435</td> <td>    0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>   -0.1064</td> <td>    0.347</td> <td>   -0.307</td> <td> 0.759</td> <td>   -0.786</td> <td>    0.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -0.0005</td> <td>    0.002</td> <td>   -0.266</td> <td> 0.790</td> <td>   -0.004</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.0009</td> <td>    0.007</td> <td>   -0.129</td> <td> 0.898</td> <td>   -0.015</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td> 6.497e-05</td> <td>    0.000</td> <td>    0.438</td> <td> 0.661</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>   -0.0069</td> <td>    0.078</td> <td>   -0.089</td> <td> 0.929</td> <td>   -0.159</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   -0.0220</td> <td>    2.358</td> <td>   -0.009</td> <td> 0.993</td> <td>   -4.645</td> <td>    4.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>-7.258e-05</td> <td>    0.011</td> <td>   -0.007</td> <td> 0.995</td> <td>   -0.022</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>    0.0010</td> <td>    0.014</td> <td>    0.071</td> <td> 0.943</td> <td>   -0.026</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td> -2.36e-06</td> <td>    0.001</td> <td>   -0.004</td> <td> 0.997</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>    0.0140</td> <td>    0.097</td> <td>    0.145</td> <td> 0.885</td> <td>   -0.175</td> <td>    0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   -0.0986</td> <td>    0.873</td> <td>   -0.113</td> <td> 0.910</td> <td>   -1.810</td> <td>    1.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>-8.958e-05</td> <td>    0.007</td> <td>   -0.012</td> <td> 0.990</td> <td>   -0.014</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>    0.0017</td> <td>    0.012</td> <td>    0.141</td> <td> 0.887</td> <td>   -0.021</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>-9.453e-06</td> <td>    0.000</td> <td>   -0.036</td> <td> 0.971</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilconsumption</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>  2.97e-05</td> <td>    0.000</td> <td>    0.112</td> <td> 0.910</td> <td>   -0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>   -0.9735</td> <td>    2.045</td> <td>   -0.476</td> <td> 0.634</td> <td>   -4.981</td> <td>    3.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td> -540.3990</td> <td>   10.046</td> <td>  -53.791</td> <td> 0.000</td> <td> -560.089</td> <td> -520.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>    8.6605</td> <td>   10.727</td> <td>    0.807</td> <td> 0.419</td> <td>  -12.363</td> <td>   29.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>   12.1982</td> <td>   28.333</td> <td>    0.431</td> <td> 0.667</td> <td>  -43.334</td> <td>   67.730</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -0.8172</td> <td>    0.541</td> <td>   -1.511</td> <td> 0.131</td> <td>   -1.878</td> <td>    0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -0.0145</td> <td>    1.995</td> <td>   -0.007</td> <td> 0.994</td> <td>   -3.924</td> <td>    3.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>    0.0025</td> <td>    0.028</td> <td>    0.091</td> <td> 0.928</td> <td>   -0.052</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -5.4630</td> <td>   10.737</td> <td>   -0.509</td> <td> 0.611</td> <td>  -26.507</td> <td>   15.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>    3.1575</td> <td>   39.546</td> <td>    0.080</td> <td> 0.936</td> <td>  -74.351</td> <td>   80.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -0.3608</td> <td>    0.211</td> <td>   -1.706</td> <td> 0.088</td> <td>   -0.775</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.1385</td> <td>    0.917</td> <td>   -0.151</td> <td> 0.880</td> <td>   -1.935</td> <td>    1.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>    0.0039</td> <td>    0.021</td> <td>    0.183</td> <td> 0.855</td> <td>   -0.038</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>   -1.8528</td> <td>   12.489</td> <td>   -0.148</td> <td> 0.882</td> <td>  -26.330</td> <td>   22.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   -6.8748</td> <td>   27.174</td> <td>   -0.253</td> <td> 0.800</td> <td>  -60.135</td> <td>   46.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>   -0.2136</td> <td>    0.546</td> <td>   -0.391</td> <td> 0.696</td> <td>   -1.284</td> <td>    0.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>    0.1649</td> <td>    1.904</td> <td>    0.087</td> <td> 0.931</td> <td>   -3.566</td> <td>    3.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td>   -0.0074</td> <td>    0.027</td> <td>   -0.269</td> <td> 0.788</td> <td>   -0.061</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>   -0.0141</td> <td>   13.163</td> <td>   -0.001</td> <td> 0.999</td> <td>  -25.814</td> <td>   25.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   -7.0769</td> <td>   39.893</td> <td>   -0.177</td> <td> 0.859</td> <td>  -85.265</td> <td>   71.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.2983</td> <td>    0.460</td> <td>   -0.649</td> <td> 0.516</td> <td>   -1.200</td> <td>    0.603</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>    0.1095</td> <td>    1.577</td> <td>    0.069</td> <td> 0.945</td> <td>   -2.981</td> <td>    3.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>   -0.0046</td> <td>    0.025</td> <td>   -0.187</td> <td> 0.852</td> <td>   -0.053</td> <td>    0.044</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilproduction</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>    0.1839</td> <td>    2.409</td> <td>    0.076</td> <td> 0.939</td> <td>   -4.537</td> <td>    4.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>    0.0037</td> <td>    0.007</td> <td>    0.517</td> <td> 0.605</td> <td>   -0.010</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>    8.4673</td> <td>   20.529</td> <td>    0.412</td> <td> 0.680</td> <td>  -31.768</td> <td>   48.703</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>    1.2009</td> <td>    3.388</td> <td>    0.354</td> <td> 0.723</td> <td>   -5.440</td> <td>    7.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>    2.4914</td> <td>   86.971</td> <td>    0.029</td> <td> 0.977</td> <td> -167.969</td> <td>  172.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>    0.0102</td> <td>    0.347</td> <td>    0.029</td> <td> 0.977</td> <td>   -0.670</td> <td>    0.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -0.6087</td> <td>    0.592</td> <td>   -1.029</td> <td> 0.304</td> <td>   -1.768</td> <td>    0.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>    0.0005</td> <td>    0.020</td> <td>    0.023</td> <td> 0.982</td> <td>   -0.039</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -0.0323</td> <td>    7.515</td> <td>   -0.004</td> <td> 0.997</td> <td>  -14.760</td> <td>   14.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>    0.8493</td> <td>   13.189</td> <td>    0.064</td> <td> 0.949</td> <td>  -25.000</td> <td>   26.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>    0.0225</td> <td>    0.086</td> <td>    0.264</td> <td> 0.792</td> <td>   -0.145</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.2943</td> <td>    0.254</td> <td>   -1.158</td> <td> 0.247</td> <td>   -0.793</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>   -0.0022</td> <td>    0.007</td> <td>   -0.328</td> <td> 0.743</td> <td>   -0.016</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>   -0.2488</td> <td>    3.678</td> <td>   -0.068</td> <td> 0.946</td> <td>   -7.457</td> <td>    6.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   -0.8970</td> <td>   86.648</td> <td>   -0.010</td> <td> 0.992</td> <td> -170.725</td> <td>  168.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>   -0.0143</td> <td>    0.345</td> <td>   -0.042</td> <td> 0.967</td> <td>   -0.691</td> <td>    0.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>   -0.0370</td> <td>    0.638</td> <td>   -0.058</td> <td> 0.954</td> <td>   -1.288</td> <td>    1.214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td>   -0.0004</td> <td>    0.020</td> <td>   -0.019</td> <td> 0.985</td> <td>   -0.040</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>    0.0212</td> <td>    3.632</td> <td>    0.006</td> <td> 0.995</td> <td>   -7.098</td> <td>    7.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   -0.5754</td> <td>   34.385</td> <td>   -0.017</td> <td> 0.987</td> <td>  -67.969</td> <td>   66.818</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.0027</td> <td>    0.201</td> <td>   -0.013</td> <td> 0.989</td> <td>   -0.396</td> <td>    0.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>   -0.1505</td> <td>    0.517</td> <td>   -0.291</td> <td> 0.771</td> <td>   -1.163</td> <td>    0.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>   -0.0007</td> <td>    0.011</td> <td>   -0.063</td> <td> 0.950</td> <td>   -0.021</td> <td>    0.020</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilstorage</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>   -0.0034</td> <td>    0.034</td> <td>   -0.103</td> <td> 0.918</td> <td>   -0.069</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>   -4.5011</td> <td>   63.194</td> <td>   -0.071</td> <td> 0.943</td> <td> -128.360</td> <td>  119.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>   -0.0276</td> <td>    0.090</td> <td>   -0.307</td> <td> 0.759</td> <td>   -0.204</td> <td>    0.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>  -42.9718</td> <td>   87.189</td> <td>   -0.493</td> <td> 0.622</td> <td> -213.859</td> <td>  127.915</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>  -66.5921</td> <td>   95.503</td> <td>   -0.697</td> <td> 0.486</td> <td> -253.775</td> <td>  120.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -2.9679</td> <td>    6.435</td> <td>   -0.461</td> <td> 0.645</td> <td>  -15.581</td> <td>    9.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -3.7951</td> <td>   24.258</td> <td>   -0.156</td> <td> 0.876</td> <td>  -51.340</td> <td>   43.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>   -0.1443</td> <td>    0.361</td> <td>   -0.400</td> <td> 0.689</td> <td>   -0.852</td> <td>    0.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>  -69.2574</td> <td>   87.243</td> <td>   -0.794</td> <td> 0.427</td> <td> -240.251</td> <td>  101.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>   68.7800</td> <td>   79.749</td> <td>    0.862</td> <td> 0.388</td> <td>  -87.525</td> <td>  225.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -2.7436</td> <td>    2.275</td> <td>   -1.206</td> <td> 0.228</td> <td>   -7.202</td> <td>    1.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>    0.5415</td> <td>   12.919</td> <td>    0.042</td> <td> 0.967</td> <td>  -24.780</td> <td>   25.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>   -0.2534</td> <td>    0.272</td> <td>   -0.932</td> <td> 0.351</td> <td>   -0.786</td> <td>    0.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>  -31.4084</td> <td>  114.788</td> <td>   -0.274</td> <td> 0.784</td> <td> -256.388</td> <td>  193.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   13.4952</td> <td>   83.249</td> <td>    0.162</td> <td> 0.871</td> <td> -149.670</td> <td>  176.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>   -1.2255</td> <td>    6.072</td> <td>   -0.202</td> <td> 0.840</td> <td>  -13.127</td> <td>   10.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>    2.1629</td> <td>   23.464</td> <td>    0.092</td> <td> 0.927</td> <td>  -43.826</td> <td>   48.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td>   -0.1157</td> <td>    0.347</td> <td>   -0.333</td> <td> 0.739</td> <td>   -0.796</td> <td>    0.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>   18.8303</td> <td>  138.912</td> <td>    0.136</td> <td> 0.892</td> <td> -253.433</td> <td>  291.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   15.5177</td> <td>   76.092</td> <td>    0.204</td> <td> 0.838</td> <td> -133.621</td> <td>  164.656</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -1.0091</td> <td>    6.361</td> <td>   -0.159</td> <td> 0.874</td> <td>  -13.476</td> <td>   11.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>    0.5607</td> <td>   18.868</td> <td>    0.030</td> <td> 0.976</td> <td>  -36.420</td> <td>   37.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>   -0.1009</td> <td>    0.313</td> <td>   -0.322</td> <td> 0.747</td> <td>   -0.714</td> <td>    0.512</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Error covariance matrix</caption>\n",
       "<tr>\n",
       "                    <td></td>                       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilrig_count</th>                 <td>   15.1559</td> <td>    1.105</td> <td>   13.717</td> <td> 0.000</td> <td>   12.990</td> <td>   17.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oil_price</th>       <td>    1.2563</td> <td>    0.855</td> <td>    1.469</td> <td> 0.142</td> <td>   -0.420</td> <td>    2.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oil_price</th>                    <td>    5.2267</td> <td>    0.322</td> <td>   16.249</td> <td> 0.000</td> <td>    4.596</td> <td>    5.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oilconsumption</th>  <td>   86.1078</td> <td>  136.845</td> <td>    0.629</td> <td> 0.529</td> <td> -182.104</td> <td>  354.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oil_price.oilconsumption</th>     <td>  -21.4752</td> <td>   98.347</td> <td>   -0.218</td> <td> 0.827</td> <td> -214.231</td> <td>  171.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilconsumption</th>               <td>  641.0378</td> <td>   66.830</td> <td>    9.592</td> <td> 0.000</td> <td>  510.053</td> <td>  772.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oilproduction</th>   <td>    2.6106</td> <td>   42.583</td> <td>    0.061</td> <td> 0.951</td> <td>  -80.851</td> <td>   86.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oil_price.oilproduction</th>      <td>  -24.3730</td> <td>   27.369</td> <td>   -0.891</td> <td> 0.373</td> <td>  -78.014</td> <td>   29.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilconsumption.oilproduction</th> <td>   50.9332</td> <td>   30.156</td> <td>    1.689</td> <td> 0.091</td> <td>   -8.172</td> <td>  110.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilproduction</th>                <td>  171.8582</td> <td>   13.301</td> <td>   12.921</td> <td> 0.000</td> <td>  145.788</td> <td>  197.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oilstorage</th>      <td>-2142.1706</td> <td>   23.505</td> <td>  -91.135</td> <td> 0.000</td> <td>-2188.240</td> <td>-2096.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oil_price.oilstorage</th>         <td>-1609.2288</td> <td>    5.457</td> <td> -294.902</td> <td> 0.000</td> <td>-1619.924</td> <td>-1598.534</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilconsumption.oilstorage</th>    <td>-1856.2930</td> <td>    2.051</td> <td> -904.978</td> <td> 0.000</td> <td>-1860.313</td> <td>-1852.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilproduction.oilstorage</th>     <td> 2101.5118</td> <td>    8.268</td> <td>  254.173</td> <td> 0.000</td> <td> 2085.307</td> <td> 2117.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilstorage</th>                   <td> 9177.9318</td> <td>   19.756</td> <td>  464.570</td> <td> 0.000</td> <td> 9139.211</td> <td> 9216.652</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 1.29e+22. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                                         Statespace Model Results                                                         \n",
       "==========================================================================================================================================\n",
       "Dep. Variable:     ['oilrig_count', 'oil_price', 'oilconsumption', 'oilproduction', 'oilstorage']   No. Observations:                  284\n",
       "Model:                                                                                 VARMA(2,2)   Log Likelihood               -9057.796\n",
       "                                                                                      + intercept   AIC                          18375.592\n",
       "Date:                                                                            Sun, 21 Nov 2021   BIC                          18849.959\n",
       "Time:                                                                                    11:22:32   HQIC                         18565.776\n",
       "Sample:                                                                                03-31-1997                                         \n",
       "                                                                                     - 10-31-2020                                         \n",
       "Covariance Type:                                                                              opg                                         \n",
       "===============================================================================================================\n",
       "Ljung-Box (L1) (Q):     0.83, 0.34, 4.51, 0.50, 1.34   Jarque-Bera (JB):   1895.97, 37.73, 803.86, 953.23, 0.11\n",
       "Prob(Q):                0.36, 0.56, 0.03, 0.48, 0.25   Prob(JB):                   0.00, 0.00, 0.00, 0.00, 0.95\n",
       "Heteroskedasticity (H): 4.99, 3.84, 2.31, 2.58, 1.97   Skew:                    -1.68, -0.14, -1.08, 0.03, 0.03\n",
       "Prob(H) (two-sided):    0.00, 0.00, 0.00, 0.00, 0.00   Kurtosis:                15.21, 4.77, 10.95, 11.98, 2.92\n",
       "                           Results for equation oilrig_count                            \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "intercept                0.0144      4.203      0.003      0.997      -8.223       8.252\n",
       "trend.2               9.495e-06      0.000      0.090      0.929      -0.000       0.000\n",
       "drift                    0.1097      1.002      0.110      0.913      -1.853       2.073\n",
       "L1.oilrig_count          0.6419      0.327      1.965      0.049       0.002       1.282\n",
       "L1.oil_price             0.4176      6.186      0.068      0.946     -11.707      12.542\n",
       "L1.oilconsumption       -0.0045      0.025     -0.179      0.858      -0.053       0.045\n",
       "L1.oilproduction        -0.0033      0.043     -0.075      0.940      -0.088       0.082\n",
       "L1.oilstorage           -0.0001      0.001     -0.108      0.914      -0.003       0.002\n",
       "L2.oilrig_count         -0.4715      0.529     -0.890      0.373      -1.509       0.566\n",
       "L2.oil_price             0.2003      0.977      0.205      0.837      -1.714       2.114\n",
       "L2.oilconsumption       -0.0047      0.004     -1.208      0.227      -0.012       0.003\n",
       "L2.oilproduction        -0.0017      0.026     -0.067      0.946      -0.053       0.049\n",
       "L2.oilstorage         -2.18e-06      0.001     -0.003      0.997      -0.001       0.001\n",
       "L1.e(oilrig_count)       0.1287      0.317      0.406      0.685      -0.493       0.751\n",
       "L1.e(oil_price)          0.0785      6.217      0.013      0.990     -12.107      12.264\n",
       "L1.e(oilconsumption)     0.0006      0.024      0.026      0.979      -0.047       0.049\n",
       "L1.e(oilproduction)     -0.0027      0.043     -0.062      0.951      -0.088       0.082\n",
       "L1.e(oilstorage)      7.593e-05      0.001      0.053      0.958      -0.003       0.003\n",
       "L2.e(oilrig_count)      -0.2823      0.279     -1.011      0.312      -0.829       0.265\n",
       "L2.e(oil_price)          0.3685      2.539      0.145      0.885      -4.609       5.346\n",
       "L2.e(oilconsumption)    -0.0005      0.019     -0.027      0.978      -0.037       0.036\n",
       "L2.e(oilproduction)     -0.0002      0.035     -0.007      0.994      -0.069       0.069\n",
       "L2.e(oilstorage)     -1.131e-05      0.001     -0.013      0.990      -0.002       0.002\n",
       "                             Results for equation oil_price                             \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "drift                   -0.0055      0.073     -0.076      0.939      -0.148       0.137\n",
       "intercept               48.0843    119.999      0.401      0.689    -187.110     283.278\n",
       "trend.2                 -0.0004      0.003     -0.131      0.896      -0.007       0.006\n",
       "L1.oilrig_count         -0.0509      0.072     -0.705      0.481      -0.192       0.091\n",
       "L1.oil_price            -0.3941      2.351     -0.168      0.867      -5.001       4.213\n",
       "L1.oilconsumption       -0.0004      0.011     -0.032      0.975      -0.022       0.021\n",
       "L1.oilproduction         0.0017      0.014      0.125      0.900      -0.025       0.029\n",
       "L1.oilstorage        -3.447e-05      0.001     -0.059      0.953      -0.001       0.001\n",
       "L2.oilrig_count         -0.0505      0.196     -0.257      0.797      -0.435       0.334\n",
       "L2.oil_price            -0.1064      0.347     -0.307      0.759      -0.786       0.573\n",
       "L2.oilconsumption       -0.0005      0.002     -0.266      0.790      -0.004       0.003\n",
       "L2.oilproduction        -0.0009      0.007     -0.129      0.898      -0.015       0.013\n",
       "L2.oilstorage         6.497e-05      0.000      0.438      0.661      -0.000       0.000\n",
       "L1.e(oilrig_count)      -0.0069      0.078     -0.089      0.929      -0.159       0.145\n",
       "L1.e(oil_price)         -0.0220      2.358     -0.009      0.993      -4.645       4.601\n",
       "L1.e(oilconsumption) -7.258e-05      0.011     -0.007      0.995      -0.022       0.021\n",
       "L1.e(oilproduction)      0.0010      0.014      0.071      0.943      -0.026       0.028\n",
       "L1.e(oilstorage)      -2.36e-06      0.001     -0.004      0.997      -0.001       0.001\n",
       "L2.e(oilrig_count)       0.0140      0.097      0.145      0.885      -0.175       0.203\n",
       "L2.e(oil_price)         -0.0986      0.873     -0.113      0.910      -1.810       1.613\n",
       "L2.e(oilconsumption) -8.958e-05      0.007     -0.012      0.990      -0.014       0.014\n",
       "L2.e(oilproduction)      0.0017      0.012      0.141      0.887      -0.021       0.025\n",
       "L2.e(oilstorage)     -9.453e-06      0.000     -0.036      0.971      -0.001       0.001\n",
       "                          Results for equation oilconsumption                           \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "trend.2                2.97e-05      0.000      0.112      0.910      -0.000       0.001\n",
       "drift                   -0.9735      2.045     -0.476      0.634      -4.981       3.034\n",
       "intercept             -540.3990     10.046    -53.791      0.000    -560.089    -520.709\n",
       "L1.oilrig_count          8.6605     10.727      0.807      0.419     -12.363      29.684\n",
       "L1.oil_price            12.1982     28.333      0.431      0.667     -43.334      67.730\n",
       "L1.oilconsumption       -0.8172      0.541     -1.511      0.131      -1.878       0.243\n",
       "L1.oilproduction        -0.0145      1.995     -0.007      0.994      -3.924       3.895\n",
       "L1.oilstorage            0.0025      0.028      0.091      0.928      -0.052       0.057\n",
       "L2.oilrig_count         -5.4630     10.737     -0.509      0.611     -26.507      15.581\n",
       "L2.oil_price             3.1575     39.546      0.080      0.936     -74.351      80.666\n",
       "L2.oilconsumption       -0.3608      0.211     -1.706      0.088      -0.775       0.054\n",
       "L2.oilproduction        -0.1385      0.917     -0.151      0.880      -1.935       1.659\n",
       "L2.oilstorage            0.0039      0.021      0.183      0.855      -0.038       0.046\n",
       "L1.e(oilrig_count)      -1.8528     12.489     -0.148      0.882     -26.330      22.624\n",
       "L1.e(oil_price)         -6.8748     27.174     -0.253      0.800     -60.135      46.386\n",
       "L1.e(oilconsumption)    -0.2136      0.546     -0.391      0.696      -1.284       0.857\n",
       "L1.e(oilproduction)      0.1649      1.904      0.087      0.931      -3.566       3.896\n",
       "L1.e(oilstorage)        -0.0074      0.027     -0.269      0.788      -0.061       0.046\n",
       "L2.e(oilrig_count)      -0.0141     13.163     -0.001      0.999     -25.814      25.786\n",
       "L2.e(oil_price)         -7.0769     39.893     -0.177      0.859     -85.265      71.111\n",
       "L2.e(oilconsumption)    -0.2983      0.460     -0.649      0.516      -1.200       0.603\n",
       "L2.e(oilproduction)      0.1095      1.577      0.069      0.945      -2.981       3.200\n",
       "L2.e(oilstorage)        -0.0046      0.025     -0.187      0.852      -0.053       0.044\n",
       "                           Results for equation oilproduction                           \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "intercept                0.1839      2.409      0.076      0.939      -4.537       4.905\n",
       "trend.2                  0.0037      0.007      0.517      0.605      -0.010       0.018\n",
       "drift                    8.4673     20.529      0.412      0.680     -31.768      48.703\n",
       "L1.oilrig_count          1.2009      3.388      0.354      0.723      -5.440       7.842\n",
       "L1.oil_price             2.4914     86.971      0.029      0.977    -167.969     172.952\n",
       "L1.oilconsumption        0.0102      0.347      0.029      0.977      -0.670       0.691\n",
       "L1.oilproduction        -0.6087      0.592     -1.029      0.304      -1.768       0.551\n",
       "L1.oilstorage            0.0005      0.020      0.023      0.982      -0.039       0.040\n",
       "L2.oilrig_count         -0.0323      7.515     -0.004      0.997     -14.760      14.696\n",
       "L2.oil_price             0.8493     13.189      0.064      0.949     -25.000      26.699\n",
       "L2.oilconsumption        0.0225      0.086      0.264      0.792      -0.145       0.190\n",
       "L2.oilproduction        -0.2943      0.254     -1.158      0.247      -0.793       0.204\n",
       "L2.oilstorage           -0.0022      0.007     -0.328      0.743      -0.016       0.011\n",
       "L1.e(oilrig_count)      -0.2488      3.678     -0.068      0.946      -7.457       6.959\n",
       "L1.e(oil_price)         -0.8970     86.648     -0.010      0.992    -170.725     168.931\n",
       "L1.e(oilconsumption)    -0.0143      0.345     -0.042      0.967      -0.691       0.662\n",
       "L1.e(oilproduction)     -0.0370      0.638     -0.058      0.954      -1.288       1.214\n",
       "L1.e(oilstorage)        -0.0004      0.020     -0.019      0.985      -0.040       0.039\n",
       "L2.e(oilrig_count)       0.0212      3.632      0.006      0.995      -7.098       7.140\n",
       "L2.e(oil_price)         -0.5754     34.385     -0.017      0.987     -67.969      66.818\n",
       "L2.e(oilconsumption)    -0.0027      0.201     -0.013      0.989      -0.396       0.391\n",
       "L2.e(oilproduction)     -0.1505      0.517     -0.291      0.771      -1.163       0.862\n",
       "L2.e(oilstorage)        -0.0007      0.011     -0.063      0.950      -0.021       0.020\n",
       "                            Results for equation oilstorage                             \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "drift                   -0.0034      0.034     -0.103      0.918      -0.069       0.062\n",
       "intercept               -4.5011     63.194     -0.071      0.943    -128.360     119.358\n",
       "trend.2                 -0.0276      0.090     -0.307      0.759      -0.204       0.149\n",
       "L1.oilrig_count        -42.9718     87.189     -0.493      0.622    -213.859     127.915\n",
       "L1.oil_price           -66.5921     95.503     -0.697      0.486    -253.775     120.590\n",
       "L1.oilconsumption       -2.9679      6.435     -0.461      0.645     -15.581       9.645\n",
       "L1.oilproduction        -3.7951     24.258     -0.156      0.876     -51.340      43.749\n",
       "L1.oilstorage           -0.1443      0.361     -0.400      0.689      -0.852       0.564\n",
       "L2.oilrig_count        -69.2574     87.243     -0.794      0.427    -240.251     101.736\n",
       "L2.oil_price            68.7800     79.749      0.862      0.388     -87.525     225.084\n",
       "L2.oilconsumption       -2.7436      2.275     -1.206      0.228      -7.202       1.715\n",
       "L2.oilproduction         0.5415     12.919      0.042      0.967     -24.780      25.863\n",
       "L2.oilstorage           -0.2534      0.272     -0.932      0.351      -0.786       0.279\n",
       "L1.e(oilrig_count)     -31.4084    114.788     -0.274      0.784    -256.388     193.571\n",
       "L1.e(oil_price)         13.4952     83.249      0.162      0.871    -149.670     176.660\n",
       "L1.e(oilconsumption)    -1.2255      6.072     -0.202      0.840     -13.127      10.676\n",
       "L1.e(oilproduction)      2.1629     23.464      0.092      0.927     -43.826      48.152\n",
       "L1.e(oilstorage)        -0.1157      0.347     -0.333      0.739      -0.796       0.565\n",
       "L2.e(oilrig_count)      18.8303    138.912      0.136      0.892    -253.433     291.094\n",
       "L2.e(oil_price)         15.5177     76.092      0.204      0.838    -133.621     164.656\n",
       "L2.e(oilconsumption)    -1.0091      6.361     -0.159      0.874     -13.476      11.458\n",
       "L2.e(oilproduction)      0.5607     18.868      0.030      0.976     -36.420      37.542\n",
       "L2.e(oilstorage)        -0.1009      0.313     -0.322      0.747      -0.714       0.512\n",
       "                                         Error covariance matrix                                         \n",
       "=========================================================================================================\n",
       "                                            coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------\n",
       "sqrt.var.oilrig_count                    15.1559      1.105     13.717      0.000      12.990      17.321\n",
       "sqrt.cov.oilrig_count.oil_price           1.2563      0.855      1.469      0.142      -0.420       2.933\n",
       "sqrt.var.oil_price                        5.2267      0.322     16.249      0.000       4.596       5.857\n",
       "sqrt.cov.oilrig_count.oilconsumption     86.1078    136.845      0.629      0.529    -182.104     354.319\n",
       "sqrt.cov.oil_price.oilconsumption       -21.4752     98.347     -0.218      0.827    -214.231     171.281\n",
       "sqrt.var.oilconsumption                 641.0378     66.830      9.592      0.000     510.053     772.023\n",
       "sqrt.cov.oilrig_count.oilproduction       2.6106     42.583      0.061      0.951     -80.851      86.072\n",
       "sqrt.cov.oil_price.oilproduction        -24.3730     27.369     -0.891      0.373     -78.014      29.268\n",
       "sqrt.cov.oilconsumption.oilproduction    50.9332     30.156      1.689      0.091      -8.172     110.038\n",
       "sqrt.var.oilproduction                  171.8582     13.301     12.921      0.000     145.788     197.928\n",
       "sqrt.cov.oilrig_count.oilstorage      -2142.1706     23.505    -91.135      0.000   -2188.240   -2096.101\n",
       "sqrt.cov.oil_price.oilstorage         -1609.2288      5.457   -294.902      0.000   -1619.924   -1598.534\n",
       "sqrt.cov.oilconsumption.oilstorage    -1856.2930      2.051   -904.978      0.000   -1860.313   -1852.273\n",
       "sqrt.cov.oilproduction.oilstorage      2101.5118      8.268    254.173      0.000    2085.307    2117.717\n",
       "sqrt.var.oilstorage                    9177.9318     19.756    464.570      0.000    9139.211    9216.652\n",
       "=========================================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 1.29e+22. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_model = VARMAX(train, order=(2,2), trend='ctt')\n",
    "oil_results = oil_model.fit(maxiter=1000, disp=False)\n",
    "oil_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c29801",
   "metadata": {},
   "source": [
    "#### Generate Forecast values and plot results\n",
    "\n",
    "- Create forecasts of differenced dataframe\n",
    "- Invert dataframe transformation\n",
    "- Plot results and compare Forecasted values Vs Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9cf44d6",
   "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>oilrig_count</th>\n",
       "      <th>oil_price</th>\n",
       "      <th>oilconsumption</th>\n",
       "      <th>oilproduction</th>\n",
       "      <th>oilstorage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>-4.280394</td>\n",
       "      <td>-1.435711</td>\n",
       "      <td>-695.481553</td>\n",
       "      <td>-182.799444</td>\n",
       "      <td>-7846.901445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>-13.202206</td>\n",
       "      <td>-0.066545</td>\n",
       "      <td>-60.069108</td>\n",
       "      <td>139.041036</td>\n",
       "      <td>-2688.598486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>-2.123777</td>\n",
       "      <td>1.368484</td>\n",
       "      <td>264.122033</td>\n",
       "      <td>-56.779543</td>\n",
       "      <td>4217.805805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>4.743731</td>\n",
       "      <td>-0.386538</td>\n",
       "      <td>-66.416784</td>\n",
       "      <td>-4.966729</td>\n",
       "      <td>259.075275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>4.193703</td>\n",
       "      <td>0.064971</td>\n",
       "      <td>113.294082</td>\n",
       "      <td>12.344146</td>\n",
       "      <td>-1984.705119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>1.370701</td>\n",
       "      <td>-0.352358</td>\n",
       "      <td>15.953364</td>\n",
       "      <td>-12.571195</td>\n",
       "      <td>-932.333185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>-0.750705</td>\n",
       "      <td>-0.350336</td>\n",
       "      <td>-1.390188</td>\n",
       "      <td>1.909956</td>\n",
       "      <td>-405.332549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>-0.404127</td>\n",
       "      <td>0.093711</td>\n",
       "      <td>53.540804</td>\n",
       "      <td>-7.021171</td>\n",
       "      <td>-247.747746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
       "      <td>0.831223</td>\n",
       "      <td>-0.001021</td>\n",
       "      <td>36.703984</td>\n",
       "      <td>-5.345431</td>\n",
       "      <td>-382.825801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>1.371113</td>\n",
       "      <td>-0.082420</td>\n",
       "      <td>42.388808</td>\n",
       "      <td>-1.618196</td>\n",
       "      <td>-563.186966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>1.158376</td>\n",
       "      <td>-0.119923</td>\n",
       "      <td>40.394593</td>\n",
       "      <td>-4.302094</td>\n",
       "      <td>-604.517117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>0.738417</td>\n",
       "      <td>-0.130883</td>\n",
       "      <td>34.401728</td>\n",
       "      <td>-3.854591</td>\n",
       "      <td>-589.553427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            oilrig_count  oil_price  oilconsumption  oilproduction  \\\n",
       "2020-11-30     -4.280394  -1.435711     -695.481553    -182.799444   \n",
       "2020-12-31    -13.202206  -0.066545      -60.069108     139.041036   \n",
       "2021-01-31     -2.123777   1.368484      264.122033     -56.779543   \n",
       "2021-02-28      4.743731  -0.386538      -66.416784      -4.966729   \n",
       "2021-03-31      4.193703   0.064971      113.294082      12.344146   \n",
       "2021-04-30      1.370701  -0.352358       15.953364     -12.571195   \n",
       "2021-05-31     -0.750705  -0.350336       -1.390188       1.909956   \n",
       "2021-06-30     -0.404127   0.093711       53.540804      -7.021171   \n",
       "2021-07-31      0.831223  -0.001021       36.703984      -5.345431   \n",
       "2021-08-31      1.371113  -0.082420       42.388808      -1.618196   \n",
       "2021-09-30      1.158376  -0.119923       40.394593      -4.302094   \n",
       "2021-10-31      0.738417  -0.130883       34.401728      -3.854591   \n",
       "\n",
       "             oilstorage  \n",
       "2020-11-30 -7846.901445  \n",
       "2020-12-31 -2688.598486  \n",
       "2021-01-31  4217.805805  \n",
       "2021-02-28   259.075275  \n",
       "2021-03-31 -1984.705119  \n",
       "2021-04-30  -932.333185  \n",
       "2021-05-31  -405.332549  \n",
       "2021-06-30  -247.747746  \n",
       "2021-07-31  -382.825801  \n",
       "2021-08-31  -563.186966  \n",
       "2021-09-30  -604.517117  \n",
       "2021-10-31  -589.553427  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#12 months forecast\n",
    "dfoil_forecast = oil_results.forecast(nobs)\n",
    "dfoil_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c2e3866",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Invert dataframe transformation\n",
    "dfoil_forecast['oilrig_count1d'] = (dfoil['oilrig_count'].iloc[-nobs-1]-dfoil['oilrig_count'].iloc[-nobs-2])+dfoil_forecast['oilrig_count'].cumsum()\n",
    "dfoil_forecast['oilrig_forecast'] = dfoil['oilrig_count'].iloc[-nobs-1] + dfoil_forecast['oilrig_count1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fcb6703d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Oil Rig Count Prediction: FORECAST vs TEST Set - VARMA'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfoil['oilrig_count'].loc['2017':].plot(legend=True)\n",
    "dfoil_forecast['oilrig_forecast'].plot(legend=True,linewidth=4,title='Oil Rig Count Prediction: FORECAST vs TEST Set - VARMA')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88c1896f",
   "metadata": {},
   "source": [
    "#### Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f28eb28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oil Rig VARMA(2,2) RMSE: 35.022\n",
      "Oil Rig VARMA(2,2) MSE: 1226.545\n",
      "Oil Rig VARMA(2,2) MAE: 31.681\n",
      "\n",
      "Oil Rig Mean Value: 345.055\n",
      "Oil Rig std Value: 54.791\n",
      "Oil Rig Percent Change: 10.150%\n"
     ]
    }
   ],
   "source": [
    "RMSE1 = rmse(dfoil['oilrig_count'].loc[test.index.min():],dfoil_forecast['oilrig_forecast'])\n",
    "MSE1 = mse(dfoil['oilrig_count'].loc[test.index.min():],dfoil_forecast['oilrig_forecast'])\n",
    "MAE1 = meanabs(dfoil['oilrig_count'].loc[test.index.min():],dfoil_forecast['oilrig_forecast'])\n",
    "\n",
    "print(f\"\"\"Oil Rig VARMA(2,2) RMSE: {RMSE1:.3f}\n",
    "Oil Rig VARMA(2,2) MSE: {MSE1:.3f}\n",
    "Oil Rig VARMA(2,2) MAE: {MAE1:.3f}\\n\n",
    "Oil Rig Mean Value: {dfoil['oilrig_count'][-nobs:].mean():.3f}\n",
    "Oil Rig std Value: {dfoil['oilrig_count'][-nobs:].std():.3f}\n",
    "Oil Rig Percent Change: {RMSE1/dfoil['oilrig_count'][-nobs:].mean()*100:.3f}%\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "279373b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 176 TEST: 12\n",
      "TRAIN: 188 TEST: 12\n",
      "TRAIN: 200 TEST: 12\n",
      "TRAIN: 212 TEST: 12\n",
      "TRAIN: 224 TEST: 12\n",
      "TRAIN: 236 TEST: 12\n",
      "TRAIN: 248 TEST: 12\n",
      "TRAIN: 260 TEST: 12\n",
      "TRAIN: 272 TEST: 12\n",
      "TRAIN: 284 TEST: 12\n",
      "\n",
      "10-fold Cross Validation Oil Rig\n",
      "RMSE: 543.836,\n",
      "MSE: 437414.838\n",
      "MAE: 521.412\n",
      "\n",
      "Oil Rig Mean Value: 345.055\n",
      "Oil Rig std Value: 54.791\n",
      "Oil Rig Percent Change: 157.608%\n"
     ]
    }
   ],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=10, test_size=12)\n",
    "RMSE_oil = []\n",
    "MSE_oil = []\n",
    "MAE_oil = []\n",
    "for train_index, test_index in tscv.split(dfoil_transformed):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\", len(test_index))\n",
    "    train,test = dfoil_transformed.loc[dfoil_transformed.index[train_index],:], dfoil_transformed.loc[dfoil_transformed.index[test_index],:]\n",
    "    oil_model = VARMAX(train, order=(2,2), trend='ctt')\n",
    "    oil_results = oil_model.fit(maxiter=1000, disp=False)\n",
    "    dfoil_forecast = oil_results.forecast(nobs)\n",
    "    dfoil_forecast['oilrig_count1d'] = (dfoil['oilrig_count'].iloc[-nobs-1]-dfoil['oilrig_count'].iloc[-nobs-2])+dfoil_forecast['oilrig_count'].cumsum()\n",
    "    dfoil_forecast['oilrig_forecast'] = dfoil['oilrig_count'].iloc[-nobs-1] + dfoil_forecast['oilrig_count1d'].cumsum()\n",
    "    RMSE_oil.append('{:.3f}'.format(rmse(dfoil.loc[dfoil.index[test_index],'oilrig_count'],dfoil_forecast['oilrig_forecast'])))\n",
    "    MSE_oil.append('{:.3f}'.format(mse(dfoil.loc[dfoil.index[test_index],'oilrig_count'],dfoil_forecast['oilrig_forecast'])))\n",
    "    MAE_oil.append('{:.3f}'.format(meanabs(dfoil.loc[dfoil.index[test_index],'oilrig_count'],dfoil_forecast['oilrig_forecast'])))\n",
    "\n",
    "RMSE_oil = np.array(RMSE_oil).astype('float')\n",
    "MSE_oil = np.array(MSE_oil).astype('float')\n",
    "MAE_oil = np.array(MAE_oil).astype('float')\n",
    "print(f\"\"\"\\n10-fold Cross Validation Oil Rig\n",
    "RMSE: {RMSE_oil.mean():.3f},\n",
    "MSE: {MSE_oil.mean():.3f}\n",
    "MAE: {MAE_oil.mean():.3f}\\n\n",
    "Oil Rig Mean Value: {dfoil['oilrig_count'][-nobs:].mean():.3f}\n",
    "Oil Rig std Value: {dfoil['oilrig_count'][-nobs:].std():.3f}\n",
    "Oil Rig Percent Change: {RMSE_oil.mean()/dfoil['oilrig_count'][-nobs:].mean()*100:.3f}%\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aff6f8f",
   "metadata": {},
   "source": [
    "#### Fit Model with 100% of Data and forecast Real Future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa63eb88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['oilrig_count', 'oil_price', 'oilconsumption', 'oilproduction', 'oilstorage']</td> <th>  No. Observations:  </th>    <td>296</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                             <td>VARMA(2,2)</td>                                   <th>  Log Likelihood     </th> <td>-9482.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                   <td>+ intercept</td>                                  <th>  AIC                </th> <td>19225.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                                           <td>Sun, 21 Nov 2021</td>                                <th>  BIC                </th> <td>19704.975</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                               <td>11:39:43</td>                                    <th>  HQIC               </th> <td>19417.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                                            <td>03-31-1997</td>                                   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                                                  <td>- 10-31-2021</td>                                  <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                                       <td>opg</td>                                      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.91, 0.40, 5.88, 0.55, 1.44</td> <th>  Jarque-Bera (JB):  </th> <td>2058.68, 37.70, 697.26, 559.51, 0.60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.34, 0.53, 0.02, 0.46, 0.23</td> <th>  Prob(JB):          </th>     <td>0.00, 0.00, 0.00, 0.00, 0.74</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>4.76, 3.45, 2.35, 5.24, 2.24</td> <th>  Skew:              </th>    <td>-1.65, -0.18, -0.94, 0.07, 0.11</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00, 0.00, 0.00, 0.00, 0.00</td> <th>  Kurtosis:          </th>    <td>15.49, 4.71, 10.28, 9.73, 2.93</td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilrig_count</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>   -0.3443</td> <td>    4.179</td> <td>   -0.082</td> <td> 0.934</td> <td>   -8.535</td> <td>    7.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td> 2.105e-05</td> <td>    0.000</td> <td>    0.202</td> <td> 0.840</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>   -0.3432</td> <td>    0.996</td> <td>   -0.345</td> <td> 0.730</td> <td>   -2.295</td> <td>    1.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>    0.6406</td> <td>    0.295</td> <td>    2.175</td> <td> 0.030</td> <td>    0.063</td> <td>    1.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>    0.4255</td> <td>    6.644</td> <td>    0.064</td> <td> 0.949</td> <td>  -12.597</td> <td>   13.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -0.0043</td> <td>    0.027</td> <td>   -0.156</td> <td> 0.876</td> <td>   -0.058</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -0.0023</td> <td>    0.044</td> <td>   -0.052</td> <td> 0.958</td> <td>   -0.089</td> <td>    0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>   -0.0001</td> <td>    0.002</td> <td>   -0.080</td> <td> 0.936</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -0.4718</td> <td>    0.541</td> <td>   -0.872</td> <td> 0.383</td> <td>   -1.533</td> <td>    0.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>    0.1921</td> <td>    1.042</td> <td>    0.184</td> <td> 0.854</td> <td>   -1.850</td> <td>    2.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -0.0043</td> <td>    0.005</td> <td>   -0.930</td> <td> 0.352</td> <td>   -0.013</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.0019</td> <td>    0.035</td> <td>   -0.055</td> <td> 0.956</td> <td>   -0.070</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>-2.003e-05</td> <td>    0.001</td> <td>   -0.031</td> <td> 0.976</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>    0.1280</td> <td>    0.280</td> <td>    0.458</td> <td> 0.647</td> <td>   -0.420</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>    0.0740</td> <td>    6.643</td> <td>    0.011</td> <td> 0.991</td> <td>  -12.946</td> <td>   13.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>    0.0006</td> <td>    0.027</td> <td>    0.023</td> <td> 0.981</td> <td>   -0.052</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>   -0.0026</td> <td>    0.043</td> <td>   -0.060</td> <td> 0.952</td> <td>   -0.086</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td> 6.048e-05</td> <td>    0.002</td> <td>    0.036</td> <td> 0.971</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>   -0.2819</td> <td>    0.253</td> <td>   -1.112</td> <td> 0.266</td> <td>   -0.779</td> <td>    0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>    0.3586</td> <td>    2.621</td> <td>    0.137</td> <td> 0.891</td> <td>   -4.778</td> <td>    5.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.0006</td> <td>    0.018</td> <td>   -0.030</td> <td> 0.976</td> <td>   -0.036</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>   -0.0001</td> <td>    0.032</td> <td>   -0.004</td> <td> 0.997</td> <td>   -0.062</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>-1.128e-05</td> <td>    0.001</td> <td>   -0.012</td> <td> 0.991</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oil_price</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>    0.0042</td> <td>    0.074</td> <td>    0.057</td> <td> 0.955</td> <td>   -0.141</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>   26.5147</td> <td>  113.372</td> <td>    0.234</td> <td> 0.815</td> <td> -195.690</td> <td>  248.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>    0.0015</td> <td>    0.003</td> <td>    0.477</td> <td> 0.633</td> <td>   -0.005</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>   -0.0514</td> <td>    0.077</td> <td>   -0.666</td> <td> 0.505</td> <td>   -0.203</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>   -0.3895</td> <td>    2.445</td> <td>   -0.159</td> <td> 0.873</td> <td>   -5.181</td> <td>    4.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -0.0003</td> <td>    0.011</td> <td>   -0.031</td> <td> 0.975</td> <td>   -0.022</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>    0.0006</td> <td>    0.014</td> <td>    0.044</td> <td> 0.965</td> <td>   -0.027</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>-3.558e-05</td> <td>    0.001</td> <td>   -0.057</td> <td> 0.955</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -0.0460</td> <td>    0.188</td> <td>   -0.244</td> <td> 0.807</td> <td>   -0.415</td> <td>    0.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>   -0.1152</td> <td>    0.357</td> <td>   -0.323</td> <td> 0.747</td> <td>   -0.814</td> <td>    0.584</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -0.0005</td> <td>    0.002</td> <td>   -0.230</td> <td> 0.818</td> <td>   -0.005</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.0014</td> <td>    0.010</td> <td>   -0.143</td> <td> 0.886</td> <td>   -0.021</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td> 7.022e-05</td> <td>    0.000</td> <td>    0.470</td> <td> 0.639</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>   -0.0061</td> <td>    0.079</td> <td>   -0.078</td> <td> 0.938</td> <td>   -0.160</td> <td>    0.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   -0.0255</td> <td>    2.452</td> <td>   -0.010</td> <td> 0.992</td> <td>   -4.831</td> <td>    4.780</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>-5.768e-05</td> <td>    0.011</td> <td>   -0.005</td> <td> 0.996</td> <td>   -0.021</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>    0.0010</td> <td>    0.014</td> <td>    0.069</td> <td> 0.945</td> <td>   -0.027</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td> 1.025e-06</td> <td>    0.001</td> <td>    0.002</td> <td> 0.999</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>    0.0159</td> <td>    0.097</td> <td>    0.163</td> <td> 0.870</td> <td>   -0.175</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   -0.0930</td> <td>    0.883</td> <td>   -0.105</td> <td> 0.916</td> <td>   -1.823</td> <td>    1.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.0001</td> <td>    0.007</td> <td>   -0.016</td> <td> 0.987</td> <td>   -0.014</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>    0.0021</td> <td>    0.010</td> <td>    0.216</td> <td> 0.829</td> <td>   -0.017</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td> -6.18e-06</td> <td>    0.000</td> <td>   -0.022</td> <td> 0.982</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilconsumption</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>-1.135e-05</td> <td>    0.000</td> <td>   -0.042</td> <td> 0.967</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>   -0.3921</td> <td>    1.873</td> <td>   -0.209</td> <td> 0.834</td> <td>   -4.063</td> <td>    3.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td> -303.2647</td> <td>    4.069</td> <td>  -74.524</td> <td> 0.000</td> <td> -311.241</td> <td> -295.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>    8.5622</td> <td>   10.035</td> <td>    0.853</td> <td> 0.394</td> <td>  -11.106</td> <td>   28.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>   13.1172</td> <td>   17.239</td> <td>    0.761</td> <td> 0.447</td> <td>  -20.671</td> <td>   46.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -0.8169</td> <td>    0.482</td> <td>   -1.694</td> <td> 0.090</td> <td>   -1.762</td> <td>    0.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>    0.0337</td> <td>    1.895</td> <td>    0.018</td> <td> 0.986</td> <td>   -3.680</td> <td>    3.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>    0.0014</td> <td>    0.028</td> <td>    0.048</td> <td> 0.961</td> <td>   -0.053</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>   -5.7011</td> <td>   10.470</td> <td>   -0.544</td> <td> 0.586</td> <td>  -26.223</td> <td>   14.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>    3.2571</td> <td>   41.677</td> <td>    0.078</td> <td> 0.938</td> <td>  -78.428</td> <td>   84.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -0.3554</td> <td>    0.190</td> <td>   -1.874</td> <td> 0.061</td> <td>   -0.727</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.1839</td> <td>    0.782</td> <td>   -0.235</td> <td> 0.814</td> <td>   -1.717</td> <td>    1.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>    0.0022</td> <td>    0.020</td> <td>    0.107</td> <td> 0.915</td> <td>   -0.038</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>   -1.9086</td> <td>   10.979</td> <td>   -0.174</td> <td> 0.862</td> <td>  -23.427</td> <td>   19.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   -6.8571</td> <td>   17.748</td> <td>   -0.386</td> <td> 0.699</td> <td>  -41.642</td> <td>   27.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>   -0.2228</td> <td>    0.482</td> <td>   -0.462</td> <td> 0.644</td> <td>   -1.168</td> <td>    0.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>    0.2149</td> <td>    1.857</td> <td>    0.116</td> <td> 0.908</td> <td>   -3.425</td> <td>    3.854</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td>   -0.0068</td> <td>    0.027</td> <td>   -0.249</td> <td> 0.803</td> <td>   -0.060</td> <td>    0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>    0.2817</td> <td>   12.192</td> <td>    0.023</td> <td> 0.982</td> <td>  -23.614</td> <td>   24.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   -8.1972</td> <td>   38.787</td> <td>   -0.211</td> <td> 0.833</td> <td>  -84.219</td> <td>   67.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.2976</td> <td>    0.415</td> <td>   -0.718</td> <td> 0.473</td> <td>   -1.110</td> <td>    0.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>    0.0625</td> <td>    1.442</td> <td>    0.043</td> <td> 0.965</td> <td>   -2.764</td> <td>    2.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>   -0.0041</td> <td>    0.023</td> <td>   -0.183</td> <td> 0.855</td> <td>   -0.049</td> <td>    0.040</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilproduction</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>    0.2699</td> <td>    2.269</td> <td>    0.119</td> <td> 0.905</td> <td>   -4.177</td> <td>    4.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>    0.0012</td> <td>    0.006</td> <td>    0.186</td> <td> 0.853</td> <td>   -0.011</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>    1.8950</td> <td>   20.110</td> <td>    0.094</td> <td> 0.925</td> <td>  -37.519</td> <td>   41.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>    1.1280</td> <td>    3.999</td> <td>    0.282</td> <td> 0.778</td> <td>   -6.710</td> <td>    8.966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>    3.9030</td> <td>   85.897</td> <td>    0.045</td> <td> 0.964</td> <td> -164.451</td> <td>  172.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>    0.0170</td> <td>    0.387</td> <td>    0.044</td> <td> 0.965</td> <td>   -0.742</td> <td>    0.776</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -0.6923</td> <td>    0.505</td> <td>   -1.370</td> <td> 0.171</td> <td>   -1.683</td> <td>    0.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>    0.0008</td> <td>    0.022</td> <td>    0.038</td> <td> 0.969</td> <td>   -0.041</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>    0.1720</td> <td>    7.878</td> <td>    0.022</td> <td> 0.983</td> <td>  -15.268</td> <td>   15.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>    0.8904</td> <td>   15.923</td> <td>    0.056</td> <td> 0.955</td> <td>  -30.319</td> <td>   32.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>    0.0299</td> <td>    0.113</td> <td>    0.265</td> <td> 0.791</td> <td>   -0.192</td> <td>    0.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -0.3131</td> <td>    0.421</td> <td>   -0.743</td> <td> 0.457</td> <td>   -1.139</td> <td>    0.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>   -0.0022</td> <td>    0.007</td> <td>   -0.310</td> <td> 0.756</td> <td>   -0.016</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>   -0.2724</td> <td>    4.234</td> <td>   -0.064</td> <td> 0.949</td> <td>   -8.571</td> <td>    8.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   -1.1196</td> <td>   85.620</td> <td>   -0.013</td> <td> 0.990</td> <td> -168.932</td> <td>  166.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>   -0.0189</td> <td>    0.393</td> <td>   -0.048</td> <td> 0.962</td> <td>   -0.789</td> <td>    0.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>   -0.0501</td> <td>    0.509</td> <td>   -0.098</td> <td> 0.922</td> <td>   -1.049</td> <td>    0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td>   -0.0006</td> <td>    0.021</td> <td>   -0.028</td> <td> 0.978</td> <td>   -0.042</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>    0.1023</td> <td>    3.970</td> <td>    0.026</td> <td> 0.979</td> <td>   -7.678</td> <td>    7.883</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   -1.2893</td> <td>   30.131</td> <td>   -0.043</td> <td> 0.966</td> <td>  -60.346</td> <td>   57.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -0.0104</td> <td>    0.206</td> <td>   -0.050</td> <td> 0.960</td> <td>   -0.413</td> <td>    0.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>   -0.1676</td> <td>    0.399</td> <td>   -0.420</td> <td> 0.674</td> <td>   -0.949</td> <td>    0.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>   -0.0011</td> <td>    0.011</td> <td>   -0.104</td> <td> 0.917</td> <td>   -0.023</td> <td>    0.021</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation oilstorage</caption>\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>drift</th>                <td>   -0.0057</td> <td>    0.032</td> <td>   -0.179</td> <td> 0.858</td> <td>   -0.068</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>            <td>   12.2887</td> <td>   66.597</td> <td>    0.185</td> <td> 0.854</td> <td> -118.240</td> <td>  142.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend.2</th>              <td>    0.0004</td> <td>    0.087</td> <td>    0.004</td> <td> 0.997</td> <td>   -0.171</td> <td>    0.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilrig_count</th>      <td>  -51.2926</td> <td>  105.172</td> <td>   -0.488</td> <td> 0.626</td> <td> -257.426</td> <td>  154.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oil_price</th>         <td>  -59.1401</td> <td>   81.734</td> <td>   -0.724</td> <td> 0.469</td> <td> -219.336</td> <td>  101.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilconsumption</th>    <td>   -2.0837</td> <td>    6.256</td> <td>   -0.333</td> <td> 0.739</td> <td>  -14.344</td> <td>   10.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilproduction</th>     <td>   -8.2646</td> <td>   19.927</td> <td>   -0.415</td> <td> 0.678</td> <td>  -47.322</td> <td>   30.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.oilstorage</th>        <td>   -0.1309</td> <td>    0.347</td> <td>   -0.378</td> <td> 0.706</td> <td>   -0.810</td> <td>    0.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilrig_count</th>      <td>  -60.5039</td> <td>   90.799</td> <td>   -0.666</td> <td> 0.505</td> <td> -238.468</td> <td>  117.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oil_price</th>         <td>   68.9280</td> <td>   77.093</td> <td>    0.894</td> <td> 0.371</td> <td>  -82.172</td> <td>  220.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilconsumption</th>    <td>   -1.9875</td> <td>    2.225</td> <td>   -0.893</td> <td> 0.372</td> <td>   -6.349</td> <td>    2.374</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilproduction</th>     <td>   -1.0526</td> <td>   10.683</td> <td>   -0.099</td> <td> 0.922</td> <td>  -21.991</td> <td>   19.886</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.oilstorage</th>        <td>   -0.2606</td> <td>    0.284</td> <td>   -0.918</td> <td> 0.358</td> <td>   -0.817</td> <td>    0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilrig_count)</th>   <td>  -33.6323</td> <td>  116.375</td> <td>   -0.289</td> <td> 0.773</td> <td> -261.723</td> <td>  194.459</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oil_price)</th>      <td>   26.6196</td> <td>   66.630</td> <td>    0.400</td> <td> 0.690</td> <td> -103.973</td> <td>  157.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilconsumption)</th> <td>   -1.4085</td> <td>    6.027</td> <td>   -0.234</td> <td> 0.815</td> <td>  -13.221</td> <td>   10.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilproduction)</th>  <td>    2.0038</td> <td>   17.423</td> <td>    0.115</td> <td> 0.908</td> <td>  -32.146</td> <td>   36.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(oilstorage)</th>     <td>   -0.1135</td> <td>    0.332</td> <td>   -0.342</td> <td> 0.732</td> <td>   -0.763</td> <td>    0.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilrig_count)</th>   <td>   18.7789</td> <td>  132.632</td> <td>    0.142</td> <td> 0.887</td> <td> -241.175</td> <td>  278.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oil_price)</th>      <td>   28.3586</td> <td>  120.460</td> <td>    0.235</td> <td> 0.814</td> <td> -207.739</td> <td>  264.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilconsumption)</th> <td>   -1.3544</td> <td>    5.982</td> <td>   -0.226</td> <td> 0.821</td> <td>  -13.079</td> <td>   10.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilproduction)</th>  <td>   -0.2327</td> <td>   15.613</td> <td>   -0.015</td> <td> 0.988</td> <td>  -30.833</td> <td>   30.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.e(oilstorage)</th>     <td>   -0.1117</td> <td>    0.323</td> <td>   -0.346</td> <td> 0.730</td> <td>   -0.745</td> <td>    0.522</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Error covariance matrix</caption>\n",
       "<tr>\n",
       "                    <td></td>                       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilrig_count</th>                 <td>   14.8966</td> <td>    1.050</td> <td>   14.190</td> <td> 0.000</td> <td>   12.839</td> <td>   16.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oil_price</th>       <td>    1.2061</td> <td>    0.774</td> <td>    1.558</td> <td> 0.119</td> <td>   -0.311</td> <td>    2.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oil_price</th>                    <td>    5.2094</td> <td>    0.281</td> <td>   18.545</td> <td> 0.000</td> <td>    4.659</td> <td>    5.760</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oilconsumption</th>  <td>   84.2875</td> <td>  121.479</td> <td>    0.694</td> <td> 0.488</td> <td> -153.808</td> <td>  322.383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oil_price.oilconsumption</th>     <td>  -29.3903</td> <td>   89.720</td> <td>   -0.328</td> <td> 0.743</td> <td> -205.238</td> <td>  146.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilconsumption</th>               <td>  639.2858</td> <td>   64.487</td> <td>    9.913</td> <td> 0.000</td> <td>  512.895</td> <td>  765.677</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oilproduction</th>   <td>    4.2119</td> <td>   43.290</td> <td>    0.097</td> <td> 0.922</td> <td>  -80.635</td> <td>   89.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oil_price.oilproduction</th>      <td>  -22.5232</td> <td>   27.884</td> <td>   -0.808</td> <td> 0.419</td> <td>  -77.175</td> <td>   32.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilconsumption.oilproduction</th> <td>   51.7839</td> <td>   32.138</td> <td>    1.611</td> <td> 0.107</td> <td>  -11.205</td> <td>  114.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilproduction</th>                <td>  197.5986</td> <td>   13.119</td> <td>   15.062</td> <td> 0.000</td> <td>  171.886</td> <td>  223.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilrig_count.oilstorage</th>      <td>-1973.9053</td> <td>   36.212</td> <td>  -54.509</td> <td> 0.000</td> <td>-2044.880</td> <td>-1902.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oil_price.oilstorage</th>         <td>-1494.4142</td> <td>    3.511</td> <td> -425.678</td> <td> 0.000</td> <td>-1501.295</td> <td>-1487.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilconsumption.oilstorage</th>    <td>-1883.8979</td> <td>    6.152</td> <td> -306.241</td> <td> 0.000</td> <td>-1895.955</td> <td>-1871.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.oilproduction.oilstorage</th>     <td> 2846.3159</td> <td>    3.535</td> <td>  805.275</td> <td> 0.000</td> <td> 2839.388</td> <td> 2853.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.oilstorage</th>                   <td> 9496.5814</td> <td>    3.828</td> <td> 2481.097</td> <td> 0.000</td> <td> 9489.079</td> <td> 9504.083</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 3.21e+19. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                                         Statespace Model Results                                                         \n",
       "==========================================================================================================================================\n",
       "Dep. Variable:     ['oilrig_count', 'oil_price', 'oilconsumption', 'oilproduction', 'oilstorage']   No. Observations:                  296\n",
       "Model:                                                                                 VARMA(2,2)   Log Likelihood               -9482.614\n",
       "                                                                                      + intercept   AIC                          19225.228\n",
       "Date:                                                                            Sun, 21 Nov 2021   BIC                          19704.975\n",
       "Time:                                                                                    11:39:43   HQIC                         19417.309\n",
       "Sample:                                                                                03-31-1997                                         \n",
       "                                                                                     - 10-31-2021                                         \n",
       "Covariance Type:                                                                              opg                                         \n",
       "===============================================================================================================\n",
       "Ljung-Box (L1) (Q):     0.91, 0.40, 5.88, 0.55, 1.44   Jarque-Bera (JB):   2058.68, 37.70, 697.26, 559.51, 0.60\n",
       "Prob(Q):                0.34, 0.53, 0.02, 0.46, 0.23   Prob(JB):                   0.00, 0.00, 0.00, 0.00, 0.74\n",
       "Heteroskedasticity (H): 4.76, 3.45, 2.35, 5.24, 2.24   Skew:                    -1.65, -0.18, -0.94, 0.07, 0.11\n",
       "Prob(H) (two-sided):    0.00, 0.00, 0.00, 0.00, 0.00   Kurtosis:                 15.49, 4.71, 10.28, 9.73, 2.93\n",
       "                           Results for equation oilrig_count                            \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "intercept               -0.3443      4.179     -0.082      0.934      -8.535       7.846\n",
       "trend.2               2.105e-05      0.000      0.202      0.840      -0.000       0.000\n",
       "drift                   -0.3432      0.996     -0.345      0.730      -2.295       1.609\n",
       "L1.oilrig_count          0.6406      0.295      2.175      0.030       0.063       1.218\n",
       "L1.oil_price             0.4255      6.644      0.064      0.949     -12.597      13.448\n",
       "L1.oilconsumption       -0.0043      0.027     -0.156      0.876      -0.058       0.049\n",
       "L1.oilproduction        -0.0023      0.044     -0.052      0.958      -0.089       0.084\n",
       "L1.oilstorage           -0.0001      0.002     -0.080      0.936      -0.003       0.003\n",
       "L2.oilrig_count         -0.4718      0.541     -0.872      0.383      -1.533       0.589\n",
       "L2.oil_price             0.1921      1.042      0.184      0.854      -1.850       2.234\n",
       "L2.oilconsumption       -0.0043      0.005     -0.930      0.352      -0.013       0.005\n",
       "L2.oilproduction        -0.0019      0.035     -0.055      0.956      -0.070       0.066\n",
       "L2.oilstorage        -2.003e-05      0.001     -0.031      0.976      -0.001       0.001\n",
       "L1.e(oilrig_count)       0.1280      0.280      0.458      0.647      -0.420       0.676\n",
       "L1.e(oil_price)          0.0740      6.643      0.011      0.991     -12.946      13.094\n",
       "L1.e(oilconsumption)     0.0006      0.027      0.023      0.981      -0.052       0.053\n",
       "L1.e(oilproduction)     -0.0026      0.043     -0.060      0.952      -0.086       0.081\n",
       "L1.e(oilstorage)      6.048e-05      0.002      0.036      0.971      -0.003       0.003\n",
       "L2.e(oilrig_count)      -0.2819      0.253     -1.112      0.266      -0.779       0.215\n",
       "L2.e(oil_price)          0.3586      2.621      0.137      0.891      -4.778       5.496\n",
       "L2.e(oilconsumption)    -0.0006      0.018     -0.030      0.976      -0.036       0.035\n",
       "L2.e(oilproduction)     -0.0001      0.032     -0.004      0.997      -0.062       0.062\n",
       "L2.e(oilstorage)     -1.128e-05      0.001     -0.012      0.991      -0.002       0.002\n",
       "                             Results for equation oil_price                             \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "drift                    0.0042      0.074      0.057      0.955      -0.141       0.150\n",
       "intercept               26.5147    113.372      0.234      0.815    -195.690     248.720\n",
       "trend.2                  0.0015      0.003      0.477      0.633      -0.005       0.008\n",
       "L1.oilrig_count         -0.0514      0.077     -0.666      0.505      -0.203       0.100\n",
       "L1.oil_price            -0.3895      2.445     -0.159      0.873      -5.181       4.402\n",
       "L1.oilconsumption       -0.0003      0.011     -0.031      0.975      -0.022       0.021\n",
       "L1.oilproduction         0.0006      0.014      0.044      0.965      -0.027       0.028\n",
       "L1.oilstorage        -3.558e-05      0.001     -0.057      0.955      -0.001       0.001\n",
       "L2.oilrig_count         -0.0460      0.188     -0.244      0.807      -0.415       0.323\n",
       "L2.oil_price            -0.1152      0.357     -0.323      0.747      -0.814       0.584\n",
       "L2.oilconsumption       -0.0005      0.002     -0.230      0.818      -0.005       0.004\n",
       "L2.oilproduction        -0.0014      0.010     -0.143      0.886      -0.021       0.018\n",
       "L2.oilstorage         7.022e-05      0.000      0.470      0.639      -0.000       0.000\n",
       "L1.e(oilrig_count)      -0.0061      0.079     -0.078      0.938      -0.160       0.148\n",
       "L1.e(oil_price)         -0.0255      2.452     -0.010      0.992      -4.831       4.780\n",
       "L1.e(oilconsumption) -5.768e-05      0.011     -0.005      0.996      -0.021       0.021\n",
       "L1.e(oilproduction)      0.0010      0.014      0.069      0.945      -0.027       0.029\n",
       "L1.e(oilstorage)      1.025e-06      0.001      0.002      0.999      -0.001       0.001\n",
       "L2.e(oilrig_count)       0.0159      0.097      0.163      0.870      -0.175       0.207\n",
       "L2.e(oil_price)         -0.0930      0.883     -0.105      0.916      -1.823       1.637\n",
       "L2.e(oilconsumption)    -0.0001      0.007     -0.016      0.987      -0.014       0.013\n",
       "L2.e(oilproduction)      0.0021      0.010      0.216      0.829      -0.017       0.022\n",
       "L2.e(oilstorage)      -6.18e-06      0.000     -0.022      0.982      -0.001       0.001\n",
       "                          Results for equation oilconsumption                           \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "trend.2              -1.135e-05      0.000     -0.042      0.967      -0.001       0.001\n",
       "drift                   -0.3921      1.873     -0.209      0.834      -4.063       3.279\n",
       "intercept             -303.2647      4.069    -74.524      0.000    -311.241    -295.289\n",
       "L1.oilrig_count          8.5622     10.035      0.853      0.394     -11.106      28.231\n",
       "L1.oil_price            13.1172     17.239      0.761      0.447     -20.671      46.906\n",
       "L1.oilconsumption       -0.8169      0.482     -1.694      0.090      -1.762       0.128\n",
       "L1.oilproduction         0.0337      1.895      0.018      0.986      -3.680       3.748\n",
       "L1.oilstorage            0.0014      0.028      0.048      0.961      -0.053       0.056\n",
       "L2.oilrig_count         -5.7011     10.470     -0.544      0.586     -26.223      14.821\n",
       "L2.oil_price             3.2571     41.677      0.078      0.938     -78.428      84.942\n",
       "L2.oilconsumption       -0.3554      0.190     -1.874      0.061      -0.727       0.016\n",
       "L2.oilproduction        -0.1839      0.782     -0.235      0.814      -1.717       1.349\n",
       "L2.oilstorage            0.0022      0.020      0.107      0.915      -0.038       0.042\n",
       "L1.e(oilrig_count)      -1.9086     10.979     -0.174      0.862     -23.427      19.610\n",
       "L1.e(oil_price)         -6.8571     17.748     -0.386      0.699     -41.642      27.928\n",
       "L1.e(oilconsumption)    -0.2228      0.482     -0.462      0.644      -1.168       0.723\n",
       "L1.e(oilproduction)      0.2149      1.857      0.116      0.908      -3.425       3.854\n",
       "L1.e(oilstorage)        -0.0068      0.027     -0.249      0.803      -0.060       0.047\n",
       "L2.e(oilrig_count)       0.2817     12.192      0.023      0.982     -23.614      24.177\n",
       "L2.e(oil_price)         -8.1972     38.787     -0.211      0.833     -84.219      67.824\n",
       "L2.e(oilconsumption)    -0.2976      0.415     -0.718      0.473      -1.110       0.515\n",
       "L2.e(oilproduction)      0.0625      1.442      0.043      0.965      -2.764       2.889\n",
       "L2.e(oilstorage)        -0.0041      0.023     -0.183      0.855      -0.049       0.040\n",
       "                           Results for equation oilproduction                           \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "intercept                0.2699      2.269      0.119      0.905      -4.177       4.717\n",
       "trend.2                  0.0012      0.006      0.186      0.853      -0.011       0.014\n",
       "drift                    1.8950     20.110      0.094      0.925     -37.519      41.309\n",
       "L1.oilrig_count          1.1280      3.999      0.282      0.778      -6.710       8.966\n",
       "L1.oil_price             3.9030     85.897      0.045      0.964    -164.451     172.257\n",
       "L1.oilconsumption        0.0170      0.387      0.044      0.965      -0.742       0.776\n",
       "L1.oilproduction        -0.6923      0.505     -1.370      0.171      -1.683       0.298\n",
       "L1.oilstorage            0.0008      0.022      0.038      0.969      -0.041       0.043\n",
       "L2.oilrig_count          0.1720      7.878      0.022      0.983     -15.268      15.612\n",
       "L2.oil_price             0.8904     15.923      0.056      0.955     -30.319      32.100\n",
       "L2.oilconsumption        0.0299      0.113      0.265      0.791      -0.192       0.252\n",
       "L2.oilproduction        -0.3131      0.421     -0.743      0.457      -1.139       0.512\n",
       "L2.oilstorage           -0.0022      0.007     -0.310      0.756      -0.016       0.011\n",
       "L1.e(oilrig_count)      -0.2724      4.234     -0.064      0.949      -8.571       8.027\n",
       "L1.e(oil_price)         -1.1196     85.620     -0.013      0.990    -168.932     166.693\n",
       "L1.e(oilconsumption)    -0.0189      0.393     -0.048      0.962      -0.789       0.751\n",
       "L1.e(oilproduction)     -0.0501      0.509     -0.098      0.922      -1.049       0.948\n",
       "L1.e(oilstorage)        -0.0006      0.021     -0.028      0.978      -0.042       0.040\n",
       "L2.e(oilrig_count)       0.1023      3.970      0.026      0.979      -7.678       7.883\n",
       "L2.e(oil_price)         -1.2893     30.131     -0.043      0.966     -60.346      57.767\n",
       "L2.e(oilconsumption)    -0.0104      0.206     -0.050      0.960      -0.413       0.393\n",
       "L2.e(oilproduction)     -0.1676      0.399     -0.420      0.674      -0.949       0.614\n",
       "L2.e(oilstorage)        -0.0011      0.011     -0.104      0.917      -0.023       0.021\n",
       "                            Results for equation oilstorage                             \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "drift                   -0.0057      0.032     -0.179      0.858      -0.068       0.057\n",
       "intercept               12.2887     66.597      0.185      0.854    -118.240     142.817\n",
       "trend.2                  0.0004      0.087      0.004      0.997      -0.171       0.171\n",
       "L1.oilrig_count        -51.2926    105.172     -0.488      0.626    -257.426     154.841\n",
       "L1.oil_price           -59.1401     81.734     -0.724      0.469    -219.336     101.055\n",
       "L1.oilconsumption       -2.0837      6.256     -0.333      0.739     -14.344      10.177\n",
       "L1.oilproduction        -8.2646     19.927     -0.415      0.678     -47.322      30.792\n",
       "L1.oilstorage           -0.1309      0.347     -0.378      0.706      -0.810       0.548\n",
       "L2.oilrig_count        -60.5039     90.799     -0.666      0.505    -238.468     117.460\n",
       "L2.oil_price            68.9280     77.093      0.894      0.371     -82.172     220.027\n",
       "L2.oilconsumption       -1.9875      2.225     -0.893      0.372      -6.349       2.374\n",
       "L2.oilproduction        -1.0526     10.683     -0.099      0.922     -21.991      19.886\n",
       "L2.oilstorage           -0.2606      0.284     -0.918      0.358      -0.817       0.296\n",
       "L1.e(oilrig_count)     -33.6323    116.375     -0.289      0.773    -261.723     194.459\n",
       "L1.e(oil_price)         26.6196     66.630      0.400      0.690    -103.973     157.213\n",
       "L1.e(oilconsumption)    -1.4085      6.027     -0.234      0.815     -13.221      10.404\n",
       "L1.e(oilproduction)      2.0038     17.423      0.115      0.908     -32.146      36.153\n",
       "L1.e(oilstorage)        -0.1135      0.332     -0.342      0.732      -0.763       0.536\n",
       "L2.e(oilrig_count)      18.7789    132.632      0.142      0.887    -241.175     278.733\n",
       "L2.e(oil_price)         28.3586    120.460      0.235      0.814    -207.739     264.456\n",
       "L2.e(oilconsumption)    -1.3544      5.982     -0.226      0.821     -13.079      10.370\n",
       "L2.e(oilproduction)     -0.2327     15.613     -0.015      0.988     -30.833      30.368\n",
       "L2.e(oilstorage)        -0.1117      0.323     -0.346      0.730      -0.745       0.522\n",
       "                                         Error covariance matrix                                         \n",
       "=========================================================================================================\n",
       "                                            coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------\n",
       "sqrt.var.oilrig_count                    14.8966      1.050     14.190      0.000      12.839      16.954\n",
       "sqrt.cov.oilrig_count.oil_price           1.2061      0.774      1.558      0.119      -0.311       2.723\n",
       "sqrt.var.oil_price                        5.2094      0.281     18.545      0.000       4.659       5.760\n",
       "sqrt.cov.oilrig_count.oilconsumption     84.2875    121.479      0.694      0.488    -153.808     322.383\n",
       "sqrt.cov.oil_price.oilconsumption       -29.3903     89.720     -0.328      0.743    -205.238     146.458\n",
       "sqrt.var.oilconsumption                 639.2858     64.487      9.913      0.000     512.895     765.677\n",
       "sqrt.cov.oilrig_count.oilproduction       4.2119     43.290      0.097      0.922     -80.635      89.059\n",
       "sqrt.cov.oil_price.oilproduction        -22.5232     27.884     -0.808      0.419     -77.175      32.128\n",
       "sqrt.cov.oilconsumption.oilproduction    51.7839     32.138      1.611      0.107     -11.205     114.773\n",
       "sqrt.var.oilproduction                  197.5986     13.119     15.062      0.000     171.886     223.311\n",
       "sqrt.cov.oilrig_count.oilstorage      -1973.9053     36.212    -54.509      0.000   -2044.880   -1902.930\n",
       "sqrt.cov.oil_price.oilstorage         -1494.4142      3.511   -425.678      0.000   -1501.295   -1487.533\n",
       "sqrt.cov.oilconsumption.oilstorage    -1883.8979      6.152   -306.241      0.000   -1895.955   -1871.841\n",
       "sqrt.cov.oilproduction.oilstorage      2846.3159      3.535    805.275      0.000    2839.388    2853.244\n",
       "sqrt.var.oilstorage                    9496.5814      3.828   2481.097      0.000    9489.079    9504.083\n",
       "=========================================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 3.21e+19. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startTime = time.time()\n",
    "oil_model = VARMAX(dfoil_transformed, order=(2,2), trend='ctt')\n",
    "oil_results = oil_model.fit(maxiter=1000, disp=False)\n",
    "dfoil_forecast = oil_results.forecast(nobs)\n",
    "dfoil_forecast['oilrig_count1d'] = (dfoil['oilrig_count'].iloc[-1]-dfoil['oilrig_count'].iloc[-2])+dfoil_forecast['oilrig_count'].cumsum()\n",
    "dfoil_forecast['oilrig_forecast'] = dfoil['oilrig_count'].iloc[-1] + dfoil_forecast['oilrig_count1d'].cumsum()\n",
    "executionTime = (time.time() - startTime)\n",
    "oil_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2333e042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time in seconds: 147.76382875442505\n"
     ]
    }
   ],
   "source": [
    "print('Execution time in seconds: ' + str(executionTime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d436f9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Oil Rig Count Prediction: REAL FORECAST - VARMA'}>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfoil['oilrig_count'].loc['2017':].plot(legend=True)\n",
    "dfoil_forecast['oilrig_forecast'].plot(legend=True,linewidth=3,title='Oil Rig Count Prediction: REAL FORECAST - VARMA')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50bc851",
   "metadata": {},
   "source": [
    "#### Create model with Gas Rig Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2d325b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gasrig_count', 'gas_price', 'gaswell_count', 'gasimports_amount',\n",
       "       'gasconsumption_amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfgas.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "493a8d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfgas_transformed = dfgas.diff().diff()\n",
    "dfgas_transformed = dfgas_transformed.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c42b08",
   "metadata": {},
   "source": [
    "Evaluation Metrics Gas Rig Count VARMA Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1adc192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = dfgas_transformed[0:-nobs], dfoil_transformed[-nobs:]\n",
    "gas_model = VARMAX(train, order=(2,2),trend='ctt')\n",
    "gas_results = gas_model.fit(maxiter=1000, disp=False)\n",
    "dfgas_forecast = gas_results.forecast(nobs)\n",
    "dfgas_forecast['gasrig_count1d'] = (dfgas['gasrig_count'].iloc[-nobs-1]-dfgas['gasrig_count'].iloc[-nobs-2])+dfgas_forecast['gasrig_count'].cumsum()\n",
    "dfgas_forecast['gasrig_forecast'] = dfgas['gasrig_count'].iloc[-nobs-1] + dfgas_forecast['gasrig_count1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7077488b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Gas Rig Count Prediction: Forecast Vs Test set - VARMA'}>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfgas['gasrig_count'].loc['2017':].plot(legend=True)\n",
    "dfgas_forecast['gasrig_forecast'].plot(legend=True,linewidth=4,title='Gas Rig Count Prediction: Forecast Vs Test set - VARMA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f245957",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gas Rig VARMA(2,2) RMSE: 7.045\n",
      "Gas Rig VARMA(2,2) MSE: 49.629\n",
      "Gas Rig VARMA(2,2) MAE: 5.611\n",
      "\n",
      "Gas Rig Mean Value: 94.254\n",
      "Gas Rig std Value: 7.984\n",
      "Gas Rig Percent Change: 7.474%\n"
     ]
    }
   ],
   "source": [
    "RMSE2 = rmse(dfgas['gasrig_count'].loc[test.index.min():],dfgas_forecast['gasrig_forecast'])\n",
    "MSE2 = mse(dfgas['gasrig_count'].loc[test.index.min():],dfgas_forecast['gasrig_forecast'])\n",
    "MAE2 = meanabs(dfgas['gasrig_count'].loc[test.index.min():],dfgas_forecast['gasrig_forecast'])\n",
    "\n",
    "print(f\"\"\"Gas Rig VARMA(2,2) RMSE: {RMSE2:.3f}\n",
    "Gas Rig VARMA(2,2) MSE: {MSE2:.3f}\n",
    "Gas Rig VARMA(2,2) MAE: {MAE2:.3f}\\n\n",
    "Gas Rig Mean Value: {dfgas['gasrig_count'][-nobs:].mean():.3f}\n",
    "Gas Rig std Value: {dfgas['gasrig_count'][-nobs:].std():.3f}\n",
    "Gas Rig Percent Change: {RMSE2/dfgas['gasrig_count'][-nobs:].mean()*100:.3f}%\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "788be812",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 176 TEST: 12\n",
      "TRAIN: 188 TEST: 12\n",
      "TRAIN: 200 TEST: 12\n",
      "TRAIN: 212 TEST: 12\n",
      "TRAIN: 224 TEST: 12\n",
      "TRAIN: 236 TEST: 12\n",
      "TRAIN: 248 TEST: 12\n",
      "TRAIN: 260 TEST: 12\n",
      "TRAIN: 272 TEST: 12\n",
      "TRAIN: 284 TEST: 12\n",
      "\n",
      "10-fold Cross Validation Gas Rig\n",
      "RMSE: 213.752,\n",
      "MSE: 87621.795\n",
      "MAE: 209.565\n",
      "\n",
      "Gas Rig Mean Value: 94.254\n",
      "Gas Rig std Value: 7.984\n",
      "Gas Rig Percent Change: 226.782%\n"
     ]
    }
   ],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=10, test_size=12)\n",
    "RMSE_gas = []\n",
    "MSE_gas = []\n",
    "MAE_gas = []\n",
    "for train_index, test_index in tscv.split(dfoil_transformed):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\", len(test_index))\n",
    "    train,test = dfgas_transformed.loc[dfgas_transformed.index[train_index],:], dfgas_transformed.loc[dfgas_transformed.index[test_index],:]\n",
    "    gas_model = VARMAX(train, order=(2,2), trend='ctt')\n",
    "    gas_results = gas_model.fit(maxiter=1000, disp=False)\n",
    "    dfgas_forecast = gas_results.forecast(nobs)\n",
    "    dfgas_forecast['gasrig_count1d'] = (dfgas['gasrig_count'].iloc[-nobs-1]-dfgas['gasrig_count'].iloc[-nobs-2])+dfgas_forecast['gasrig_count'].cumsum()\n",
    "    dfgas_forecast['gasrig_forecast'] = dfgas['gasrig_count'].iloc[-nobs-1] + dfgas_forecast['gasrig_count1d'].cumsum()\n",
    "    RMSE_gas.append('{:.3f}'.format(rmse(dfgas.loc[dfgas.index[test_index],'gasrig_count'],dfgas_forecast['gasrig_forecast'])))\n",
    "    MSE_gas.append('{:.3f}'.format(mse(dfgas.loc[dfgas.index[test_index],'gasrig_count'],dfgas_forecast['gasrig_forecast'])))\n",
    "    MAE_gas.append('{:.3f}'.format(meanabs(dfgas.loc[dfgas.index[test_index],'gasrig_count'],dfgas_forecast['gasrig_forecast'])))\n",
    "\n",
    "RMSE_gas = np.array(RMSE_gas).astype('float')\n",
    "MSE_gas = np.array(MSE_gas).astype('float')\n",
    "MAE_gas = np.array(MAE_gas).astype('float')\n",
    "print(f\"\"\"\\n10-fold Cross Validation Gas Rig\n",
    "RMSE: {RMSE_gas.mean():.3f},\n",
    "MSE: {MSE_gas.mean():.3f}\n",
    "MAE: {MAE_gas.mean():.3f}\\n\n",
    "Gas Rig Mean Value: {dfgas['gasrig_count'][-nobs:].mean():.3f}\n",
    "Gas Rig std Value: {dfgas['gasrig_count'][-nobs:].std():.3f}\n",
    "Gas Rig Percent Change: {RMSE_gas.mean()/dfgas['gasrig_count'][-nobs:].mean()*100:.3f}%\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a669fa8",
   "metadata": {},
   "source": [
    "Forecast Real Future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ed70b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "gas_model = VARMAX(dfgas_transformed, order=(2,2),trend='ctt')\n",
    "gas_results = gas_model.fit(maxiter=1000, disp=False)\n",
    "dfgas_forecast = gas_results.forecast(nobs)\n",
    "dfgas_forecast['gasrig_count1d'] = (dfgas['gasrig_count'].iloc[-1]-dfgas['gasrig_count'].iloc[-2])+dfgas_forecast['gasrig_count'].cumsum()\n",
    "dfgas_forecast['gasrig_forecast'] = dfgas['gasrig_count'].iloc[-1] + dfgas_forecast['gasrig_count1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9369b9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Gas Rig Count Prediction: REAL FORECAST - VARMA'}>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfgas['gasrig_count'].loc['2017':].plot(legend=True)\n",
    "dfgas_forecast['gasrig_forecast'].plot(legend=True,linewidth=4,title='Gas Rig Count Prediction: REAL FORECAST - VARMA')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}