{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## ARMA Modeling: Forecasting\n",
    "\n",
    "**Functions**\n",
    "\n",
    "`tsa.SARIMAX.forecast`\n",
    "\n",
    "### Exercise 70\n",
    "Produce 1-step forecasts from your preferred model in the previous exercise, as well\n",
    "as a random-walk model. \n",
    "\n",
    "1. Are the forecasts objectively accurate?\n",
    "2. Compare these forecasts to the random walk models using MSE and MAE.\n",
    "\n",
    "**Note**: Use 50% of the sample to estimate the model and 50% to evaluate it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:12.877424Z",
     "iopub.status.busy": "2023-09-28T12:33:12.877424Z",
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     "shell.execute_reply": "2023-09-28T12:33:13.735592Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_hdf(\"data/term-premium.h5\", \"term_premium\")\n",
    "term = data[\"TERM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:13.735592Z",
     "iopub.status.busy": "2023-09-28T12:33:13.735592Z",
     "iopub.status.idle": "2023-09-28T12:33:14.987797Z",
     "shell.execute_reply": "2023-09-28T12:33:14.987797Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>TERM</td>       <th>  No. Observations:  </th>   <td>422</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 1)</td> <th>  Log Likelihood     </th> <td>-70.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 28 Sep 2023</td> <th>  AIC                </th> <td>148.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:33:14</td>     <th>  BIC                </th> <td>164.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1953</td>    <th>  HQIC               </th> <td>154.686</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 05-01-1988</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",
       "      <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>    0.0587</td> <td>    0.021</td> <td>    2.772</td> <td> 0.006</td> <td>    0.017</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.9001</td> <td>    0.016</td> <td>   56.265</td> <td> 0.000</td> <td>    0.869</td> <td>    0.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    0.4589</td> <td>    0.019</td> <td>   23.680</td> <td> 0.000</td> <td>    0.421</td> <td>    0.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0811</td> <td>    0.003</td> <td>   27.575</td> <td> 0.000</td> <td>    0.075</td> <td>    0.087</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.25</td> <th>  Jarque-Bera (JB):  </th> <td>1050.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.62</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>6.47</td> <th>  Skew:              </th>  <td>0.32</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>10.70</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &       TERM       & \\textbf{  No. Observations:  } &    422      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 0, 1) & \\textbf{  Log Likelihood     } &  -70.146    \\\\\n",
       "\\textbf{Date:}                   & Thu, 28 Sep 2023 & \\textbf{  AIC                } &  148.293    \\\\\n",
       "\\textbf{Time:}                   &     13:33:14     & \\textbf{  BIC                } &  164.473    \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1953    & \\textbf{  HQIC               } &  154.686    \\\\\n",
       "\\textbf{}                        &   - 05-01-1988   & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{intercept} &       0.0587  &        0.021     &     2.772  &         0.006        &        0.017    &        0.100     \\\\\n",
       "\\textbf{ar.L1}     &       0.9001  &        0.016     &    56.265  &         0.000        &        0.869    &        0.931     \\\\\n",
       "\\textbf{ma.L1}     &       0.4589  &        0.019     &    23.680  &         0.000        &        0.421    &        0.497     \\\\\n",
       "\\textbf{sigma2}    &       0.0811  &        0.003     &    27.575  &         0.000        &        0.075    &        0.087     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.25 & \\textbf{  Jarque-Bera (JB):  } & 1050.33  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.62 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 6.47 & \\textbf{  Skew:              } &   0.32   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  10.70   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   TERM   No. Observations:                  422\n",
       "Model:               SARIMAX(1, 0, 1)   Log Likelihood                 -70.146\n",
       "Date:                Thu, 28 Sep 2023   AIC                            148.293\n",
       "Time:                        13:33:14   BIC                            164.473\n",
       "Sample:                    04-01-1953   HQIC                           154.686\n",
       "                         - 05-01-1988                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0587      0.021      2.772      0.006       0.017       0.100\n",
       "ar.L1          0.9001      0.016     56.265      0.000       0.869       0.931\n",
       "ma.L1          0.4589      0.019     23.680      0.000       0.421       0.497\n",
       "sigma2         0.0811      0.003     27.575      0.000       0.075       0.087\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.25   Jarque-Bera (JB):              1050.33\n",
       "Prob(Q):                              0.62   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               6.47   Skew:                             0.32\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        10.70\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as tsa\n",
    "\n",
    "nobs = term.shape[0]\n",
    "half = nobs // 2\n",
    "mod = tsa.SARIMAX(term.iloc[:half], order=(1, 0, 1), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:14.987797Z",
     "iopub.status.busy": "2023-09-28T12:33:14.987797Z",
     "iopub.status.idle": "2023-09-28T12:33:15.032852Z",
     "shell.execute_reply": "2023-09-28T12:33:15.032852Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mod = tsa.statespace.SARIMAX(term, order=(1, 0, 1), trend=\"c\")\n",
    "forecast_res = mod.filter(res.params)\n",
    "\n",
    "fcast = forecast_res.predict()\n",
    "\n",
    "errors = term - fcast\n",
    "oos_errors = errors.iloc[half:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:15.032852Z",
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     "iopub.status.idle": "2023-09-28T12:33:16.433876Z",
     "shell.execute_reply": "2023-09-28T12:33:16.433876Z"
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     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"font\", size=14)\n",
    "\n",
    "both = pd.concat([term, fcast], axis=1)\n",
    "both.columns = [\"TERM\", \"One-step Ahead\"]\n",
    "both_oos = both.iloc[half:]\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "both_oos.plot.line(ax=axes[0])\n",
    "axes[0].set_xticklabels([])\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[0].set_title(\"Term and ARMA(1,1) 1-step Forecast\")\n",
    "errors = both.TERM - both[\"One-step Ahead\"]\n",
    "errors.plot.line(ax=axes[1], linestyle=\"none\", marker=\"x\")\n",
    "axes[1].set_title(\"ARMA(1,1) 1-step Forecast Errors\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:16.433876Z",
     "iopub.status.busy": "2023-09-28T12:33:16.433876Z",
     "iopub.status.idle": "2023-09-28T12:33:16.886852Z",
     "shell.execute_reply": "2023-09-28T12:33:16.886852Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   52.68</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Sep 2023</td> <th>  Prob (F-statistic):</th> <td>1.91e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:33:16</td>     <th>  Log-Likelihood:    </th> <td>  148.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   423</td>      <th>  AIC:               </th> <td>  -293.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   421</td>      <th>  BIC:               </th> <td>  -285.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Const</th>    <td>   -0.0291</td> <td>    0.013</td> <td>   -2.219</td> <td> 0.027</td> <td>   -0.055</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Forecast</th> <td>    0.0587</td> <td>    0.008</td> <td>    7.258</td> <td> 0.000</td> <td>    0.043</td> <td>    0.075</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>24.121</td> <th>  Durbin-Watson:     </th> <td>   2.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  53.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.285</td> <th>  Prob(JB):          </th> <td>2.77e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.642</td> <th>  Cond. No.          </th> <td>    3.22</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.111   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.109   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     52.68   \\\\\n",
       "\\textbf{Date:}             & Thu, 28 Sep 2023 & \\textbf{  Prob (F-statistic):} &  1.91e-12   \\\\\n",
       "\\textbf{Time:}             &     13:33:16     & \\textbf{  Log-Likelihood:    } &    148.57   \\\\\n",
       "\\textbf{No. Observations:} &         423      & \\textbf{  AIC:               } &    -293.1   \\\\\n",
       "\\textbf{Df Residuals:}     &         421      & \\textbf{  BIC:               } &    -285.1   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Const}    &      -0.0291  &        0.013     &    -2.219  &         0.027        &       -0.055    &       -0.003     \\\\\n",
       "\\textbf{Forecast} &       0.0587  &        0.008     &     7.258  &         0.000        &        0.043    &        0.075     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 24.121 & \\textbf{  Durbin-Watson:     } &    2.203  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   53.221  \\\\\n",
       "\\textbf{Skew:}          &  0.285 & \\textbf{  Prob(JB):          } & 2.77e-12  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.642 & \\textbf{  Cond. No.          } &     3.22  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.111\n",
       "Model:                            OLS   Adj. R-squared:                  0.109\n",
       "Method:                 Least Squares   F-statistic:                     52.68\n",
       "Date:                Thu, 28 Sep 2023   Prob (F-statistic):           1.91e-12\n",
       "Time:                        13:33:16   Log-Likelihood:                 148.57\n",
       "No. Observations:                 423   AIC:                            -293.1\n",
       "Df Residuals:                     421   BIC:                            -285.1\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Const         -0.0291      0.013     -2.219      0.027      -0.055      -0.003\n",
       "Forecast       0.0587      0.008      7.258      0.000       0.043       0.075\n",
       "==============================================================================\n",
       "Omnibus:                       24.121   Durbin-Watson:                   2.203\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               53.221\n",
       "Skew:                           0.285   Prob(JB):                     2.77e-12\n",
       "Kurtosis:                       4.642   Cond. No.                         3.22\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "oos_fcast = sm.add_constant(fcast.iloc[half:])\n",
    "oos_fcast.columns = [\"Const\", \"Forecast\"]\n",
    "res = sm.OLS(oos_errors, oos_fcast).fit()\n",
    "\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:16.886852Z",
     "iopub.status.busy": "2023-09-28T12:33:16.886852Z",
     "iopub.status.idle": "2023-09-28T12:33:16.900228Z",
     "shell.execute_reply": "2023-09-28T12:33:16.900228Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\anaconda\\Lib\\site-packages\\statsmodels\\base\\model.py:1906: FutureWarning: The behavior of wald_test will change after 0.14 to returning scalar test statistic values. To get the future behavior now, set scalar to True. To silence this message while retaining the legacy behavior, set scalar to False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<F test: F=array([[40.71301765]]), p=6.850093904657588e-17, df_denom=421, df_num=2>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "r_matrix = np.eye(2)\n",
    "res.wald_test(r_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:16.900228Z",
     "iopub.status.busy": "2023-09-28T12:33:16.900228Z",
     "iopub.status.idle": "2023-09-28T12:33:16.929894Z",
     "shell.execute_reply": "2023-09-28T12:33:16.929894Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>TERM</td>       <th>  R-squared:         </th> <td>   0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.8603</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Sep 2023</td> <th>  Prob (F-statistic):</th>  <td> 0.354</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:33:16</td>     <th>  Log-Likelihood:    </th> <td>  127.11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   423</td>      <th>  AIC:               </th> <td>  -250.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   421</td>      <th>  BIC:               </th> <td>  -242.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Const</th>                <td>    0.0028</td> <td>    0.014</td> <td>    0.209</td> <td> 0.835</td> <td>   -0.024</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>One-step RW Forecast</th> <td>   -0.0074</td> <td>    0.008</td> <td>   -0.928</td> <td> 0.354</td> <td>   -0.023</td> <td>    0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>34.063</td> <th>  Durbin-Watson:     </th> <td>   1.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  92.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.358</td> <th>  Prob(JB):          </th> <td>9.55e-21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.172</td> <th>  Cond. No.          </th> <td>    3.26</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}       &       TERM       & \\textbf{  R-squared:         } &     0.002   \\\\\n",
       "\\textbf{Model:}               &       OLS        & \\textbf{  Adj. R-squared:    } &    -0.000   \\\\\n",
       "\\textbf{Method:}              &  Least Squares   & \\textbf{  F-statistic:       } &    0.8603   \\\\\n",
       "\\textbf{Date:}                & Thu, 28 Sep 2023 & \\textbf{  Prob (F-statistic):} &    0.354    \\\\\n",
       "\\textbf{Time:}                &     13:33:16     & \\textbf{  Log-Likelihood:    } &    127.11   \\\\\n",
       "\\textbf{No. Observations:}    &         423      & \\textbf{  AIC:               } &    -250.2   \\\\\n",
       "\\textbf{Df Residuals:}        &         421      & \\textbf{  BIC:               } &    -242.1   \\\\\n",
       "\\textbf{Df Model:}            &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}     &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                              & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Const}                &       0.0028  &        0.014     &     0.209  &         0.835        &       -0.024    &        0.030     \\\\\n",
       "\\textbf{One-step RW Forecast} &      -0.0074  &        0.008     &    -0.928  &         0.354        &       -0.023    &        0.008     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 34.063 & \\textbf{  Durbin-Watson:     } &    1.370  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   92.196  \\\\\n",
       "\\textbf{Skew:}          &  0.358 & \\textbf{  Prob(JB):          } & 9.55e-21  \\\\\n",
       "\\textbf{Kurtosis:}      &  5.172 & \\textbf{  Cond. No.          } &     3.26  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   TERM   R-squared:                       0.002\n",
       "Model:                            OLS   Adj. R-squared:                 -0.000\n",
       "Method:                 Least Squares   F-statistic:                    0.8603\n",
       "Date:                Thu, 28 Sep 2023   Prob (F-statistic):              0.354\n",
       "Time:                        13:33:16   Log-Likelihood:                 127.11\n",
       "No. Observations:                 423   AIC:                            -250.2\n",
       "Df Residuals:                     421   BIC:                            -242.1\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================\n",
       "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "Const                    0.0028      0.014      0.209      0.835      -0.024       0.030\n",
       "One-step RW Forecast    -0.0074      0.008     -0.928      0.354      -0.023       0.008\n",
       "==============================================================================\n",
       "Omnibus:                       34.063   Durbin-Watson:                   1.370\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               92.196\n",
       "Skew:                           0.358   Prob(JB):                     9.55e-21\n",
       "Kurtosis:                       5.172   Cond. No.                         3.26\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rw_forecast = term.shift(1)\n",
    "both = pd.concat([term, rw_forecast], axis=1)\n",
    "rw_error = term - rw_forecast\n",
    "rw_oos_error = rw_error.iloc[half:]\n",
    "rw_forecast = sm.add_constant(rw_forecast)\n",
    "rw_forecast = rw_forecast.iloc[half:]\n",
    "rw_forecast.columns = [\"Const\", \"One-step RW Forecast\"]\n",
    "res = sm.OLS(rw_oos_error, rw_forecast).fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:16.929894Z",
     "iopub.status.busy": "2023-09-28T12:33:16.929894Z",
     "iopub.status.idle": "2023-09-28T12:33:16.940237Z",
     "shell.execute_reply": "2023-09-28T12:33:16.940237Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\envs\\anaconda\\Lib\\site-packages\\statsmodels\\base\\model.py:1906: FutureWarning: The behavior of wald_test will change after 0.14 to returning scalar test statistic values. To get the future behavior now, set scalar to True. To silence this message while retaining the legacy behavior, set scalar to False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<F test: F=array([[0.73625766]]), p=0.4795183758828, df_denom=421, df_num=2>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.wald_test(r_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:16.944021Z",
     "iopub.status.busy": "2023-09-28T12:33:16.943019Z",
     "iopub.status.idle": "2023-09-28T12:33:17.665296Z",
     "shell.execute_reply": "2023-09-28T12:33:17.665296Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "\n",
    "both.columns = [\"TERM\", \"RW One-step Ahead\"]\n",
    "both_oos = both.iloc[half:]\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "both_oos.plot.line(ax=axes[0])\n",
    "axes[0].set_xticklabels([])\n",
    "axes[0].set_xlabel(\"\")\n",
    "axes[0].set_title(\"Term and RW 1-step Forecast\")\n",
    "\n",
    "errors = rw_oos_error\n",
    "errors.plot.line(ax=axes[1], linestyle=\"none\", marker=\"x\")\n",
    "axes[1].set_title(\"RW 1-step Forecast Errors\")\n",
    "fig.tight_layout(pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 71\n",
    "\n",
    "Produce 3-step forecasts from the models selected in the previous exercises as well as a random walk model.\n",
    "\n",
    "1. Compare these forecasts to the random walk models using MSE and MAE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:17.665296Z",
     "iopub.status.busy": "2023-09-28T12:33:17.665296Z",
     "iopub.status.idle": "2023-09-28T12:33:17.864965Z",
     "shell.execute_reply": "2023-09-28T12:33:17.864965Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nobs = term.shape[0]\n",
    "half = nobs // 2\n",
    "mod = tsa.SARIMAX(term.iloc[:half], order=(1, 0, 1), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.summary()\n",
    "\n",
    "mod = tsa.statespace.SARIMAX(term, order=(1, 0, 1), trend=\"c\")\n",
    "forecast_res = mod.filter(res.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:17.864965Z",
     "iopub.status.busy": "2023-09-28T12:33:17.864965Z",
     "iopub.status.idle": "2023-09-28T12:33:18.547767Z",
     "shell.execute_reply": "2023-09-28T12:33:18.547767Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "three_step = {}\n",
    "for t in range(half, nobs):\n",
    "    fcasts = forecast_res.predict(t, t + 2, dynamic=True)\n",
    "    key = fcasts.index[-1]\n",
    "    value = fcasts.iloc[-1]\n",
    "    three_step[key] = value\n",
    "three_step = pd.Series(three_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:18.551275Z",
     "iopub.status.busy": "2023-09-28T12:33:18.551275Z",
     "iopub.status.idle": "2023-09-28T12:33:18.559076Z",
     "shell.execute_reply": "2023-09-28T12:33:18.559076Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1988-08-01   -0.323108\n",
       "1988-09-01   -0.246349\n",
       "1988-10-01   -0.441219\n",
       "1988-11-01   -0.411333\n",
       "1988-12-01   -0.661195\n",
       "                ...   \n",
       "2023-04-01   -0.463667\n",
       "2023-05-01   -0.598025\n",
       "2023-06-01   -0.917611\n",
       "2023-07-01   -0.599572\n",
       "2023-08-01   -0.334206\n",
       "Length: 421, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_step_errors = term - three_step\n",
    "three_step_errors = three_step_errors.dropna()\n",
    "three_step_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:18.559076Z",
     "iopub.status.busy": "2023-09-28T12:33:18.559076Z",
     "iopub.status.idle": "2023-09-28T12:33:18.566007Z",
     "shell.execute_reply": "2023-09-28T12:33:18.566007Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "three_step_rw = term.shift(3)\n",
    "three_step_rw_errors = term - three_step_rw\n",
    "three_step_rw_errors = three_step_rw_errors.iloc[half:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:18.567018Z",
     "iopub.status.busy": "2023-09-28T12:33:18.567018Z",
     "iopub.status.idle": "2023-09-28T12:33:18.583970Z",
     "shell.execute_reply": "2023-09-28T12:33:18.582955Z"
    },
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     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "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>ARMA</th>\n",
       "      <th>RW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSE</th>\n",
       "      <td>0.205157</td>\n",
       "      <td>0.140252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE</th>\n",
       "      <td>0.348047</td>\n",
       "      <td>0.287102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ARMA        RW\n",
       "MSE  0.205157  0.140252\n",
       "MAE  0.348047  0.287102"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors = pd.DataFrame({\"ARMA\": three_step_errors, \"RW\": three_step_rw_errors}).dropna()\n",
    "pd.DataFrame([(errors**2).mean(), errors.abs().mean()], index=[\"MSE\", \"MAE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:18.586965Z",
     "iopub.status.busy": "2023-09-28T12:33:18.585968Z",
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     "shell.execute_reply": "2023-09-28T12:33:18.606227Z"
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     "is_executing": false,
     "name": "#%%\n"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Sep 2023</td> <th>  Prob (F-statistic):</th>  <td>   nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:33:18</td>     <th>  Log-Likelihood:    </th> <td>  16.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   421</td>      <th>  AIC:               </th> <td>  -30.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   420</td>      <th>  BIC:               </th> <td>  -26.84</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     0</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>HAC</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0649</td> <td>    0.023</td> <td>    2.867</td> <td> 0.004</td> <td>    0.021</td> <td>    0.109</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>86.887</td> <th>  Durbin-Watson:     </th> <td>   0.468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 217.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.016</td> <th>  Prob(JB):          </th> <td>4.97e-48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.880</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.000   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.000   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &       nan   \\\\\n",
       "\\textbf{Date:}             & Thu, 28 Sep 2023 & \\textbf{  Prob (F-statistic):} &      nan    \\\\\n",
       "\\textbf{Time:}             &     13:33:18     & \\textbf{  Log-Likelihood:    } &    16.442   \\\\\n",
       "\\textbf{No. Observations:} &         421      & \\textbf{  AIC:               } &    -30.88   \\\\\n",
       "\\textbf{Df Residuals:}     &         420      & \\textbf{  BIC:               } &    -26.84   \\\\\n",
       "\\textbf{Df Model:}         &           0      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &       HAC        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.0649  &        0.023     &     2.867  &         0.004        &        0.021    &        0.109     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 86.887 & \\textbf{  Durbin-Watson:     } &    0.468  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  217.841  \\\\\n",
       "\\textbf{Skew:}          &  1.016 & \\textbf{  Prob(JB):          } & 4.97e-48  \\\\\n",
       "\\textbf{Kurtosis:}      &  5.880 & \\textbf{  Cond. No.          } &     1.00  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                  0.000\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Thu, 28 Sep 2023   Prob (F-statistic):                nan\n",
       "Time:                        13:33:18   Log-Likelihood:                 16.442\n",
       "No. Observations:                 421   AIC:                            -30.88\n",
       "Df Residuals:                     420   BIC:                            -26.84\n",
       "Df Model:                           0                                         \n",
       "Covariance Type:                  HAC                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0649      0.023      2.867      0.004       0.021       0.109\n",
       "==============================================================================\n",
       "Omnibus:                       86.887   Durbin-Watson:                   0.468\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              217.841\n",
       "Skew:                           1.016   Prob(JB):                     4.97e-48\n",
       "Kurtosis:                       5.880   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = errors[\"ARMA\"] ** 2 - errors[\"RW\"] ** 2\n",
    "x = np.ones_like(d)\n",
    "maxlags = int(d.shape[0] ** (1 / 3))\n",
    "res = sm.OLS(d, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": maxlags})\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-28T12:33:18.606227Z",
     "iopub.status.busy": "2023-09-28T12:33:18.606227Z",
     "iopub.status.idle": "2023-09-28T12:33:18.629024Z",
     "shell.execute_reply": "2023-09-28T12:33:18.629024Z"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>     nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 28 Sep 2023</td> <th>  Prob (F-statistic):</th>  <td>   nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:33:18</td>     <th>  Log-Likelihood:    </th> <td> -22.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   421</td>      <th>  AIC:               </th> <td>   46.61</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   420</td>      <th>  BIC:               </th> <td>   50.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     0</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>         <td>HAC</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0609</td> <td>    0.024</td> <td>    2.513</td> <td> 0.012</td> <td>    0.013</td> <td>    0.108</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.507</td> <th>  Durbin-Watson:     </th> <td>   0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.776</td> <th>  Jarque-Bera (JB):  </th> <td>   0.483</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.083</td> <th>  Prob(JB):          </th> <td>   0.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.986</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.000   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.000   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &       nan   \\\\\n",
       "\\textbf{Date:}             & Thu, 28 Sep 2023 & \\textbf{  Prob (F-statistic):} &      nan    \\\\\n",
       "\\textbf{Time:}             &     13:33:18     & \\textbf{  Log-Likelihood:    } &   -22.305   \\\\\n",
       "\\textbf{No. Observations:} &         421      & \\textbf{  AIC:               } &     46.61   \\\\\n",
       "\\textbf{Df Residuals:}     &         420      & \\textbf{  BIC:               } &     50.65   \\\\\n",
       "\\textbf{Df Model:}         &           0      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &       HAC        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.0609  &        0.024     &     2.513  &         0.012        &        0.013    &        0.108     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  0.507 & \\textbf{  Durbin-Watson:     } &    0.547  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.776 & \\textbf{  Jarque-Bera (JB):  } &    0.483  \\\\\n",
       "\\textbf{Skew:}          &  0.083 & \\textbf{  Prob(JB):          } &    0.786  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.986 & \\textbf{  Cond. No.          } &     1.00  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.000\n",
       "Model:                            OLS   Adj. R-squared:                  0.000\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Thu, 28 Sep 2023   Prob (F-statistic):                nan\n",
       "Time:                        13:33:18   Log-Likelihood:                -22.305\n",
       "No. Observations:                 421   AIC:                             46.61\n",
       "Df Residuals:                     420   BIC:                             50.65\n",
       "Df Model:                           0                                         \n",
       "Covariance Type:                  HAC                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0609      0.024      2.513      0.012       0.013       0.108\n",
       "==============================================================================\n",
       "Omnibus:                        0.507   Durbin-Watson:                   0.547\n",
       "Prob(Omnibus):                  0.776   Jarque-Bera (JB):                0.483\n",
       "Skew:                           0.083   Prob(JB):                        0.786\n",
       "Kurtosis:                       2.986   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 7 lags and without small sample correction\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = errors[\"ARMA\"].abs() - errors[\"RW\"].abs()\n",
    "res = sm.OLS(d, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": maxlags})\n",
    "res.summary()"
   ]
  }
 ],
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