{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## ARMA Modeling: Estimation\n",
    "\n",
    "**Functions**\n",
    "\n",
    "`tsa.SARIMAX`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "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": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"font\", size=14)\n",
    "\n",
    "term.plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Exercise 65\n",
    "Estimate an AR(1) on the term premium, and compute standard errors for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "intercept    0.023735\n",
       "ar.L1        0.972459\n",
       "sigma2       0.062853\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as tsa\n",
    "\n",
    "mod = tsa.SARIMAX(term, order=(1, 0, 0), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "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>868</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)</td> <th>  Log Likelihood     </th> <td>-32.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 27 Aug 2025</td> <th>  AIC                </th> <td>70.501</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:17:55</td>     <th>  BIC                </th> <td>84.800</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1953</td>    <th>  HQIC               </th> <td>75.973</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 07-01-2025</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>    0.0237</td> <td>    0.009</td> <td>    2.719</td> <td> 0.007</td> <td>    0.007</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.9725</td> <td>    0.006</td> <td>  159.336</td> <td> 0.000</td> <td>    0.960</td> <td>    0.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0629</td> <td>    0.001</td> <td>   59.678</td> <td> 0.000</td> <td>    0.061</td> <td>    0.065</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>80.72</td> <th>  Jarque-Bera (JB):  </th> <td>10565.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.73</td>  <th>  Skew:              </th>   <td>0.91</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.01</td>  <th>  Kurtosis:          </th>   <td>19.99</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:  } &    868      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 0, 0) & \\textbf{  Log Likelihood     } &  -32.251    \\\\\n",
       "\\textbf{Date:}                   & Wed, 27 Aug 2025 & \\textbf{  AIC                } &   70.501    \\\\\n",
       "\\textbf{Time:}                   &     15:17:55     & \\textbf{  BIC                } &   84.800    \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1953    & \\textbf{  HQIC               } &   75.973    \\\\\n",
       "\\textbf{}                        &   - 07-01-2025   & \\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} &       0.0237  &        0.009     &     2.719  &         0.007        &        0.007    &        0.041     \\\\\n",
       "\\textbf{ar.L1}     &       0.9725  &        0.006     &   159.336  &         0.000        &        0.960    &        0.984     \\\\\n",
       "\\textbf{sigma2}    &       0.0629  &        0.001     &    59.678  &         0.000        &        0.061    &        0.065     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 80.72 & \\textbf{  Jarque-Bera (JB):  } & 10565.33  \\\\\n",
       "\\textbf{Prob(Q):}                &  0.00 & \\textbf{  Prob(JB):          } &   0.00    \\\\\n",
       "\\textbf{Heteroskedasticity (H):} &  0.73 & \\textbf{  Skew:              } &   0.91    \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    &  0.01 & \\textbf{  Kurtosis:          } &  19.99    \\\\\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:                  868\n",
       "Model:               SARIMAX(1, 0, 0)   Log Likelihood                 -32.251\n",
       "Date:                Wed, 27 Aug 2025   AIC                             70.501\n",
       "Time:                        15:17:55   BIC                             84.800\n",
       "Sample:                    04-01-1953   HQIC                            75.973\n",
       "                         - 07-01-2025                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0237      0.009      2.719      0.007       0.007       0.041\n",
       "ar.L1          0.9725      0.006    159.336      0.000       0.960       0.984\n",
       "sigma2         0.0629      0.001     59.678      0.000       0.061       0.065\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                  80.72   Jarque-Bera (JB):             10565.33\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.73   Skew:                             0.91\n",
       "Prob(H) (two-sided):                  0.01   Kurtosis:                        19.99\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Exercise 66\n",
    "Estimate an MA(5) on the term premium, and compute standard errors for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\python\\anaconda3\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "intercept    0.897579\n",
       "ma.L1        1.533798\n",
       "ma.L2        1.464543\n",
       "ma.L3        1.220636\n",
       "ma.L4        0.759821\n",
       "ma.L5        0.295727\n",
       "sigma2       0.082649\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = tsa.SARIMAX(term, order=(0, 0, 5), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "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>868</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 0, 5)</td> <th>  Log Likelihood     </th> <td>-151.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 27 Aug 2025</td> <th>  AIC                </th>  <td>316.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:17:56</td>     <th>  BIC                </th>  <td>349.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1953</td>    <th>  HQIC               </th>  <td>328.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 07-01-2025</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>    0.8976</td> <td>    0.063</td> <td>   14.188</td> <td> 0.000</td> <td>    0.774</td> <td>    1.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    1.5338</td> <td>    0.016</td> <td>   97.324</td> <td> 0.000</td> <td>    1.503</td> <td>    1.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>    1.4645</td> <td>    0.029</td> <td>   51.103</td> <td> 0.000</td> <td>    1.408</td> <td>    1.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>     <td>    1.2206</td> <td>    0.035</td> <td>   35.116</td> <td> 0.000</td> <td>    1.153</td> <td>    1.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4</th>     <td>    0.7598</td> <td>    0.034</td> <td>   22.253</td> <td> 0.000</td> <td>    0.693</td> <td>    0.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5</th>     <td>    0.2957</td> <td>    0.023</td> <td>   12.698</td> <td> 0.000</td> <td>    0.250</td> <td>    0.341</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0826</td> <td>    0.003</td> <td>   32.029</td> <td> 0.000</td> <td>    0.078</td> <td>    0.088</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>13.87</td> <th>  Jarque-Bera (JB):  </th> <td>658.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.11</td>  <th>  Skew:              </th>  <td>-0.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.38</td>  <th>  Kurtosis:          </th>  <td>7.23</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:  } &    868      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(0, 0, 5) & \\textbf{  Log Likelihood     } &  -151.023   \\\\\n",
       "\\textbf{Date:}                   & Wed, 27 Aug 2025 & \\textbf{  AIC                } &  316.046    \\\\\n",
       "\\textbf{Time:}                   &     15:17:56     & \\textbf{  BIC                } &  349.409    \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1953    & \\textbf{  HQIC               } &  328.813    \\\\\n",
       "\\textbf{}                        &   - 07-01-2025   & \\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} &       0.8976  &        0.063     &    14.188  &         0.000        &        0.774    &        1.022     \\\\\n",
       "\\textbf{ma.L1}     &       1.5338  &        0.016     &    97.324  &         0.000        &        1.503    &        1.565     \\\\\n",
       "\\textbf{ma.L2}     &       1.4645  &        0.029     &    51.103  &         0.000        &        1.408    &        1.521     \\\\\n",
       "\\textbf{ma.L3}     &       1.2206  &        0.035     &    35.116  &         0.000        &        1.153    &        1.289     \\\\\n",
       "\\textbf{ma.L4}     &       0.7598  &        0.034     &    22.253  &         0.000        &        0.693    &        0.827     \\\\\n",
       "\\textbf{ma.L5}     &       0.2957  &        0.023     &    12.698  &         0.000        &        0.250    &        0.341     \\\\\n",
       "\\textbf{sigma2}    &       0.0826  &        0.003     &    32.029  &         0.000        &        0.078    &        0.088     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 13.87 & \\textbf{  Jarque-Bera (JB):  } & 658.10  \\\\\n",
       "\\textbf{Prob(Q):}                &  0.00 & \\textbf{  Prob(JB):          } &  0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} &  1.11 & \\textbf{  Skew:              } & -0.26   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    &  0.38 & \\textbf{  Kurtosis:          } &  7.23   \\\\\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:                  868\n",
       "Model:               SARIMAX(0, 0, 5)   Log Likelihood                -151.023\n",
       "Date:                Wed, 27 Aug 2025   AIC                            316.046\n",
       "Time:                        15:17:56   BIC                            349.409\n",
       "Sample:                    04-01-1953   HQIC                           328.813\n",
       "                         - 07-01-2025                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.8976      0.063     14.188      0.000       0.774       1.022\n",
       "ma.L1          1.5338      0.016     97.324      0.000       1.503       1.565\n",
       "ma.L2          1.4645      0.029     51.103      0.000       1.408       1.521\n",
       "ma.L3          1.2206      0.035     35.116      0.000       1.153       1.289\n",
       "ma.L4          0.7598      0.034     22.253      0.000       0.693       0.827\n",
       "ma.L5          0.2957      0.023     12.698      0.000       0.250       0.341\n",
       "sigma2         0.0826      0.003     32.029      0.000       0.078       0.088\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                  13.87   Jarque-Bera (JB):               658.10\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.11   Skew:                            -0.26\n",
       "Prob(H) (two-sided):                  0.38   Kurtosis:                         7.23\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Exercise 67\n",
    "Estimate an ARMA(1,1) on the term premium, and compute standard errors for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "intercept    0.041525\n",
       "ar.L1        0.952645\n",
       "ma.L1        0.416872\n",
       "sigma2       0.055041\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = tsa.SARIMAX(term, order=(1, 0, 1), trend=\"c\")\n",
    "res = mod.fit()\n",
    "res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "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>868</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 1)</td> <th>  Log Likelihood     </th> <td>25.184</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 27 Aug 2025</td> <th>  AIC                </th> <td>-42.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:17:56</td>     <th>  BIC                </th> <td>-23.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>04-01-1953</td>    <th>  HQIC               </th> <td>-35.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 07-01-2025</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>    0.0415</td> <td>    0.011</td> <td>    3.615</td> <td> 0.000</td> <td>    0.019</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.9526</td> <td>    0.008</td> <td>  120.987</td> <td> 0.000</td> <td>    0.937</td> <td>    0.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    0.4169</td> <td>    0.012</td> <td>   35.067</td> <td> 0.000</td> <td>    0.394</td> <td>    0.440</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0550</td> <td>    0.001</td> <td>   44.317</td> <td> 0.000</td> <td>    0.053</td> <td>    0.057</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.64</td> <th>  Jarque-Bera (JB):  </th> <td>4074.35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.42</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.69</td> <th>  Skew:              </th>  <td>0.63</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>13.54</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:  } &    868      \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(1, 0, 1) & \\textbf{  Log Likelihood     } &   25.184    \\\\\n",
       "\\textbf{Date:}                   & Wed, 27 Aug 2025 & \\textbf{  AIC                } &  -42.368    \\\\\n",
       "\\textbf{Time:}                   &     15:17:56     & \\textbf{  BIC                } &  -23.303    \\\\\n",
       "\\textbf{Sample:}                 &    04-01-1953    & \\textbf{  HQIC               } &  -35.072    \\\\\n",
       "\\textbf{}                        &   - 07-01-2025   & \\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} &       0.0415  &        0.011     &     3.615  &         0.000        &        0.019    &        0.064     \\\\\n",
       "\\textbf{ar.L1}     &       0.9526  &        0.008     &   120.987  &         0.000        &        0.937    &        0.968     \\\\\n",
       "\\textbf{ma.L1}     &       0.4169  &        0.012     &    35.067  &         0.000        &        0.394    &        0.440     \\\\\n",
       "\\textbf{sigma2}    &       0.0550  &        0.001     &    44.317  &         0.000        &        0.053    &        0.057     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.64 & \\textbf{  Jarque-Bera (JB):  } & 4074.35  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.42 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 0.69 & \\textbf{  Skew:              } &   0.63   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  13.54   \\\\\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:                  868\n",
       "Model:               SARIMAX(1, 0, 1)   Log Likelihood                  25.184\n",
       "Date:                Wed, 27 Aug 2025   AIC                            -42.368\n",
       "Time:                        15:17:56   BIC                            -23.303\n",
       "Sample:                    04-01-1953   HQIC                           -35.072\n",
       "                         - 07-01-2025                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0415      0.011      3.615      0.000       0.019       0.064\n",
       "ar.L1          0.9526      0.008    120.987      0.000       0.937       0.968\n",
       "ma.L1          0.4169      0.012     35.067      0.000       0.394       0.440\n",
       "sigma2         0.0550      0.001     44.317      0.000       0.053       0.057\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.64   Jarque-Bera (JB):              4074.35\n",
       "Prob(Q):                              0.42   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.69   Skew:                             0.63\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        13.54\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
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