{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## ARMA Modeling: Unit Root Testing\n",
    "\n",
    "**Functions**\n",
    "\n",
    "`sm.tsa.stattools.adfuller`, `arch.unitroot.ADF` \n",
    "\n",
    "### Exercise 72\n",
    "Download data on the AAA and BAA yields (Moodys) from FRED and construct the\n",
    "default premium as the difference between these two.\n",
    "\n",
    "1. Test the default premium for a unit root. \n",
    "2. If you find a unit root, test the change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader as pdr\n",
    "\n",
    "# Conservative start date to get all data\n",
    "aaa = pdr.get_data_fred(\"AAA\", start=\"1950\")\n",
    "baa = pdr.get_data_fred(\"BAA\", start=\"1950\")\n",
    "\n",
    "default = aaa[\"AAA\"] - baa[\"BAA\"]\n",
    "default.name = \"Default\""
   ]
  },
  {
   "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",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"font\", size=14)\n",
    "\n",
    "default.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Augmented Dickey-Fuller Results</caption>\n",
       "<tr>\n",
       "  <td>Test Statistic</td>    <td>-3.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>P-value</td>            <td>0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Lags</td>                  <td>16</td>\n",
       "</tr>\n",
       "</table><br/><br/>Trend: Constant and Linear Time Trend<br/>Critical Values: -3.97 (1%), -3.42 (5%), -3.13 (10%)<br/>Null Hypothesis: The process contains a unit root.<br/>Alternative Hypothesis: The process is weakly stationary."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lr}\n",
       "\\toprule\n",
       "Test Statistic     &             -3.648  \\\\\n",
       "P-value            &              0.026  \\\\\n",
       "Lags               &                 16  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Augmented Dickey-Fuller Results}\n",
       "\\end{center}\n",
       "\n",
       "Trend: Constant and Linear Time Trend \\newline\n",
       " Critical Values: -3.97 (1%), -3.42 (5%), -3.13 (10%) \\newline\n",
       " Null Hypothesis: The process contains a unit root. \\newline\n",
       " Alternative Hypothesis: The process is weakly stationary."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "   Augmented Dickey-Fuller Results   \n",
       "=====================================\n",
       "Test Statistic                 -3.648\n",
       "P-value                         0.026\n",
       "Lags                               16\n",
       "-------------------------------------\n",
       "\n",
       "Trend: Constant and Linear Time Trend\n",
       "Critical Values: -3.97 (1%), -3.42 (5%), -3.13 (10%)\n",
       "Null Hypothesis: The process contains a unit root.\n",
       "Alternative Hypothesis: The process is weakly stationary.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from arch.unitroot import ADF\n",
    "\n",
    "adf = ADF(default, trend=\"ct\")\n",
    "adf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "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>   0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th> <td>2.94e-28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:18:09</td>     <th>  Log-Likelihood:    </th> <td>  832.04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   890</td>      <th>  AIC:               </th> <td>  -1626.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   871</td>      <th>  BIC:               </th> <td>  -1535.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    18</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>Level.L1</th> <td>   -0.0342</td> <td>    0.009</td> <td>   -3.648</td> <td> 0.000</td> <td>   -0.053</td> <td>   -0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L1</th>  <td>    0.3656</td> <td>    0.034</td> <td>   10.786</td> <td> 0.000</td> <td>    0.299</td> <td>    0.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L2</th>  <td>   -0.1324</td> <td>    0.036</td> <td>   -3.679</td> <td> 0.000</td> <td>   -0.203</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L3</th>  <td>    0.0218</td> <td>    0.036</td> <td>    0.603</td> <td> 0.547</td> <td>   -0.049</td> <td>    0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L4</th>  <td>   -0.0069</td> <td>    0.036</td> <td>   -0.191</td> <td> 0.848</td> <td>   -0.078</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L5</th>  <td>    0.1236</td> <td>    0.036</td> <td>    3.425</td> <td> 0.001</td> <td>    0.053</td> <td>    0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L6</th>  <td>   -0.0391</td> <td>    0.036</td> <td>   -1.077</td> <td> 0.282</td> <td>   -0.110</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L7</th>  <td>   -0.0878</td> <td>    0.036</td> <td>   -2.429</td> <td> 0.015</td> <td>   -0.159</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L8</th>  <td>   -0.0077</td> <td>    0.036</td> <td>   -0.212</td> <td> 0.832</td> <td>   -0.079</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L9</th>  <td>    0.0561</td> <td>    0.036</td> <td>    1.549</td> <td> 0.122</td> <td>   -0.015</td> <td>    0.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L10</th> <td>   -0.0790</td> <td>    0.036</td> <td>   -2.191</td> <td> 0.029</td> <td>   -0.150</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L11</th> <td>    0.0410</td> <td>    0.036</td> <td>    1.138</td> <td> 0.256</td> <td>   -0.030</td> <td>    0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L12</th> <td>   -0.0289</td> <td>    0.036</td> <td>   -0.802</td> <td> 0.423</td> <td>   -0.099</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L13</th> <td>   -0.0437</td> <td>    0.036</td> <td>   -1.215</td> <td> 0.225</td> <td>   -0.114</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L14</th> <td>    0.0800</td> <td>    0.036</td> <td>    2.224</td> <td> 0.026</td> <td>    0.009</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L15</th> <td>   -0.0423</td> <td>    0.036</td> <td>   -1.187</td> <td> 0.236</td> <td>   -0.112</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L16</th> <td>   -0.0577</td> <td>    0.034</td> <td>   -1.705</td> <td> 0.089</td> <td>   -0.124</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>   -0.0300</td> <td>    0.010</td> <td>   -3.046</td> <td> 0.002</td> <td>   -0.049</td> <td>   -0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend</th>    <td>-6.345e-06</td> <td>  1.3e-05</td> <td>   -0.488</td> <td> 0.625</td> <td>-3.18e-05</td> <td> 1.91e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>402.626</td> <th>  Durbin-Watson:     </th> <td>   1.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>8953.483</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.531</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>18.234</td>  <th>  Cond. No.          </th> <td>8.72e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 8.72e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.183   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.166   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     10.83   \\\\\n",
       "\\textbf{Date:}             & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &  2.94e-28   \\\\\n",
       "\\textbf{Time:}             &     15:18:09     & \\textbf{  Log-Likelihood:    } &    832.04   \\\\\n",
       "\\textbf{No. Observations:} &         890      & \\textbf{  AIC:               } &    -1626.   \\\\\n",
       "\\textbf{Df Residuals:}     &         871      & \\textbf{  BIC:               } &    -1535.   \\\\\n",
       "\\textbf{Df Model:}         &          18      & \\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{Level.L1} &      -0.0342  &        0.009     &    -3.648  &         0.000        &       -0.053    &       -0.016     \\\\\n",
       "\\textbf{Diff.L1}  &       0.3656  &        0.034     &    10.786  &         0.000        &        0.299    &        0.432     \\\\\n",
       "\\textbf{Diff.L2}  &      -0.1324  &        0.036     &    -3.679  &         0.000        &       -0.203    &       -0.062     \\\\\n",
       "\\textbf{Diff.L3}  &       0.0218  &        0.036     &     0.603  &         0.547        &       -0.049    &        0.093     \\\\\n",
       "\\textbf{Diff.L4}  &      -0.0069  &        0.036     &    -0.191  &         0.848        &       -0.078    &        0.064     \\\\\n",
       "\\textbf{Diff.L5}  &       0.1236  &        0.036     &     3.425  &         0.001        &        0.053    &        0.194     \\\\\n",
       "\\textbf{Diff.L6}  &      -0.0391  &        0.036     &    -1.077  &         0.282        &       -0.110    &        0.032     \\\\\n",
       "\\textbf{Diff.L7}  &      -0.0878  &        0.036     &    -2.429  &         0.015        &       -0.159    &       -0.017     \\\\\n",
       "\\textbf{Diff.L8}  &      -0.0077  &        0.036     &    -0.212  &         0.832        &       -0.079    &        0.064     \\\\\n",
       "\\textbf{Diff.L9}  &       0.0561  &        0.036     &     1.549  &         0.122        &       -0.015    &        0.127     \\\\\n",
       "\\textbf{Diff.L10} &      -0.0790  &        0.036     &    -2.191  &         0.029        &       -0.150    &       -0.008     \\\\\n",
       "\\textbf{Diff.L11} &       0.0410  &        0.036     &     1.138  &         0.256        &       -0.030    &        0.112     \\\\\n",
       "\\textbf{Diff.L12} &      -0.0289  &        0.036     &    -0.802  &         0.423        &       -0.099    &        0.042     \\\\\n",
       "\\textbf{Diff.L13} &      -0.0437  &        0.036     &    -1.215  &         0.225        &       -0.114    &        0.027     \\\\\n",
       "\\textbf{Diff.L14} &       0.0800  &        0.036     &     2.224  &         0.026        &        0.009    &        0.151     \\\\\n",
       "\\textbf{Diff.L15} &      -0.0423  &        0.036     &    -1.187  &         0.236        &       -0.112    &        0.028     \\\\\n",
       "\\textbf{Diff.L16} &      -0.0577  &        0.034     &    -1.705  &         0.089        &       -0.124    &        0.009     \\\\\n",
       "\\textbf{const}    &      -0.0300  &        0.010     &    -3.046  &         0.002        &       -0.049    &       -0.011     \\\\\n",
       "\\textbf{trend}    &   -6.345e-06  &      1.3e-05     &    -0.488  &         0.625        &    -3.18e-05    &     1.91e-05     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 402.626 & \\textbf{  Durbin-Watson:     } &    1.995  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 8953.483  \\\\\n",
       "\\textbf{Skew:}          &  -1.531 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  18.234 & \\textbf{  Cond. No.          } & 8.72e+03  \\\\\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. \\newline\n",
       " [2] The condition number is large, 8.72e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.183\n",
       "Model:                            OLS   Adj. R-squared:                  0.166\n",
       "Method:                 Least Squares   F-statistic:                     10.83\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):           2.94e-28\n",
       "Time:                        15:18:09   Log-Likelihood:                 832.04\n",
       "No. Observations:                 890   AIC:                            -1626.\n",
       "Df Residuals:                     871   BIC:                            -1535.\n",
       "Df Model:                          18                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Level.L1      -0.0342      0.009     -3.648      0.000      -0.053      -0.016\n",
       "Diff.L1        0.3656      0.034     10.786      0.000       0.299       0.432\n",
       "Diff.L2       -0.1324      0.036     -3.679      0.000      -0.203      -0.062\n",
       "Diff.L3        0.0218      0.036      0.603      0.547      -0.049       0.093\n",
       "Diff.L4       -0.0069      0.036     -0.191      0.848      -0.078       0.064\n",
       "Diff.L5        0.1236      0.036      3.425      0.001       0.053       0.194\n",
       "Diff.L6       -0.0391      0.036     -1.077      0.282      -0.110       0.032\n",
       "Diff.L7       -0.0878      0.036     -2.429      0.015      -0.159      -0.017\n",
       "Diff.L8       -0.0077      0.036     -0.212      0.832      -0.079       0.064\n",
       "Diff.L9        0.0561      0.036      1.549      0.122      -0.015       0.127\n",
       "Diff.L10      -0.0790      0.036     -2.191      0.029      -0.150      -0.008\n",
       "Diff.L11       0.0410      0.036      1.138      0.256      -0.030       0.112\n",
       "Diff.L12      -0.0289      0.036     -0.802      0.423      -0.099       0.042\n",
       "Diff.L13      -0.0437      0.036     -1.215      0.225      -0.114       0.027\n",
       "Diff.L14       0.0800      0.036      2.224      0.026       0.009       0.151\n",
       "Diff.L15      -0.0423      0.036     -1.187      0.236      -0.112       0.028\n",
       "Diff.L16      -0.0577      0.034     -1.705      0.089      -0.124       0.009\n",
       "const         -0.0300      0.010     -3.046      0.002      -0.049      -0.011\n",
       "trend      -6.345e-06    1.3e-05     -0.488      0.625   -3.18e-05    1.91e-05\n",
       "==============================================================================\n",
       "Omnibus:                      402.626   Durbin-Watson:                   1.995\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             8953.483\n",
       "Skew:                          -1.531   Prob(JB):                         0.00\n",
       "Kurtosis:                      18.234   Cond. No.                     8.72e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.72e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf.regression.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Augmented Dickey-Fuller Results</caption>\n",
       "<tr>\n",
       "  <td>Test Statistic</td>    <td>-3.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>P-value</td>            <td>0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Lags</td>                  <td>16</td>\n",
       "</tr>\n",
       "</table><br/><br/>Trend: Constant<br/>Critical Values: -3.44 (1%), -2.86 (5%), -2.57 (10%)<br/>Null Hypothesis: The process contains a unit root.<br/>Alternative Hypothesis: The process is weakly stationary."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lr}\n",
       "\\toprule\n",
       "Test Statistic     &             -3.648  \\\\\n",
       "P-value            &              0.005  \\\\\n",
       "Lags               &                 16  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Augmented Dickey-Fuller Results}\n",
       "\\end{center}\n",
       "\n",
       "Trend: Constant \\newline\n",
       " Critical Values: -3.44 (1%), -2.86 (5%), -2.57 (10%) \\newline\n",
       " Null Hypothesis: The process contains a unit root. \\newline\n",
       " Alternative Hypothesis: The process is weakly stationary."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "   Augmented Dickey-Fuller Results   \n",
       "=====================================\n",
       "Test Statistic                 -3.648\n",
       "P-value                         0.005\n",
       "Lags                               16\n",
       "-------------------------------------\n",
       "\n",
       "Trend: Constant\n",
       "Critical Values: -3.44 (1%), -2.86 (5%), -2.57 (10%)\n",
       "Null Hypothesis: The process contains a unit root.\n",
       "Alternative Hypothesis: The process is weakly stationary.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf = ADF(default, trend=\"c\")\n",
    "adf.summary()"
   ]
  },
  {
   "cell_type": "code",
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    {
     "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>   0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   11.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th> <td>9.49e-29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:18:09</td>     <th>  Log-Likelihood:    </th> <td>  831.91</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   890</td>      <th>  AIC:               </th> <td>  -1628.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   872</td>      <th>  BIC:               </th> <td>  -1542.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    17</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>Level.L1</th> <td>   -0.0330</td> <td>    0.009</td> <td>   -3.648</td> <td> 0.000</td> <td>   -0.051</td> <td>   -0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L1</th>  <td>    0.3647</td> <td>    0.034</td> <td>   10.780</td> <td> 0.000</td> <td>    0.298</td> <td>    0.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L2</th>  <td>   -0.1333</td> <td>    0.036</td> <td>   -3.711</td> <td> 0.000</td> <td>   -0.204</td> <td>   -0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L3</th>  <td>    0.0209</td> <td>    0.036</td> <td>    0.579</td> <td> 0.563</td> <td>   -0.050</td> <td>    0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L4</th>  <td>   -0.0078</td> <td>    0.036</td> <td>   -0.216</td> <td> 0.829</td> <td>   -0.079</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L5</th>  <td>    0.1227</td> <td>    0.036</td> <td>    3.406</td> <td> 0.001</td> <td>    0.052</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L6</th>  <td>   -0.0400</td> <td>    0.036</td> <td>   -1.103</td> <td> 0.270</td> <td>   -0.111</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L7</th>  <td>   -0.0886</td> <td>    0.036</td> <td>   -2.455</td> <td> 0.014</td> <td>   -0.159</td> <td>   -0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L8</th>  <td>   -0.0085</td> <td>    0.036</td> <td>   -0.234</td> <td> 0.815</td> <td>   -0.080</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L9</th>  <td>    0.0554</td> <td>    0.036</td> <td>    1.530</td> <td> 0.126</td> <td>   -0.016</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L10</th> <td>   -0.0796</td> <td>    0.036</td> <td>   -2.211</td> <td> 0.027</td> <td>   -0.150</td> <td>   -0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L11</th> <td>    0.0404</td> <td>    0.036</td> <td>    1.122</td> <td> 0.262</td> <td>   -0.030</td> <td>    0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L12</th> <td>   -0.0295</td> <td>    0.036</td> <td>   -0.822</td> <td> 0.411</td> <td>   -0.100</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L13</th> <td>   -0.0443</td> <td>    0.036</td> <td>   -1.234</td> <td> 0.217</td> <td>   -0.115</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L14</th> <td>    0.0794</td> <td>    0.036</td> <td>    2.209</td> <td> 0.027</td> <td>    0.009</td> <td>    0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L15</th> <td>   -0.0428</td> <td>    0.036</td> <td>   -1.203</td> <td> 0.229</td> <td>   -0.113</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L16</th> <td>   -0.0585</td> <td>    0.034</td> <td>   -1.730</td> <td> 0.084</td> <td>   -0.125</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>   -0.0317</td> <td>    0.009</td> <td>   -3.436</td> <td> 0.001</td> <td>   -0.050</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>402.646</td> <th>  Durbin-Watson:     </th> <td>   1.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9003.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.529</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>18.279</td>  <th>  Cond. No.          </th> <td>    24.0</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:         } &     0.183   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.167   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     11.47   \\\\\n",
       "\\textbf{Date:}             & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &  9.49e-29   \\\\\n",
       "\\textbf{Time:}             &     15:18:09     & \\textbf{  Log-Likelihood:    } &    831.91   \\\\\n",
       "\\textbf{No. Observations:} &         890      & \\textbf{  AIC:               } &    -1628.   \\\\\n",
       "\\textbf{Df Residuals:}     &         872      & \\textbf{  BIC:               } &    -1542.   \\\\\n",
       "\\textbf{Df Model:}         &          17      & \\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{Level.L1} &      -0.0330  &        0.009     &    -3.648  &         0.000        &       -0.051    &       -0.015     \\\\\n",
       "\\textbf{Diff.L1}  &       0.3647  &        0.034     &    10.780  &         0.000        &        0.298    &        0.431     \\\\\n",
       "\\textbf{Diff.L2}  &      -0.1333  &        0.036     &    -3.711  &         0.000        &       -0.204    &       -0.063     \\\\\n",
       "\\textbf{Diff.L3}  &       0.0209  &        0.036     &     0.579  &         0.563        &       -0.050    &        0.092     \\\\\n",
       "\\textbf{Diff.L4}  &      -0.0078  &        0.036     &    -0.216  &         0.829        &       -0.079    &        0.063     \\\\\n",
       "\\textbf{Diff.L5}  &       0.1227  &        0.036     &     3.406  &         0.001        &        0.052    &        0.193     \\\\\n",
       "\\textbf{Diff.L6}  &      -0.0400  &        0.036     &    -1.103  &         0.270        &       -0.111    &        0.031     \\\\\n",
       "\\textbf{Diff.L7}  &      -0.0886  &        0.036     &    -2.455  &         0.014        &       -0.159    &       -0.018     \\\\\n",
       "\\textbf{Diff.L8}  &      -0.0085  &        0.036     &    -0.234  &         0.815        &       -0.080    &        0.063     \\\\\n",
       "\\textbf{Diff.L9}  &       0.0554  &        0.036     &     1.530  &         0.126        &       -0.016    &        0.126     \\\\\n",
       "\\textbf{Diff.L10} &      -0.0796  &        0.036     &    -2.211  &         0.027        &       -0.150    &       -0.009     \\\\\n",
       "\\textbf{Diff.L11} &       0.0404  &        0.036     &     1.122  &         0.262        &       -0.030    &        0.111     \\\\\n",
       "\\textbf{Diff.L12} &      -0.0295  &        0.036     &    -0.822  &         0.411        &       -0.100    &        0.041     \\\\\n",
       "\\textbf{Diff.L13} &      -0.0443  &        0.036     &    -1.234  &         0.217        &       -0.115    &        0.026     \\\\\n",
       "\\textbf{Diff.L14} &       0.0794  &        0.036     &     2.209  &         0.027        &        0.009    &        0.150     \\\\\n",
       "\\textbf{Diff.L15} &      -0.0428  &        0.036     &    -1.203  &         0.229        &       -0.113    &        0.027     \\\\\n",
       "\\textbf{Diff.L16} &      -0.0585  &        0.034     &    -1.730  &         0.084        &       -0.125    &        0.008     \\\\\n",
       "\\textbf{const}    &      -0.0317  &        0.009     &    -3.436  &         0.001        &       -0.050    &       -0.014     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 402.646 & \\textbf{  Durbin-Watson:     } &    1.995  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 9003.792  \\\\\n",
       "\\textbf{Skew:}          &  -1.529 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  18.279 & \\textbf{  Cond. No.          } &     24.0  \\\\\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:                       0.183\n",
       "Model:                            OLS   Adj. R-squared:                  0.167\n",
       "Method:                 Least Squares   F-statistic:                     11.47\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):           9.49e-29\n",
       "Time:                        15:18:09   Log-Likelihood:                 831.91\n",
       "No. Observations:                 890   AIC:                            -1628.\n",
       "Df Residuals:                     872   BIC:                            -1542.\n",
       "Df Model:                          17                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Level.L1      -0.0330      0.009     -3.648      0.000      -0.051      -0.015\n",
       "Diff.L1        0.3647      0.034     10.780      0.000       0.298       0.431\n",
       "Diff.L2       -0.1333      0.036     -3.711      0.000      -0.204      -0.063\n",
       "Diff.L3        0.0209      0.036      0.579      0.563      -0.050       0.092\n",
       "Diff.L4       -0.0078      0.036     -0.216      0.829      -0.079       0.063\n",
       "Diff.L5        0.1227      0.036      3.406      0.001       0.052       0.193\n",
       "Diff.L6       -0.0400      0.036     -1.103      0.270      -0.111       0.031\n",
       "Diff.L7       -0.0886      0.036     -2.455      0.014      -0.159      -0.018\n",
       "Diff.L8       -0.0085      0.036     -0.234      0.815      -0.080       0.063\n",
       "Diff.L9        0.0554      0.036      1.530      0.126      -0.016       0.126\n",
       "Diff.L10      -0.0796      0.036     -2.211      0.027      -0.150      -0.009\n",
       "Diff.L11       0.0404      0.036      1.122      0.262      -0.030       0.111\n",
       "Diff.L12      -0.0295      0.036     -0.822      0.411      -0.100       0.041\n",
       "Diff.L13      -0.0443      0.036     -1.234      0.217      -0.115       0.026\n",
       "Diff.L14       0.0794      0.036      2.209      0.027       0.009       0.150\n",
       "Diff.L15      -0.0428      0.036     -1.203      0.229      -0.113       0.027\n",
       "Diff.L16      -0.0585      0.034     -1.730      0.084      -0.125       0.008\n",
       "const         -0.0317      0.009     -3.436      0.001      -0.050      -0.014\n",
       "==============================================================================\n",
       "Omnibus:                      402.646   Durbin-Watson:                   1.995\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9003.792\n",
       "Skew:                          -1.529   Prob(JB):                         0.00\n",
       "Kurtosis:                      18.279   Cond. No.                         24.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf.regression.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 73\n",
    "\n",
    "Download data on consumer prices in the UK from the ONS.\n",
    "\n",
    "1. Test the log of CPI for a unit root. \n",
    "2. If you find a unit root, test inflation for one."
   ]
  },
  {
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   "execution_count": 7,
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    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
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     "execution_count": 7,
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    },
    {
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NGjWq1TWUl9fK/Ag6LoulueGONb7qRo9m/GO9qpu8GpoUoUeuGqLECHawxol7Bzga+gZm0Tswg76BGfQNzKJ3YAZ9AzM6Y98cqvlETmoG4gUXXCBJmjJlikaMGKEf/ehHWrx4sX73u98d9fq0tDQtWbJE99xzj37xi19IkoKCgnTjjTcqMjJSf/3rXxUV1bbdgQ1DneaLYsaxxvfMFzmqbvKqb3yonr5upJx2a5f+fUDbdfU/G2gf9A3MondgBn0DM+gbmEXvwAz6BmZ0xb45ZQvlTZ48WeHh4dq2bdtxrxsxYoTee+897d69W3V1derXr5+ioqI0b9482e12paamnqqSuqwD5Q16ZXOBJGnO+X3lZFdlAAAAAAAAtJM2JU8VFRW65JJLdNdddx1xzu12y+Vyyek89mu027dv1/+1d+dxUVf7/8BfMwybgiyiqIiiaKMigigqgmK4LwHuiaiYqZl5K1vcretyu1lfNbFcbpJeLXBBEpXUUMhdwS1SFAVJcENcWGQZmDm/P/zNp7g6phMwLK/n4+HDhzOfGc558PIz5/OezzknPDwchYWFUCqV6NSpE6ysrKBWq3Hs2DG4u7vDxMTkpTtR26z8JQ1qjUBP5/ro2tzG0M0hIiIiIiIiIqIa7KUKiLa2tjA2NkZsbCxSUlLKPBcWFoaSkhL07dtX5+svX76MTz/9FDExMWUeX7duHe7du4eJEye+THNqpePXH+DY9QdQyGV417eloZtDREREREREREQ13EtPYf7000/xxhtvYPz48QgKCoKdnR1OnjyJ/fv3o1OnTggJCQEAZGRk4OzZs2jWrBk6duwIABg4cCDCwsKwdOlS/P7772jWrBlOnjyJPXv2YNiwYejTp0+5dq4mKSpRI+LsTWw8nQEAGN3RAc1szA3cKiIiIiIiIiIiquleuoDYuXNnbN26FatWrcLmzZtRWFgIR0dHvPvuu3jzzTelKcgJCQmYM2cOhg4dKhUQ69Spg++++w5fffUVoqOjkZOTg+bNm+OTTz7B66+/Xr49qyFK1RrsvZiF/5z4HXfyigEA7Rtb4k2vZgZuGRERERERERER1QYyIar3vjDZ2dVna+yXUarR4OD1R/j60FXczn1SOLS3NMXbPk4Y0LYh5DKZgVtIVZVMBtjZWdbY/xtUMZgb0hezQ/pgbkgfzA3pi9khfTA3pI/qmBttm/9Kue3CTOVrzdF0/DchEwBgW8cY4zwdMcKtMcyMjQzcMiIiIiIiIiIiqk1YQKyilPYWcHO0Rt/W9RHQvhELh0REREREREREZBAsIFZR/ds0xFgf52p12ysREREREREREdU8ckM3gIiIiIiIiIiIiKouFhCJiIiIiIiIiIhIJxYQiYiIiIiIiIiISCcWEImIiIiIiIiIiEgnFhCJiIiIiIiIiIhIJxYQiYiIiIiIiIiISCcWEImIiIiIiIiIiEgnFhCJiIiIiIiIiIhIJxYQiYiIiIiIiIiISCcWEImIiIiIiIiIiEgnFhCJiIiIiIiIiIhIJxYQiYiIiIiIiIiISCcWEImIiIiIiIiIiEgnFhCJiIiIiIiIiIhIJxYQiYiIiIiIiIiISCeFoRvwd8lkhm5BxdD2q6b2jyoOs0P6YG5IX8wO6YO5IX0wN6QvZof0wdyQPqpjbl60rTIhhKjYphAREREREREREVF1xSnMREREREREREREpBMLiERERERERERERKQTC4hERERERERERESkEwuIREREREREREREpBMLiERERERERERERKQTC4hERERERERERESkEwuIREREREREREREpBMLiERERERERERERKQTC4hERERERERERESkEwuIREREREREREREpBMLiERERERERERERKQTC4hERERERERERESkEwuIREREhPz8fNy9exf5+fnSY0IIA7aIqovc3FysWLEC586dAwBoNBoDt4iqg/z8fKSnp+PBgwfSYzzn0IvIzc1FQkICUlNTcf/+fQDMDv21nJwcxMXFISkpCXfv3gXA3NBf4/i4LIWhG1BbpKamwsbGBtbW1pDLWbelF3PmzBnUrVsXNjY2sLe3N3RzqBo5duwYTE1NYW9vD0dHR0M3h6q4r7/+GpGRkVCr1ZDL5Rg/fjz8/f1Rv359QzeNqoGdO3di3bp1yM/Ph6urKxQKBYQQkMlkhm4aVVGhoaHYsWMHVCoVNBoNRowYgfHjx8Pe3p7ZoecKDQ1FeHg4SktLUVhYiNatW2Pu3Lno3LmzoZtGVdg333yDTZs2QQiBgoIC1K9fH8uWLUPXrl0N3TSqwjg+fhoLiBUsJiYGq1evxqNHj1BYWAg/Pz+MHj0aXbp0QWlpKRQK/groaTExMVi5cqWUm4YNGyIkJATBwcEcVNNzxcbG4t///jdycnJQVFQEU1NTTJ06FZMnTzZ006gKevz4MebPn49Tp04hICAApqam+PXXX/H5558jPj4eq1atgpWVlaGbSVVcamoqAOD06dOIj49Hnz59DNwiqqru3buHefPm4cKFCxg2bJh0ztmwYQPS0tKwZs0ajnPome7evYsFCxbgwoULGDFiBBwdHXHr1i1s2bIFH3/8MT777DMWg+iZvvzyS0RFReH111+HUqnE77//jujoaBw/fhxt27ZFvXr1DN1EqmI4PtaN1asKolKp8PXXXyMsLAyDBg1CkyZNcOfOHURFReH06dM4cOAAzM3N+S0rSYQQUKlUWL16NTZt2oQBAwagffv2EEIgPDwcS5cuhampKYYOHQpjY2NDN5eqoJiYGCxduhQ+Pj7w8vJCSUkJtm/fjvPnzyMjI4N3ItJTLl68iLi4OEyZMgXjxo2DpaUl1Go1QkNDsW7dOnzxxReYPn06GjdubOimUhWk/SJUo9HAysoKV69eRXR0NDp16gQbGxtoNBrOuqAyTp48iWPHjuHdd99FUFAQLCwsUFxcjClTpiAuLg6//vorOnToYOhmUhV05MgRnDp1CjNnzkRgYKB08e7s7IxPPvkE8fHx6Nq1K6+tqIyMjAxs374dfn5+mDRpklQsHDp0KBQKBerVq8fPKnoKx8e6sYBYQW7cuIHIyEj06tULM2fOlKaf2tjYICwsDBEREZg4cSI/4Egik8lw/fp1REVFYcCAAZg5cyYaNWoEAHBxccHChQuxa9cuvPbaa1AoFMwOSYQQKC0tRXh4OGxtbTFt2jQ4OTkBAHr27Am1Wo0mTZpwUE1PSUpKQlFRESZMmIC6detKBaExY8YgNzcXW7duRatWrRAcHMw75ukp2kwkJydjxIgRyM7Oxq5du+Dj44NRo0bxgozKUKlUOHr0KGQyGcaNGwdzc3OoVCqYmpqiT58+OHXqFNRqtaGbSVWQSqXCL7/8AisrK0yYMAEAoFarYWRkhLZt20Imk+HIkSOYNWsWxzlURkpKCnJychAcHFzmTkNbW1vcv3+fxUOSro/+fJ3E8bFu/N9SQc6ePYvs7GyMGjUK9vb2KCkpAQCMHDnSwC2jquzEiRPIzs5GSEgIGjVqJC1Er1QqUb9+fZw5cwZ3797l4IjKkMlkyMnJQUJCAvz9/eHk5CRdhGnPPxqNplYv+EvPlpGRARMTE1y7dg0AYGRkBOBJbsaOHQsnJydERkbi0qVLhmwmVVEajQbFxcWQyWQwMzPD22+/DVNTU+zcuRPXr1+XjiECABMTE+Tm5qK0tBQnTpwAAGk8k5KSgnbt2sHR0VH6rOJnVu3252KyiYkJsrOzoVarcevWLQB/fF61bt0aFhYWMDc3R15eHnNTy/3vlxDp6ekAgOzsbOmxVatWwc/PD0FBQQgICMDGjRuRl5dXmc2kKkRbp/nz9fWNGzc4PtaBBcRy8KzBcWlpKQDgwoULACBNOb1w4QJsbGzg5ubGDzh6SkFBAYAnu4QBgFwuh1qthqWlJaytrVGnTp0yO0ARad24cQMApHwYGRlhz549GDBgAMaOHYtBgwbh008/lXado9rj6NGjWLx4MUJDQxEXF4eHDx9Kz3l4eEClUiEzMxMApG9gAcDJyQkTJkzA1atXceTIERQVFRmk/WQ4urKjHffI5XKYmJjgxo0baNy4MZo3b47g4GCcP38ee/bskY5RqVQG6wNVvuedc0aPHg3gyYYGp0+fRlZWFqKiohAVFYWsrCxMmDABH374IS5fvmyo5pMBZWRkwN/fH48ePYKRkVGZLz9dXV3RokUL6UJe6+rVq8jOzkadOnVgaWnJL9lroWflRvs51a5dOwBAVlYW7t27h0mTJiEiIgI9evRAt27d8PjxY/z73//GV199Je0Iz2v02uHw4cP4xz/+gZkzZ2LOnDk4fvy49Lvv2LEjx8c61K77LctZeno6tmzZAgBo1KgRvLy84OLiAgAYMGAAvvrqK2zevBlNmzZFp06dcPfuXaxZswZ5eXlYuHAh7O3tMXLkSPTp04c7FtYiGRkZiI6OlnZX7tKli7R+QsuWLeHg4FBmfUwjIyMIIXDt2jUoFAo4ODgYuAdkKM/LziuvvIK6desiKysLeXl5+Pbbb/Hdd9+hV69esLGxQVJSErZt24bbt2/jvffeg4uLC885NdyjR4+wZMkS/Pzzz9I6vIWFhXB1dcXKlSvh4OCAVq1awc7ODjExMejfv3+Z5RGMjIzQs2dPeHh4YN++fRg+fLi0rALVbC+SHa3MzEwYGRnBxsYGABASEoKffvoJe/bsga2tLRISEjBw4ED079/fUN2hSvJXuWnSpAl69eqFqVOnYs+ePZg2bRoUCgUKCgowePBgNGrUCGlpadi7dy8uXryIuXPnomfPnobuFlWiS5cuISUlBatXr8b8+fMB/HFX0NSpU2FqagoLCwsAf0w7TExMhBAC3t7eAMApqbXQs3KjzUCzZs3QtGlTnDx5EtbW1vj999+xfPlyuLu7w8zMDKmpqVi5ciW2bt0KpVKJkSNHcmxcw92/fx+ffvop4uLi8Morr0CtVuP69evYt28f5s+fj4CAALRp04bjY10EvTS1Wi1WrVolXFxcRO/evYW3t7dQKpXCzc1NJCQkSMcdOHBADB06VLi4uIgePXoIpVIp3njjDfHll1+KefPmCS8vL+Hi4iK+//57UVJSYsAeUWVQq9Vi5cqVwtXVVQwePFjKhJeXlzhw4IAQQojCwkJx7969p1575coV0bFjR/HGG28IIYQoLS2t1LaTYT0vOz///LMoKioSQggxffp00bt3b3Hu3DkxePBgsXHjRpGXlyeEEOLhw4ciNDRUKJVKsWTJElFcXGzILlEl+P7774Wnp6cIDw8XN27cEGq1WoSGhopOnTqJsWPHiuTkZFFcXCxmz54tlEqlOH78uPRajUYjhBBCpVKJ7777rszz2ueo5npedoKDg8Wvv/4qHZueni5cXFzK5CcsLEwolUqhVCpFSEiI9B5Us/1Vbs6dOyeEeDKGefz4sQgNDRVeXl7i+++/Fzk5OdL77N69W3Ts2FHMmzdPFBYWGqg3ZAjazxulUikuXrwohHjyOfQs2mund999V7Rv3166BuNnVO3zvNzk5eWJBQsWCKVSKV599VWxYMGCp16fnJwsPDw8xMcff8zxcQ1XWloqli1bJrp06SK2bdsmMjMzhRBCHD16VAwePFj0799fJCcni/z8fI6PdeDXM3qIi4tDWFgYgoODsW7dOhw+fBhLly6FTCbDv/71L+m4vn37Ytu2bVi2bBkUCgVGjRqFzz77DB988AGWLFmCVatWoUWLFtixYwenFdYC+/btw/r16zFixAh8+eWXOHz4MNasWYOmTZti/vz5iI6OhpmZGezs7KTXaG+/T0hIQEFBATw9PQHw1vra5nnZmTdvHg4cOAAA6NGjB27duoWZM2fC1NQUEyZMkL6pr1evHkJCQtChQwecOHEC9+/fN2SXqIIVFRVh8+bNaNeuHUaOHAlHR0fI5XKEhITggw8+wJkzZ/Cf//wHJSUl8Pf3h6WlJUJDQ1FYWAjgj6kaxsbGUCqVUCgUOH78uPQc1Vx/lZ3ExERs2rQJWVlZAIDr16/D2NgYLVu2RGlpKVavXo2VK1fC3Nwc5ubm8Pf3l96Daq4Xyc2WLVuQlZUFIyMj5ObmIiIiAg0aNMDo0aOlzQ00Gg26du0KJycnnDlzRloCiGo2IQRyc3MRFRUFKysrKBQKLFmyBAB0ZkChUCAnJwdHjx6Fk5MT2rdvD+CPzyhuyFPz/VVuhBCwsLCAr68vWrZsiVu3bj214YUQAk2aNIGzszMuXrwIExMTQ3SFKklWVha2b9+OHj16YOTIkdKMik6dOsHf3x/p6enIzMxE3bp14efnBysrK46P/wdHcy9JrVZj586dkMvlmDhxIpydnSGXyzF8+HD4+Pjg0qVLuHr1KoAnJyS5XI7du3cjKysLY8eORcOGDaX3cnFxQdeuXXHp0qVaN3e+tlGpVIiKikLdunXx1ltvoU2bNgAAX19ffPzxxzA2NkZYWBgSExMBlF1fCnhSQDIxMYGPjw+AP3a+VKvVLCbWcC+Snf/85z+4dOkSXn31VXh6euLWrVsoKSmR1h3TTucxMzODp6cnrl27Jq3TSjWPEAIFBQV4/PgxGjZsWGYdKQsLCwwbNgz+/v7Yu3cv9u7di27duiEgIABnz55FeHi4lBttRlxdXaFWq8t8uUE104tmJyYmBvv37wfwZCqPqakp9u7di4EDB2LLli2YMWMGli5disLCQsTGxkpfWHBDlZpJn9wYGxtDpVKhefPmAJ6cb9RqNeRyORo0aICSkhKYmZlx3edaQiaT4ejRo7h+/TomTZqEV199FWfPnkVMTAwA6ByzHDhwAPn5+ejfvz/MzMwAAMXFxTh//jyio6Nx8+bNSusDVb6/yo12cwxvb2907doVCoUCDx8+xJ07dwA8yZVMJkO9evVQWFiIunXrcjOVGu7mzZvIzc1Fy5YtAfxxU46ZmRnMzc0BAMnJyQCAnj174rXXXuP4+H+wgPiS8vPzcfv2bVhbW6N+/foAIFWkTUxM0Lx5c2kwJJPJIJfL8fjxY9jb28Pa2hpCCGkArQ2psbExHj9+bIDeUGV58OABLly4gKZNm0pFZO1AWalUws/PDykpKdi1axcKCgogl8ulE1pqaioSEhLg6ekpfbuq0Whw/fp1REdH4+LFiwbrF1W8F8nO1atXER4eDjs7OwwePFg61/zvIvQKhQLFxcUwNzfnOaeGePjwIZKSknDz5k1pYCOTyWBubg6NRoM7d+4gOzu7zN1fpqamePvtt1GvXj1s374dWVlZCA4OhqenJ9asWYOff/4ZwJPPppKSEkRHR0MIAScnJ0N0kSrI38mOhYUF9uzZg7t37yI7OxuPHj3C2rVr0blzZ2zatAkTJkzAoEGDMHz4cBw8eBCRkZEAwLsQa4C/m5u9e/ciPT0dGo0GzZo1Q1JSErKzs6FQKGBkZISioiL89NNPSE9PR9++fWFlZWWorlI5e1Z2tNdERUVF2LBhAxwcHDBlyhS8+eabsLCwwOeffw4A0lrxfyaEwKFDh2BiYiKtlZmWloZNmzZh8uTJCA0N5ZelNcDfyY2JiQlKS0thZmaGYcOGwcPDA/Hx8fjpp5+gVquhUCigUqlw6NAhZGRkoGfPnrC0tDRYX6n8PCs3ANC0aVMATzYsLSwshEwmk84T2oKzs7MzgCefXcOHD0fXrl05Pv4TbqLyHKmpqbCysoKlpSVMTU0BAFZWVmjYsCEuXbqEbdu2ISgoCObm5khNTUVSUhKcnZ2xY8cOtGrVCl26dIFKpYKTkxNOnz6NtLQ02NvbQyaToaSkBKmpqYiLi0P37t2lzVeo+ntWbho2bAgbGxvI5XLcunULTZo0kQbXlpaWqFevHjQaDRISEnDixAn07t1bug36xIkTAIBXX30VAHDnzh0kJCRgw4YNuHz5MtauXWuAXlJF0Dc7QgicOnUKJ0+exJAhQ/Dbb79h27ZtiIyMRP369eHg4ACNRoO0tDQcPXoUnp6eaN26tSG7SuVgxYoViIiIAPDkTtUePXpg5syZcHJygrm5OXr27ImDBw/i+vXrsLOzKzO1onnz5hg/fjxWr16N+Ph4jB49Gh999BFmz56NuXPnIjk5GR4eHnjw4AHCw8Ph7e2NTp06GaqrVM7KKzu//PILfH19MWbMGPj5+aFTp06oU6eOdOzEiROxb98+WFtbV3YXqQKUV25OnTqF0aNHo1+/fli5ciU+/PBDjB8/Hra2trh+/To2bNiA1q1bc+OdGuR52QEgFXi000fd3NwwdOhQbN68GatXr8Y777wDjUYj7cAshIBKpcKVK1fg7OyMevXqYdeuXdi4cSMuX76MkJAQzJo1yyB9pfJTHrnRFp5dXV0xffp0LFq0CJ9//jlSUlLg5uaGwsJCREZGwtHREX379jVIP6l8PS83NjY2WLx4MTp37ixda2k/q7Q35TRu3FjapKlt27Z4//33MW/ePI6P/z+Z4PzHp+zatQvr16+HSqXC/fv34e7ujlGjRqFfv36Qy+W4cuUKQkJC8PDhQ3h7e8POzg7x8fEQQsDa2ho3b96EkZERJk2ahKlTp+Ls2bP46KOPYGJigjfffBMdOnRAZmYmdu7ciaSkJCxZsgR9+vThjqjVnK7cDBgwACUlJVi8eDF27tyJ5cuXo1evXjAxMYFKpYKJiQk++ugjZGVl4dSpUwgKCsIHH3yAunXrAgA++ugjHDhwAGvXroWRkRHCwsIQHx+PXr16YeHChWjSpImBe05/V3ll5/XXX8eCBQuQkZGBFStWYP/+/fDw8EBgYCCKiopw7NgxnDt3DosXL0b//v15zqmmtJmIi4vD6NGjYW9vj+TkZOzcuRONGjXCnDlz4Ovri9jYWLz//vsYO3Ys3nvvPWl6l1ZGRgbGjRsHV1dXrFixAgqFAqmpqfjiiy8QHx8vDax69OiBf/7zn9Jd91R9lWd2goOD4ebmhuXLl6O0tBSmpqbS+eTP55aCgoIyRUWqfsr7nOPi4oKvv/4a9+7dw65du7B27Vrk5+fDysoKRUVF6N69OxYtWoQGDRoYqMdUXp6XncaNG2P27Nnw9fUFAGlco5WWloZp06YhIyMDhw8fhp2dHdRqtVREzMzMRL9+/WBvb482bdogLi4Ovr6++Oc//1m7dkStgco7NyUlJdJamqmpqVixYgXi4+NhbGwMU1NTdO7cGQsXLiyz1BhVPy/6WfUsubm5CAwMRGlpKWJjY2FiYlJmLJOWloZly5ZxfAxwF+Y/0+4W5+LiIubMmSN++OEHsX79ejFo0CDh4uIi1q1bJ+1oeuXKFbFx40YxceJEoVQqxeLFi0VycrLIzc0ViYmJ4o033hAuLi5i27ZtQggh9u/fL/r27SuUSqXw8PAQHTt2FEOGDBGJiYmG7DKVg+flpl27dmLdunVCpVKJEydOiB49eoiBAweKPXv2CJVKJYqLi0VUVJTw8fERFy5cEDNnzhT9+vUTjx49kt7/tddeEy4uLmLGjBnCw8NDDBgwoMxuUFR9lXd2+vTpI3Jzc4UQQhQXF4tPPvlEdO/eXbRr10507dpVDB06tMxO8VQ9Xb58Wbi7u4vFixeL/Px86fH4+HjRpUsXERgYKC5duiTy8vLEpEmTRJcuXcSZM2ek47S7xOXl5YnZs2cLT09P8fjxY+l5lUolUlJSxIkTJ0RycnLldYwqXEVnh2qmis5NUlKSiI2NFTt27JB2UKWa4a+yM3ToUHH+/HkhxJPdUYUou5Pphg0bhFKpFDNnznzqucuXL4tu3boJpVIpXnvtNXHs2LHK6BJVgvLOjVqtLvO3EELcvHlTXL16VaSlpVV4f6hy6JMbrd27dwulUimWLFny1PPa3BQXF3N8LIRgAfFPbt68KXx8fMSYMWOkLb2FEOLGjRti3Lhxwt3dXfzwww/S4/n5+SIkJEQMHTpU3Lt3r0zQrl27Jtzd3cWsWbNESUmJEEKIjIwMERcXJ6KiokR8fHzldYwq1F/lxs3NTezcuVMIIcSPP/4o3NzchFKpFIMGDRK9e/cWbdu2FaGhoUIIISIiIoRSqZRObrm5uSIkJEQolUrRpUsXsWnTpsrvIFWYisjOuXPnyvyM7OxscePGDZGUlFRp/aKKFRMTI5RKpUhNTRVClB3khIeHS4NmjUYj9u/fL9zd3cWMGTPE3bt3hRBPBtnagfby5ctF+/btRUpKSuV3hCods0P6YG5IXy+Snffff1+6VtLmRHvBnpOTI4KDg4VSqZSK0iqVSgghxJ07d0RISIiIjIystP5Q5aiI3GiPpZpLn9xoj/nwww+Fi4uLOHz4sPQajUYj7ty5IzIyMiqxF1UfV7PGH7vvJCcn4969exgzZgwcHBykHW4dHR3xzjvvoLi4GD/88ANSU1MBAGfPnsWJEyfg5uYGOzs7GBkZQa1WAwBsbGxgY2ODrKwsacfcpk2bolevXggMDNR5+yxVHy+aG5VKhQ0bNiA1NRUBAQH46quvMG7cOFhbW8PT0xNhYWGYPn06AKBOnTpQKBTSrtyWlpZo0KABpkyZgqNHj2L8+PEG6y+Vn4rMzp8XCgaA+vXrw9HRUdqAh6qXn376Cbt27cIvv/wiLRqem5sLANImOX+ehv7666+jc+fO2Lt3L2JjY+Hn54dhw4YhNjYWUVFRyM/Ph0wmk17z8OFDmJub17od5GoDZof0YajcCK6oVO3pm52YmBjs27evzHvJ5XJoNBrUq1cPQUFBkMlk0sYYxsbGUKvVsLe3R1hYGIYNG1YZ3aMKUlm50V6PU81QXrnRbt51/PhxODg4oHPnzgCebGK5b98+zJgxA+vXr0d+fn5lda3Kq7X/kw4fPgwHBwc4OztL4bp9+zYA4P79+wAgrbEBPFn8uU2bNrh27RrCw8Mxf/58NGzYEObm5igsLER+fj4sLCzKrMuRlZUlLcaq0Wi4+2ANoG9utLvkzp8/H76+vvD19S2zjovWw4cPUVpaWmYNjiVLlpRZ24Oqp8rKDteMqhliY2Pxr3/9C/n5+SgqKoJKpUL37t3x3nvvSYObc+fOoU+fPtL5QZuLOXPmYPjw4di+fTu6d++OyZMn4/r161i9ejUKCgrw/vvv4/79+7h48SLi4+MxYsQI2NjYGLK7VI6YHdKHoXPD9Xirr/LIzu7du+Hj4wNra2tp3TFtJgYOHIi4uDhER0cjPDwcY8aMkcZAzE31ZYjcUPVX3rkBgMTERNy/fx8DBgyAubk5Tp8+jfDwcOzfvx8dOnTA5MmTYWFhYaguVzm1soCoUqnw1ltvISAgAPPmzZMC4e7uDhMTE6SkpODBgwewtbUtc6F++/ZtCCFw5MgRXL58GU2aNEHnzp0RHx8PLy8vBAQEQKVS4erVq1i9ejUcHBwQGBgIACwe1gB/JzcAcOTIESQlJcHV1VXKSXZ2tlQQunjxInbu3Al/f3+0aNFC+rksHlZ/hsoOVU+RkZH4/PPP4ePjgwEDBsDS0hK//fYb/u///g/5+flYsWIFPD09cfz4caSlpaFNmzYAnhSgNRoNXFxcMHjwYBw8eBCJiYnw9fXFF198gbfffhvr1q1DZGQkGjdujIyMDDg6OmL48OEG7jGVF2aH9MHckL7KKzuHDh3C+fPn0atXL6kAJJPJpBswRowYgRs3bsDZ2dmQ3aVywtyQPioiNwBw/vx5AICFhQXWrVuHtWvXwtraGqGhoejdu7eBelt11coCYmZmJjQaDQ4dOoTBgwfD29sbMpkMDRo0gI+PDw4dOgQPDw+MGDFCupA/evQounbtCm9vbyxYsACnT5/G+PHjMW7cOCxZsgSzZs1CWFgYbG1tkZmZiYcPH2LhwoVo27atgXtL5aU8cnP27Fm4urrCyMgI27dvR0REBDp27IjmzZvjwoUL0Gg0GDVqlIF7SuWN2aEXlZ+fj82bN6NJkyb4xz/+AScnJwCAl5cXMjIysG3bNhw/fhz+/v5YsGABDh8+jObNm8Pc3LzMbnFBQUHYv38/0tLS4Ovri/r16+Obb77BqVOncPLkSeTk5CAgIADBwcEG7C2VJ2aH9MHckL7KOzvp6ekAys7a0v7dpUsXREREVHofqfwxN6SPisoN8MfNOuvWrYOxsTHeeecdTJ06tbK7WG3UqtvitPPjExMTATyZJ79x40Y8ePAAAGBvb49x48ZBoVBg/vz5WLBgAf773/9i6dKlWLBgAZo3b46AgAA4ODjgwoULAICePXtizZo1GD9+PBo0aACZTAY/Pz/Ex8fD39/fMB2lclWeuUlKSgLw5JuQSZMmISgoCHK5HKmpqejWrRt27twp3X5N1R+zQy8rLy8Ply9fxrBhw+Dk5ASNRoPS0lIAKPP77dmzJ1xdXbFt2zb89ttvAFBm6k7Dhg3RoEEDnD17VnpN/fr1MWjQICxatAgrVqzghXwNw+yQPpgb0ld5Z+fMmTMAOGurpmNuSB8VlRvgyRRnuVyOMWPG4MSJEywe/oVadQei9sQSHR2NJk2awMvLC5GRkYiLi0NgYCAUCgW8vLzw5ZdfYvny5di3bx+2b98OBwcHLFiwAGPGjEFpaSmcnZ2lxTkBwNnZGXPnzoUQAoWFhahTp46hukgVoDxzk5ycLL2vo6MjFi5ciKKiIhQXF8PKyspQXaQKwuzQy7p16xYA4NKlSwCeZEg76Hn8+DGAJ+td2tvbY/z48Zg3bx62bt2KRo0awdHRESqVCiYmJnB0dERxcfFT0+K1ONCueZgd0gdzQ/qqrOxQzcLckD4qIjclJSUwNjZGv379MGTIEDg6Ohqmc9VMrSogAsCWLVuQmJiIFStWoEWLFjh37hy+++47dOnSBc2aNQMAdOvWDWFhYbh16xYKCgrQrl07aeCjUCiQm5sLBwcHAChzS6xMJmPxsIaqyNyYmZnBzMzMMB2jCsfs0Mvw8PCAu7s7PDw8pMGO9vet3WzH1dUVAODr64sxY8Zg06ZNsLa2xty5c2FiYgKVSoXY2Fjk5OTA3d2dg+pagtkhfTA3pC9mh/TB3JA+KiI3xsbGAMA1Ml9SrSsgtmvXDi1atICPjw/MzMwQGBiI5cuXIyoqCtOmTYOJiQmEELCwsMArr7yCx48fo7S0FHXq1EFpaSkSEhJw+fJl6dZW7v5VOzA3pC9mh16GTCbDxo0bnyoMl5SU4NSpUzA3N0fr1q0BAPXq1cOsWbOQkpKCLVu24Pbt2+jcuTNkMhm2b98OV1dXdO/e3RDdIANgdkgfzA3pi9khfTA3pA/mpuqQCSGEoRtR2R4+fAgbGxsAQGpqKhYtWoQrV65g/fr16NChg3RcSkoKVq5cCTs7OwQGBuLOnTuIjIzEgwcPsGrVKt7mWsswN6QvZof0pV0U/Ndff8XYsWPh7e2Nr7/+WtplUKFQ4M6dO4iOjsb69eshhIBCoYC3tzcWLlwIa2trQ3eBDITZIX0wN6QvZof0wdyQPpgbw6l1dyACgI2NjbRWgrOzMwIDA7Fo0SJs2bIFCxcuhIWFBQCgVatWKCwsxLZt27B7927IZDK0atUKn332GS/kayHmhvTF7JC+tFPZExISUFJSAl9fX2mqjlwuh0qlgq2tLaZMmYKAgAAUFBTAyMhImh5PtRezQ/pgbkhfzA7pg7khfTA3hlMrC4jAk51MtWuJeXt7o0+fPoiJicGAAQPg5+cHIQTkcjlCQ0ORmZmJzMxM1K1bF15eXoZuOhkQc0P6YnZIXyqVCnv37oVCocDAgQMBPPnmNSMjAwcPHkRubi7ee+892NvbG7ilVNUwO6QP5ob0xeyQPpgb0gdzYxi1toAI/LGWWMOGDTFkyBCcOnUKYWFhcHV1RYMGDQAAFhYWaNOmDdq0aWPIplIVwtyQvpgd0sfly5eRnp6OXr16wdraGnfu3EFiYiLCwsJw6dIlzJgxw9BNpCqK2SF9MDekL2aH9MHckD6YG8Oo1QVE4I8dTTt37ozAwEB8++232L17NyZMmMAdnUgn5ob0xezQi9Jm5caNGygoKICDgwMSEhKwceNGHDx4ED4+PoiNjUXTpk0N3VSqYpgd0gdzQ/pidkgfzA3pg7kxrFpfQJTJZBBCoG7duvDz88OuXbtw+vRpBAUF8WKedGJuSF/MDr0o7R2r165dAwAkJSUhMjIStra22LBhA7y9vQ3ZPKrCmB3SB3ND+mJ2SB/MDemDuTGsWl9ABP4IoZubG7799ltpC3Ci52FuSF/MDr2MRo0aAXiyg/eMGTMwYcIEA7eIqgtmh/TB3JC+mB3SB3ND+mBuDEMmhBCGbgQRERE92/nz53H69GmEhITAxMTE0M2haoTZIX0wN6QvZof0wdyQPpgbw2ABkYiIqArTrvVC9LKYHdIHc0P6YnZIH8wN6YO5MQwWEImIiIiIiIiIiEgnuaEbQERERERERERERFUXC4hERERERERERESkEwuIREREREREREREpBMLiERERERERERERKQTC4hERERERERERESkEwuIREREREREREREpBMLiERERERERERERKSTwtANICIiIiLDCQ0NxerVq8s8JpfLYWZmBgcHB/Tq1QuTJk2CjY3NM18vhEDv3r1x8+ZNzJgxA++8806Z55VK5Qu35cqVK8jMzETv3r3/8lhLS0skJia+8HsTERERkf5YQCQiIiIijB49Gp06dQIAaDQa5Obm4sKFC9iwYQN+/PFHbNmyBU5OTk+97uTJk7h58ybq1KmDbdu2Ydq0aTAyMpKeX7ZsWZnjz5w5g61bt6Jv377o27evzva0bNkSb731ls7njY2NX7KHRERERKQvFhCJiIiICO7u7ggICHjq8aFDh2Lq1KmYOnUq9u7dC4Wi7PBxx44dMDY2RkhICL755hvExcWhT58+0vP/+55qtRpbt26FUql85s/TsrOze+7zRERERFR5uAYiEREREenUo0cPhISEID09HdHR0WWey8vLQ2xsLDp06AB/f38AQHh4uCGaSUREREQViAVEIiIiInquESNGAAAOHjxY5vE9e/agqKgI3t7eaNGiBZRKJY4dO4aMjAxDNJOIiIiIKggLiERERET0XC1atICZmRkuXrxY5vHIyEgAwMCBAwEAgwcPhhACERERf/tnlpSU4MGDBzr/FBcX/+2fQUREREQvhgVEIiIiInoumUwGKysrPHz4UHrs6tWrSEpKQrt27dCyZUsAwKBBgwAAO3fuhEql+ls/89y5c/Dy8tL5h1OliYiIiCoPN1EhIiIior9UUlJS5t/auw8HDx4sPebo6Ah3d3ecP38eBw4cwJAhQ/T+eUqlErNnz9b5fIsWLfR+byIiIiJ6OSwgEhEREdFzlZaWIi8vDw0bNgTwpJio3VDFzc0NmZmZ0rHdunXD+fPnER4e/rcKiFZWVujevfvfazgRERERlQsWEImIiIjouZKTk1FSUoL27dsDAOLj43H//n0AQHBw8DNfk5iYiGvXrqFVq1aV1k4iIiIiqhgsIBIRERHRc2nvNuzfvz+AP6YvT506FR06dHjq+O3btyM+Ph4RERGYP39+5TWUiIiIiCoEC4hEREREpNPp06cRHh6OVq1aoX///rh37x6OHDkCa2trTJ8+Haampk+9pnHjxoiPj8ePP/6IDz74AObm5gZoORERERGVFxYQiYiIiAjnz5+HkZERAEAIgZycHGkzFFtbW4SGhkKhUODHH39EaWkphg0b9sziIQC4uLjA09MTCQkJ2LNnD0aOHPnS7cnOzsauXbuee4y3tzfs7Oxe+r2JiIiI6OWwgEhERERE2Lp1K7Zu3QoAkMlkqFOnDpycnDB58mRMmDABNjY2AICoqCjIZDIEBQU99/1CQkKQkJCAiIgIvQqIaWlp+Pjjj597zH//+18WEImIiIgqgUwIIQzdCCIiIiIiIiIiIqqa5IZuABEREREREREREVVdLCASERERERERERGRTiwgEhERERERERERkU4sIBIREREREREREZFOLCASERERERERERGRTiwgEhERERERERERkU4sIBIREREREREREZFOLCASERERERERERGRTiwgEhERERERERERkU4sIBIREREREREREZFOLCASERERERERERGRTiwgEhERERERERERkU7/DyLztBZtLGj7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "cpi = pd.read_excel(\"data/uk-cpi-ons.xlsx\", index_col=\"DATE\")\n",
    "lncpi = np.log(cpi)\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"font\", size=14)\n",
    "\n",
    "lncpi.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Augmented Dickey-Fuller Results</caption>\n",
       "<tr>\n",
       "  <td>Test Statistic</td>    <td>-3.972</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>P-value</td>            <td>0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Lags</td>                  <td>14</td>\n",
       "</tr>\n",
       "</table><br/><br/>Trend: Constant and Linear Time Trend<br/>Critical Values: -3.98 (1%), -3.42 (5%), -3.13 (10%)<br/>Null Hypothesis: The process contains a unit root.<br/>Alternative Hypothesis: The process is weakly stationary."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lr}\n",
       "\\toprule\n",
       "Test Statistic     &             -3.972  \\\\\n",
       "P-value            &              0.010  \\\\\n",
       "Lags               &                 14  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Augmented Dickey-Fuller Results}\n",
       "\\end{center}\n",
       "\n",
       "Trend: Constant and Linear Time Trend \\newline\n",
       " Critical Values: -3.98 (1%), -3.42 (5%), -3.13 (10%) \\newline\n",
       " Null Hypothesis: The process contains a unit root. \\newline\n",
       " Alternative Hypothesis: The process is weakly stationary."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "   Augmented Dickey-Fuller Results   \n",
       "=====================================\n",
       "Test Statistic                 -3.972\n",
       "P-value                         0.010\n",
       "Lags                               14\n",
       "-------------------------------------\n",
       "\n",
       "Trend: Constant and Linear Time Trend\n",
       "Critical Values: -3.98 (1%), -3.42 (5%), -3.13 (10%)\n",
       "Null Hypothesis: The process contains a unit root.\n",
       "Alternative Hypothesis: The process is weakly stationary.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf = ADF(lncpi, trend=\"ct\")\n",
    "adf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "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>   0.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   35.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Aug 2025</td> <th>  Prob (F-statistic):</th> <td>3.66e-63</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:18:10</td>     <th>  Log-Likelihood:    </th> <td>  1668.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   366</td>      <th>  AIC:               </th> <td>  -3304.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   349</td>      <th>  BIC:               </th> <td>  -3237.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    16</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>Level.L1</th> <td>   -0.0200</td> <td>    0.005</td> <td>   -3.972</td> <td> 0.000</td> <td>   -0.030</td> <td>   -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L1</th>  <td>    0.0845</td> <td>    0.052</td> <td>    1.615</td> <td> 0.107</td> <td>   -0.018</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L2</th>  <td>    0.0731</td> <td>    0.052</td> <td>    1.406</td> <td> 0.161</td> <td>   -0.029</td> <td>    0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L3</th>  <td>   -0.0121</td> <td>    0.037</td> <td>   -0.325</td> <td> 0.745</td> <td>   -0.085</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L4</th>  <td>   -0.0073</td> <td>    0.037</td> <td>   -0.196</td> <td> 0.845</td> <td>   -0.080</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L5</th>  <td>   -0.0623</td> <td>    0.037</td> <td>   -1.680</td> <td> 0.094</td> <td>   -0.135</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L6</th>  <td>    0.1141</td> <td>    0.037</td> <td>    3.063</td> <td> 0.002</td> <td>    0.041</td> <td>    0.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L7</th>  <td>    0.0251</td> <td>    0.038</td> <td>    0.666</td> <td> 0.506</td> <td>   -0.049</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L8</th>  <td>    0.0085</td> <td>    0.038</td> <td>    0.226</td> <td> 0.821</td> <td>   -0.066</td> <td>    0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L9</th>  <td>   -0.0385</td> <td>    0.037</td> <td>   -1.030</td> <td> 0.304</td> <td>   -0.112</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L10</th> <td>   -0.0200</td> <td>    0.037</td> <td>   -0.535</td> <td> 0.593</td> <td>   -0.093</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L11</th> <td>    0.0230</td> <td>    0.037</td> <td>    0.614</td> <td> 0.539</td> <td>   -0.051</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L12</th> <td>    0.6888</td> <td>    0.037</td> <td>   18.498</td> <td> 0.000</td> <td>    0.616</td> <td>    0.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L13</th> <td>   -0.1277</td> <td>    0.052</td> <td>   -2.447</td> <td> 0.015</td> <td>   -0.230</td> <td>   -0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Diff.L14</th> <td>   -0.1137</td> <td>    0.052</td> <td>   -2.167</td> <td> 0.031</td> <td>   -0.217</td> <td>   -0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>    0.0819</td> <td>    0.020</td> <td>    4.009</td> <td> 0.000</td> <td>    0.042</td> <td>    0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>trend</th>    <td> 3.419e-05</td> <td>    9e-06</td> <td>    3.798</td> <td> 0.000</td> <td> 1.65e-05</td> <td> 5.19e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>93.523</td> <th>  Durbin-Watson:     </th> <td>   1.988</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>1069.844</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.695</td> <th>  Prob(JB):          </th> <td>4.86e-233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>11.260</td> <th>  Cond. No.          </th> <td>1.13e+05</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.13e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.619   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.601   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     35.38   \\\\\n",
       "\\textbf{Date:}             & Wed, 27 Aug 2025 & \\textbf{  Prob (F-statistic):} &  3.66e-63   \\\\\n",
       "\\textbf{Time:}             &     15:18:10     & \\textbf{  Log-Likelihood:    } &    1668.8   \\\\\n",
       "\\textbf{No. Observations:} &         366      & \\textbf{  AIC:               } &    -3304.   \\\\\n",
       "\\textbf{Df Residuals:}     &         349      & \\textbf{  BIC:               } &    -3237.   \\\\\n",
       "\\textbf{Df Model:}         &          16      & \\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{Level.L1} &      -0.0200  &        0.005     &    -3.972  &         0.000        &       -0.030    &       -0.010     \\\\\n",
       "\\textbf{Diff.L1}  &       0.0845  &        0.052     &     1.615  &         0.107        &       -0.018    &        0.187     \\\\\n",
       "\\textbf{Diff.L2}  &       0.0731  &        0.052     &     1.406  &         0.161        &       -0.029    &        0.175     \\\\\n",
       "\\textbf{Diff.L3}  &      -0.0121  &        0.037     &    -0.325  &         0.745        &       -0.085    &        0.061     \\\\\n",
       "\\textbf{Diff.L4}  &      -0.0073  &        0.037     &    -0.196  &         0.845        &       -0.080    &        0.066     \\\\\n",
       "\\textbf{Diff.L5}  &      -0.0623  &        0.037     &    -1.680  &         0.094        &       -0.135    &        0.011     \\\\\n",
       "\\textbf{Diff.L6}  &       0.1141  &        0.037     &     3.063  &         0.002        &        0.041    &        0.187     \\\\\n",
       "\\textbf{Diff.L7}  &       0.0251  &        0.038     &     0.666  &         0.506        &       -0.049    &        0.099     \\\\\n",
       "\\textbf{Diff.L8}  &       0.0085  &        0.038     &     0.226  &         0.821        &       -0.066    &        0.083     \\\\\n",
       "\\textbf{Diff.L9}  &      -0.0385  &        0.037     &    -1.030  &         0.304        &       -0.112    &        0.035     \\\\\n",
       "\\textbf{Diff.L10} &      -0.0200  &        0.037     &    -0.535  &         0.593        &       -0.093    &        0.054     \\\\\n",
       "\\textbf{Diff.L11} &       0.0230  &        0.037     &     0.614  &         0.539        &       -0.051    &        0.096     \\\\\n",
       "\\textbf{Diff.L12} &       0.6888  &        0.037     &    18.498  &         0.000        &        0.616    &        0.762     \\\\\n",
       "\\textbf{Diff.L13} &      -0.1277  &        0.052     &    -2.447  &         0.015        &       -0.230    &       -0.025     \\\\\n",
       "\\textbf{Diff.L14} &      -0.1137  &        0.052     &    -2.167  &         0.031        &       -0.217    &       -0.011     \\\\\n",
       "\\textbf{const}    &       0.0819  &        0.020     &     4.009  &         0.000        &        0.042    &        0.122     \\\\\n",
       "\\textbf{trend}    &    3.419e-05  &        9e-06     &     3.798  &         0.000        &     1.65e-05    &     5.19e-05     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 93.523 & \\textbf{  Durbin-Watson:     } &     1.988  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  1069.844  \\\\\n",
       "\\textbf{Skew:}          &  0.695 & \\textbf{  Prob(JB):          } & 4.86e-233  \\\\\n",
       "\\textbf{Kurtosis:}      & 11.260 & \\textbf{  Cond. No.          } &  1.13e+05  \\\\\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. \\newline\n",
       " [2] The condition number is large, 1.13e+05. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.619\n",
       "Model:                            OLS   Adj. R-squared:                  0.601\n",
       "Method:                 Least Squares   F-statistic:                     35.38\n",
       "Date:                Wed, 27 Aug 2025   Prob (F-statistic):           3.66e-63\n",
       "Time:                        15:18:10   Log-Likelihood:                 1668.8\n",
       "No. Observations:                 366   AIC:                            -3304.\n",
       "Df Residuals:                     349   BIC:                            -3237.\n",
       "Df Model:                          16                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Level.L1      -0.0200      0.005     -3.972      0.000      -0.030      -0.010\n",
       "Diff.L1        0.0845      0.052      1.615      0.107      -0.018       0.187\n",
       "Diff.L2        0.0731      0.052      1.406      0.161      -0.029       0.175\n",
       "Diff.L3       -0.0121      0.037     -0.325      0.745      -0.085       0.061\n",
       "Diff.L4       -0.0073      0.037     -0.196      0.845      -0.080       0.066\n",
       "Diff.L5       -0.0623      0.037     -1.680      0.094      -0.135       0.011\n",
       "Diff.L6        0.1141      0.037      3.063      0.002       0.041       0.187\n",
       "Diff.L7        0.0251      0.038      0.666      0.506      -0.049       0.099\n",
       "Diff.L8        0.0085      0.038      0.226      0.821      -0.066       0.083\n",
       "Diff.L9       -0.0385      0.037     -1.030      0.304      -0.112       0.035\n",
       "Diff.L10      -0.0200      0.037     -0.535      0.593      -0.093       0.054\n",
       "Diff.L11       0.0230      0.037      0.614      0.539      -0.051       0.096\n",
       "Diff.L12       0.6888      0.037     18.498      0.000       0.616       0.762\n",
       "Diff.L13      -0.1277      0.052     -2.447      0.015      -0.230      -0.025\n",
       "Diff.L14      -0.1137      0.052     -2.167      0.031      -0.217      -0.011\n",
       "const          0.0819      0.020      4.009      0.000       0.042       0.122\n",
       "trend       3.419e-05      9e-06      3.798      0.000    1.65e-05    5.19e-05\n",
       "==============================================================================\n",
       "Omnibus:                       93.523   Durbin-Watson:                   1.988\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1069.844\n",
       "Skew:                           0.695   Prob(JB):                    4.86e-233\n",
       "Kurtosis:                      11.260   Cond. No.                     1.13e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.13e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adf.regression.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "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>CPI</th>\n",
       "      <th>const</th>\n",
       "      <th>trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>3.879397</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-02-01</th>\n",
       "      <td>3.882615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-03-01</th>\n",
       "      <td>3.886028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-04-01</th>\n",
       "      <td>3.897518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-05-01</th>\n",
       "      <td>3.902558</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CPI  const  trend\n",
       "DATE                              \n",
       "1988-01-01  3.879397    1.0    1.0\n",
       "1988-02-01  3.882615    1.0    2.0\n",
       "1988-03-01  3.886028    1.0    3.0\n",
       "1988-04-01  3.897518    1.0    4.0\n",
       "1988-05-01  3.902558    1.0    5.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.tsa.api as tsa\n",
    "\n",
    "with_trend = tsa.add_trend(lncpi, trend=\"ct\")\n",
    "with_trend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE'>"
      ]
     },
     "execution_count": 11,
     "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 statsmodels.api as sm\n",
    "\n",
    "res = sm.OLS(with_trend[\"CPI\"], with_trend[[\"const\", \"trend\"]]).fit()\n",
    "\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "res.resid.plot.line()"
   ]
  }
 ],
 "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": "3.13.5"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}