{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markov switching dynamic regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an example of the use of Markov switching models in statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:10.313660Z",
     "iopub.status.busy": "2021-02-02T06:54:10.312685Z",
     "iopub.status.idle": "2021-02-02T06:54:12.495573Z",
     "shell.execute_reply": "2021-02-02T06:54:12.496928Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NBER recessions\n",
    "from pandas_datareader.data import DataReader\n",
    "from datetime import datetime\n",
    "usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept\n",
    "\n",
    "The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:\n",
    "\n",
    "$$r_t = \\mu_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\sigma^2$.\n",
    "\n",
    "The data used in this example can be found at https://www.stata-press.com/data/r14/usmacro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:12.503008Z",
     "iopub.status.busy": "2021-02-02T06:54:12.501321Z",
     "iopub.status.idle": "2021-02-02T06:54:13.252319Z",
     "shell.execute_reply": "2021-02-02T06:54:13.252727Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds\n",
    "dta_fedfunds = pd.Series(fedfunds, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "\n",
    "# Plot the data\n",
    "dta_fedfunds.plot(title='Federal funds rate', figsize=(12,3))\n",
    "\n",
    "# Fit the model\n",
    "# (a switching mean is the default of the MarkovRegession model)\n",
    "mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)\n",
    "res_fedfunds = mod_fedfunds.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:13.267490Z",
     "iopub.status.busy": "2021-02-02T06:54:13.257941Z",
     "iopub.status.idle": "2021-02-02T06:54:13.281614Z",
     "shell.execute_reply": "2021-02-02T06:54:13.281999Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>226</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-508.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 02 Feb 2021</td> <th>  AIC                </th> <td>1027.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>06:54:13</td>     <th>  BIC                </th> <td>1044.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1954</td>    <th>  HQIC               </th> <td>1034.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    3.7088</td> <td>    0.177</td> <td>   20.988</td> <td> 0.000</td> <td>    3.362</td> <td>    4.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    9.5568</td> <td>    0.300</td> <td>   31.857</td> <td> 0.000</td> <td>    8.969</td> <td>   10.145</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    4.4418</td> <td>    0.425</td> <td>   10.447</td> <td> 0.000</td> <td>    3.608</td> <td>    5.275</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.9821</td> <td>    0.010</td> <td>   94.443</td> <td> 0.000</td> <td>    0.962</td> <td>    1.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.0504</td> <td>    0.027</td> <td>    1.876</td> <td> 0.061</td> <td>   -0.002</td> <td>    0.103</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  226\n",
       "Model:               MarkovRegression   Log Likelihood                -508.636\n",
       "Date:                Tue, 02 Feb 2021   AIC                           1027.272\n",
       "Time:                        06:54:13   BIC                           1044.375\n",
       "Sample:                    07-01-1954   HQIC                          1034.174\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          3.7088      0.177     20.988      0.000       3.362       4.055\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          9.5568      0.300     31.857      0.000       8.969      10.145\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         4.4418      0.425     10.447      0.000       3.608       5.275\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.9821      0.010     94.443      0.000       0.962       1.002\n",
       "p[1->0]        0.0504      0.027      1.876      0.061      -0.002       0.103\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary output, the mean federal funds rate in the first regime (the \"low regime\") is estimated to be $3.7$ whereas in the \"high regime\" it is $9.6$. Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980's was a time-period in which a high federal funds rate existed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:13.302366Z",
     "iopub.status.busy": "2021-02-02T06:54:13.284615Z",
     "iopub.status.idle": "2021-02-02T06:54:13.534631Z",
     "shell.execute_reply": "2021-02-02T06:54:13.535037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Probability of being in the high regime'}>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds.smoothed_marginal_probabilities[1].plot(\n",
    "    title='Probability of being in the high regime', figsize=(12,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:13.538376Z",
     "iopub.status.busy": "2021-02-02T06:54:13.537682Z",
     "iopub.status.idle": "2021-02-02T06:54:13.543212Z",
     "shell.execute_reply": "2021-02-02T06:54:13.543623Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55.85400626 19.85506546]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept and lagged dependent variable\n",
    "\n",
    "The second example augments the previous model to include the lagged value of the federal funds rate.\n",
    "\n",
    "$$r_t = \\mu_{S_t} + r_{t-1} \\beta_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:13.553080Z",
     "iopub.status.busy": "2021-02-02T06:54:13.552137Z",
     "iopub.status.idle": "2021-02-02T06:54:13.960529Z",
     "shell.execute_reply": "2021-02-02T06:54:13.960125Z"
    }
   },
   "outputs": [],
   "source": [
    "# Fit the model\n",
    "mod_fedfunds2 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1])\n",
    "res_fedfunds2 = mod_fedfunds2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:13.965202Z",
     "iopub.status.busy": "2021-02-02T06:54:13.964437Z",
     "iopub.status.idle": "2021-02-02T06:54:13.978167Z",
     "shell.execute_reply": "2021-02-02T06:54:13.977768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>225</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-264.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 02 Feb 2021</td> <th>  AIC                </th>  <td>543.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>06:54:13</td>     <th>  BIC                </th>  <td>567.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-1954</td>    <th>  HQIC               </th>  <td>553.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7245</td> <td>    0.289</td> <td>    2.510</td> <td> 0.012</td> <td>    0.159</td> <td>    1.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.7631</td> <td>    0.034</td> <td>   22.629</td> <td> 0.000</td> <td>    0.697</td> <td>    0.829</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0989</td> <td>    0.118</td> <td>   -0.835</td> <td> 0.404</td> <td>   -0.331</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    1.0612</td> <td>    0.019</td> <td>   57.351</td> <td> 0.000</td> <td>    1.025</td> <td>    1.097</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.4783</td> <td>    0.050</td> <td>    9.642</td> <td> 0.000</td> <td>    0.381</td> <td>    0.576</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.6378</td> <td>    0.120</td> <td>    5.304</td> <td> 0.000</td> <td>    0.402</td> <td>    0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.1306</td> <td>    0.050</td> <td>    2.634</td> <td> 0.008</td> <td>    0.033</td> <td>    0.228</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  225\n",
       "Model:               MarkovRegression   Log Likelihood                -264.711\n",
       "Date:                Tue, 02 Feb 2021   AIC                            543.421\n",
       "Time:                        06:54:13   BIC                            567.334\n",
       "Sample:                    10-01-1954   HQIC                           553.073\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7245      0.289      2.510      0.012       0.159       1.290\n",
       "x1             0.7631      0.034     22.629      0.000       0.697       0.829\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0989      0.118     -0.835      0.404      -0.331       0.133\n",
       "x1             1.0612      0.019     57.351      0.000       1.025       1.097\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.4783      0.050      9.642      0.000       0.381       0.576\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.6378      0.120      5.304      0.000       0.402       0.874\n",
       "p[1->0]        0.1306      0.050      2.634      0.008       0.033       0.228\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several things to notice from the summary output:\n",
    "\n",
    "1. The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.\n",
    "2. The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.\n",
    "\n",
    "Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:13.998393Z",
     "iopub.status.busy": "2021-02-02T06:54:13.996254Z",
     "iopub.status.idle": "2021-02-02T06:54:14.142270Z",
     "shell.execute_reply": "2021-02-02T06:54:14.142849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Probability of being in the high regime'}>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds2.smoothed_marginal_probabilities[0].plot(\n",
    "    title='Probability of being in the high regime', figsize=(12,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the expected durations of each regime have decreased quite a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:14.145855Z",
     "iopub.status.busy": "2021-02-02T06:54:14.145206Z",
     "iopub.status.idle": "2021-02-02T06:54:14.149848Z",
     "shell.execute_reply": "2021-02-02T06:54:14.150353Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.76105188 7.65529154]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds2.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taylor rule with 2 or 3 regimes\n",
    "\n",
    "We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.\n",
    "\n",
    "Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:14.153265Z",
     "iopub.status.busy": "2021-02-02T06:54:14.152578Z",
     "iopub.status.idle": "2021-02-02T06:54:19.242168Z",
     "shell.execute_reply": "2021-02-02T06:54:19.243308Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get the additional data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf\n",
    "dta_ogap = pd.Series(ogap, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "dta_inf = pd.Series(inf, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "\n",
    "exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]\n",
    "\n",
    "# Fit the 2-regime model\n",
    "mod_fedfunds3 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)\n",
    "res_fedfunds3 = mod_fedfunds3.fit()\n",
    "\n",
    "# Fit the 3-regime model\n",
    "np.random.seed(12345)\n",
    "mod_fedfunds4 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)\n",
    "res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:19.248152Z",
     "iopub.status.busy": "2021-02-02T06:54:19.246732Z",
     "iopub.status.idle": "2021-02-02T06:54:19.276651Z",
     "shell.execute_reply": "2021-02-02T06:54:19.277782Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-229.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 02 Feb 2021</td> <th>  AIC                </th>  <td>480.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>06:54:19</td>     <th>  BIC                </th>  <td>517.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>495.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.6555</td> <td>    0.137</td> <td>    4.771</td> <td> 0.000</td> <td>    0.386</td> <td>    0.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8314</td> <td>    0.033</td> <td>   24.951</td> <td> 0.000</td> <td>    0.766</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1355</td> <td>    0.029</td> <td>    4.609</td> <td> 0.000</td> <td>    0.078</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0274</td> <td>    0.041</td> <td>   -0.671</td> <td> 0.502</td> <td>   -0.107</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0945</td> <td>    0.128</td> <td>   -0.739</td> <td> 0.460</td> <td>   -0.345</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9293</td> <td>    0.027</td> <td>   34.309</td> <td> 0.000</td> <td>    0.876</td> <td>    0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0343</td> <td>    0.024</td> <td>    1.429</td> <td> 0.153</td> <td>   -0.013</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.2125</td> <td>    0.030</td> <td>    7.147</td> <td> 0.000</td> <td>    0.154</td> <td>    0.271</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3323</td> <td>    0.035</td> <td>    9.526</td> <td> 0.000</td> <td>    0.264</td> <td>    0.401</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7279</td> <td>    0.093</td> <td>    7.828</td> <td> 0.000</td> <td>    0.546</td> <td>    0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.2115</td> <td>    0.064</td> <td>    3.298</td> <td> 0.001</td> <td>    0.086</td> <td>    0.337</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -229.256\n",
       "Date:                Tue, 02 Feb 2021   AIC                            480.512\n",
       "Time:                        06:54:19   BIC                            517.942\n",
       "Sample:                    07-01-1955   HQIC                           495.624\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.6555      0.137      4.771      0.000       0.386       0.925\n",
       "x1             0.8314      0.033     24.951      0.000       0.766       0.897\n",
       "x2             0.1355      0.029      4.609      0.000       0.078       0.193\n",
       "x3            -0.0274      0.041     -0.671      0.502      -0.107       0.053\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0945      0.128     -0.739      0.460      -0.345       0.156\n",
       "x1             0.9293      0.027     34.309      0.000       0.876       0.982\n",
       "x2             0.0343      0.024      1.429      0.153      -0.013       0.081\n",
       "x3             0.2125      0.030      7.147      0.000       0.154       0.271\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.3323      0.035      9.526      0.000       0.264       0.401\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7279      0.093      7.828      0.000       0.546       0.910\n",
       "p[1->0]        0.2115      0.064      3.298      0.001       0.086       0.337\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:19.282144Z",
     "iopub.status.busy": "2021-02-02T06:54:19.280796Z",
     "iopub.status.idle": "2021-02-02T06:54:19.317199Z",
     "shell.execute_reply": "2021-02-02T06:54:19.318184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-180.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 02 Feb 2021</td> <th>  AIC                </th>  <td>399.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>06:54:19</td>     <th>  BIC                </th>  <td>464.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>425.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -1.0250</td> <td>    0.290</td> <td>   -3.531</td> <td> 0.000</td> <td>   -1.594</td> <td>   -0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.3277</td> <td>    0.086</td> <td>    3.812</td> <td> 0.000</td> <td>    0.159</td> <td>    0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.2036</td> <td>    0.049</td> <td>    4.152</td> <td> 0.000</td> <td>    0.107</td> <td>    0.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    1.1381</td> <td>    0.081</td> <td>   13.977</td> <td> 0.000</td> <td>    0.978</td> <td>    1.298</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0259</td> <td>    0.087</td> <td>   -0.298</td> <td> 0.765</td> <td>   -0.196</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9737</td> <td>    0.019</td> <td>   50.265</td> <td> 0.000</td> <td>    0.936</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0341</td> <td>    0.017</td> <td>    2.030</td> <td> 0.042</td> <td>    0.001</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.1215</td> <td>    0.022</td> <td>    5.606</td> <td> 0.000</td> <td>    0.079</td> <td>    0.164</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 2 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7346</td> <td>    0.130</td> <td>    5.632</td> <td> 0.000</td> <td>    0.479</td> <td>    0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8436</td> <td>    0.024</td> <td>   35.198</td> <td> 0.000</td> <td>    0.797</td> <td>    0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1633</td> <td>    0.025</td> <td>    6.515</td> <td> 0.000</td> <td>    0.114</td> <td>    0.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0499</td> <td>    0.027</td> <td>   -1.835</td> <td> 0.067</td> <td>   -0.103</td> <td>    0.003</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1660</td> <td>    0.018</td> <td>    9.240</td> <td> 0.000</td> <td>    0.131</td> <td>    0.201</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7214</td> <td>    0.117</td> <td>    6.177</td> <td> 0.000</td> <td>    0.493</td> <td>    0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td> 4.001e-08</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->0]</th> <td>    0.0783</td> <td>    0.038</td> <td>    2.079</td> <td> 0.038</td> <td>    0.004</td> <td>    0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->1]</th> <td>    0.1044</td> <td>    0.095</td> <td>    1.103</td> <td> 0.270</td> <td>   -0.081</td> <td>    0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->1]</th> <td>    0.8259</td> <td>    0.054</td> <td>   15.208</td> <td> 0.000</td> <td>    0.719</td> <td>    0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->1]</th> <td>    0.2288</td> <td>    0.073</td> <td>    3.150</td> <td> 0.002</td> <td>    0.086</td> <td>    0.371</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -180.806\n",
       "Date:                Tue, 02 Feb 2021   AIC                            399.611\n",
       "Time:                        06:54:19   BIC                            464.262\n",
       "Sample:                    07-01-1955   HQIC                           425.713\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.0250      0.290     -3.531      0.000      -1.594      -0.456\n",
       "x1             0.3277      0.086      3.812      0.000       0.159       0.496\n",
       "x2             0.2036      0.049      4.152      0.000       0.107       0.300\n",
       "x3             1.1381      0.081     13.977      0.000       0.978       1.298\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0259      0.087     -0.298      0.765      -0.196       0.144\n",
       "x1             0.9737      0.019     50.265      0.000       0.936       1.012\n",
       "x2             0.0341      0.017      2.030      0.042       0.001       0.067\n",
       "x3             0.1215      0.022      5.606      0.000       0.079       0.164\n",
       "                             Regime 2 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7346      0.130      5.632      0.000       0.479       0.990\n",
       "x1             0.8436      0.024     35.198      0.000       0.797       0.891\n",
       "x2             0.1633      0.025      6.515      0.000       0.114       0.212\n",
       "x3            -0.0499      0.027     -1.835      0.067      -0.103       0.003\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.1660      0.018      9.240      0.000       0.131       0.201\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7214      0.117      6.177      0.000       0.493       0.950\n",
       "p[1->0]     4.001e-08        nan        nan        nan         nan         nan\n",
       "p[2->0]        0.0783      0.038      2.079      0.038       0.004       0.152\n",
       "p[0->1]        0.1044      0.095      1.103      0.270      -0.081       0.290\n",
       "p[1->1]        0.8259      0.054     15.208      0.000       0.719       0.932\n",
       "p[2->1]        0.2288      0.073      3.150      0.002       0.086       0.371\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:19.322664Z",
     "iopub.status.busy": "2021-02-02T06:54:19.321304Z",
     "iopub.status.idle": "2021-02-02T06:54:19.957323Z",
     "shell.execute_reply": "2021-02-02T06:54:19.956554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, figsize=(10,7))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])\n",
    "ax.set(title='Smoothed probability of a low-interest rate regime')\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])\n",
    "ax.set(title='Smoothed probability of a medium-interest rate regime')\n",
    "\n",
    "ax = axes[2]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])\n",
    "ax.set(title='Smoothed probability of a high-interest rate regime')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Switching variances\n",
    "\n",
    "We can also accommodate switching variances. In particular, we consider the model\n",
    "\n",
    "$$\n",
    "y_t = \\mu_{S_t} + y_{t-1} \\beta_{S_t} + \\varepsilon_t \\quad \\varepsilon_t \\sim N(0, \\sigma_{S_t}^2)\n",
    "$$\n",
    "\n",
    "We use maximum likelihood to estimate the parameters of this model: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma_0^2, \\sigma_1^2$.\n",
    "\n",
    "The application is to absolute returns on stocks, where the data can be found at https://www.stata-press.com/data/r14/snp500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:19.976413Z",
     "iopub.status.busy": "2021-02-02T06:54:19.975675Z",
     "iopub.status.idle": "2021-02-02T06:54:20.982906Z",
     "shell.execute_reply": "2021-02-02T06:54:20.983328Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns\n",
    "dta_areturns = pd.Series(areturns, index=pd.date_range('2004-05-04', '2014-5-03', freq='W'))\n",
    "\n",
    "# Plot the data\n",
    "dta_areturns.plot(title='Absolute returns, S&P500', figsize=(12,3))\n",
    "\n",
    "# Fit the model\n",
    "mod_areturns = sm.tsa.MarkovRegression(\n",
    "    dta_areturns.iloc[1:], k_regimes=2, exog=dta_areturns.iloc[:-1], switching_variance=True)\n",
    "res_areturns = mod_areturns.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:20.989298Z",
     "iopub.status.busy": "2021-02-02T06:54:20.987624Z",
     "iopub.status.idle": "2021-02-02T06:54:21.007794Z",
     "shell.execute_reply": "2021-02-02T06:54:21.008667Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>520</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-745.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 02 Feb 2021</td> <th>  AIC                </th> <td>1507.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>06:54:20</td>     <th>  BIC                </th> <td>1541.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>05-16-2004</td>    <th>  HQIC               </th> <td>1520.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 04-27-2014</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.7641</td> <td>    0.078</td> <td>    9.761</td> <td> 0.000</td> <td>    0.611</td> <td>    0.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.0791</td> <td>    0.030</td> <td>    2.620</td> <td> 0.009</td> <td>    0.020</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3476</td> <td>    0.061</td> <td>    5.694</td> <td> 0.000</td> <td>    0.228</td> <td>    0.467</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    1.9728</td> <td>    0.278</td> <td>    7.086</td> <td> 0.000</td> <td>    1.427</td> <td>    2.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.5280</td> <td>    0.086</td> <td>    6.155</td> <td> 0.000</td> <td>    0.360</td> <td>    0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    2.5771</td> <td>    0.405</td> <td>    6.357</td> <td> 0.000</td> <td>    1.783</td> <td>    3.372</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7531</td> <td>    0.063</td> <td>   11.871</td> <td> 0.000</td> <td>    0.629</td> <td>    0.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.6825</td> <td>    0.066</td> <td>   10.301</td> <td> 0.000</td> <td>    0.553</td> <td>    0.812</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  520\n",
       "Model:               MarkovRegression   Log Likelihood                -745.798\n",
       "Date:                Tue, 02 Feb 2021   AIC                           1507.595\n",
       "Time:                        06:54:20   BIC                           1541.626\n",
       "Sample:                    05-16-2004   HQIC                          1520.926\n",
       "                         - 04-27-2014                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7641      0.078      9.761      0.000       0.611       0.918\n",
       "x1             0.0791      0.030      2.620      0.009       0.020       0.138\n",
       "sigma2         0.3476      0.061      5.694      0.000       0.228       0.467\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.9728      0.278      7.086      0.000       1.427       2.518\n",
       "x1             0.5280      0.086      6.155      0.000       0.360       0.696\n",
       "sigma2         2.5771      0.405      6.357      0.000       1.783       3.372\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7531      0.063     11.871      0.000       0.629       0.877\n",
       "p[1->0]        0.6825      0.066     10.301      0.000       0.553       0.812\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_areturns.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-02-02T06:54:21.012271Z",
     "iopub.status.busy": "2021-02-02T06:54:21.011521Z",
     "iopub.status.idle": "2021-02-02T06:54:21.396300Z",
     "shell.execute_reply": "2021-02-02T06:54:21.396705Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Probability of being in a low-variance regime'}>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_areturns.smoothed_marginal_probabilities[0].plot(\n",
    "    title='Probability of being in a low-variance regime', figsize=(12,3));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}