{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Detrending, Stylized Facts and the Business Cycle\n",
    "\n",
    "In an influential article, Harvey and Jaeger (1993) described the use of unobserved components models (also known as \"structural time series models\") to derive stylized facts of the business cycle.\n",
    "\n",
    "Their paper begins:\n",
    "\n",
    "    \"Establishing the 'stylized facts' associated with a set of time series is widely considered a crucial step\n",
    "    in macroeconomic research ... For such facts to be useful they should (1) be consistent with the stochastic\n",
    "    properties of the data and (2) present meaningful information.\"\n",
    "    \n",
    "In particular, they make the argument that these goals are often better met using the unobserved components approach rather than the popular Hodrick-Prescott filter or Box-Jenkins ARIMA modeling techniques.\n",
    "\n",
    "statsmodels has the ability to perform all three types of analysis, and below we follow the steps of their paper, using a slightly updated dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "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",
    "from IPython.display import display, Latex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unobserved Components\n",
    "\n",
    "The unobserved components model available in statsmodels can be written as:\n",
    "\n",
    "$$\n",
    "y_t = \\underbrace{\\mu_{t}}_{\\text{trend}} + \\underbrace{\\gamma_{t}}_{\\text{seasonal}} + \\underbrace{c_{t}}_{\\text{cycle}} + \\sum_{j=1}^k \\underbrace{\\beta_j x_{jt}}_{\\text{explanatory}} + \\underbrace{\\varepsilon_t}_{\\text{irregular}}\n",
    "$$\n",
    "\n",
    "see Durbin and Koopman 2012, Chapter 3 for notation and additional details. Notice that different specifications for the different individual components can support a wide range of models. The specific models considered in the paper and below are specializations of this general equation.\n",
    "\n",
    "### Trend\n",
    "\n",
    "The trend component is a dynamic extension of a regression model that includes an intercept and linear time-trend.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\underbrace{\\mu_{t+1}}_{\\text{level}} & = \\mu_t + \\nu_t + \\eta_{t+1} \\qquad & \\eta_{t+1} \\sim N(0, \\sigma_\\eta^2) \\\\\\\\\n",
    "\\underbrace{\\nu_{t+1}}_{\\text{trend}} & = \\nu_t + \\zeta_{t+1} & \\zeta_{t+1} \\sim N(0, \\sigma_\\zeta^2) \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where the level is a generalization of the intercept term that can dynamically vary across time, and the trend is a generalization of the time-trend such that the slope can dynamically vary across time.\n",
    "\n",
    "For both elements (level and trend), we can consider models in which:\n",
    "\n",
    "- The element is included vs excluded (if the trend is included, there must also be a level included).\n",
    "- The element is deterministic vs stochastic (i.e. whether or not the variance on the error term is confined to be zero or not)\n",
    "\n",
    "The only additional parameters to be estimated via MLE are the variances of any included stochastic components.\n",
    "\n",
    "This leads to the following specifications:\n",
    "\n",
    "|                                                                      | Level | Trend | Stochastic Level | Stochastic Trend |\n",
    "|----------------------------------------------------------------------|-------|-------|------------------|------------------|\n",
    "| Constant                                                             | ✓     |       |                  |                  |\n",
    "| Local Level <br /> (random walk)                                     | ✓     |       | ✓                |                  |\n",
    "| Deterministic trend                                                  | ✓     | ✓     |                  |                  |\n",
    "| Local level with deterministic trend <br /> (random walk with drift) | ✓     | ✓     | ✓                |                  |\n",
    "| Local linear trend                                                   | ✓     | ✓     | ✓                | ✓                |\n",
    "| Smooth trend <br /> (integrated random walk)                         | ✓     | ✓     |                  | ✓                |\n",
    "\n",
    "### Seasonal\n",
    "\n",
    "The seasonal component is written as:\n",
    "\n",
    "<span>$$\n",
    "\\gamma_t = - \\sum_{j=1}^{s-1} \\gamma_{t+1-j} + \\omega_t \\qquad \\omega_t \\sim N(0, \\sigma_\\omega^2)\n",
    "$$</span>\n",
    "\n",
    "The periodicity (number of seasons) is `s`, and the defining character is that (without the error term), the seasonal components sum to zero across one complete cycle. The inclusion of an error term allows the seasonal effects to vary over time.\n",
    "\n",
    "The variants of this model are:\n",
    "\n",
    "- The periodicity `s`\n",
    "- Whether or not to make the seasonal effects stochastic.\n",
    "\n",
    "If the seasonal effect is stochastic, then there is one additional parameter to estimate via MLE (the variance of the error term).\n",
    "\n",
    "### Cycle\n",
    "\n",
    "The cyclical component is intended to capture cyclical effects at time frames much longer than captured by the seasonal component. For example, in economics the cyclical term is often intended to capture the business cycle, and is then expected to have a period between \"1.5 and 12 years\" (see Durbin and Koopman).\n",
    "\n",
    "The cycle is written as:\n",
    "\n",
    "<span>$$\n",
    "\\begin{align}\n",
    "c_{t+1} & = c_t \\cos \\lambda_c + c_t^* \\sin \\lambda_c + \\tilde \\omega_t \\qquad & \\tilde \\omega_t \\sim N(0, \\sigma_{\\tilde \\omega}^2) \\\\\\\\\n",
    "c_{t+1}^* & = -c_t \\sin \\lambda_c + c_t^* \\cos \\lambda_c + \\tilde \\omega_t^* & \\tilde \\omega_t^* \\sim N(0, \\sigma_{\\tilde \\omega}^2)\n",
    "\\end{align}\n",
    "$$</span>\n",
    "\n",
    "The parameter $\\lambda_c$ (the frequency of the cycle) is an additional parameter to be estimated by MLE. If the seasonal effect is stochastic, then there is one another parameter to estimate (the variance of the error term - note that both of the error terms here share the same variance, but are assumed to have independent draws).\n",
    "\n",
    "### Irregular\n",
    "\n",
    "The irregular component is assumed to be a white noise error term. Its variance is a parameter to be estimated by MLE; i.e.\n",
    "\n",
    "$$\n",
    "\\varepsilon_t \\sim N(0, \\sigma_\\varepsilon^2)\n",
    "$$\n",
    "\n",
    "In some cases, we may want to generalize the irregular component to allow for autoregressive effects:\n",
    "\n",
    "$$\n",
    "\\varepsilon_t = \\rho(L) \\varepsilon_{t-1} + \\epsilon_t, \\qquad \\epsilon_t \\sim N(0, \\sigma_\\epsilon^2)\n",
    "$$\n",
    "\n",
    "In this case, the autoregressive parameters would also be estimated via MLE.\n",
    "\n",
    "### Regression effects\n",
    "\n",
    "We may want to allow for explanatory variables by including additional terms\n",
    "\n",
    "<span>$$\n",
    "\\sum_{j=1}^k \\beta_j x_{jt}\n",
    "$$</span>\n",
    "\n",
    "or for intervention effects by including\n",
    "\n",
    "<span>$$\n",
    "\\begin{align}\n",
    "\\delta w_t \\qquad \\text{where} \\qquad w_t & = 0, \\qquad t < \\tau, \\\\\\\\\n",
    "& = 1, \\qquad t \\ge \\tau\n",
    "\\end{align}\n",
    "$$</span>\n",
    "\n",
    "These additional parameters could be estimated via MLE or by including them as components of the state space formulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Following Harvey and Jaeger, we will consider the following time series:\n",
    "\n",
    "- US real GNP, \"output\", ([GNPC96](https://research.stlouisfed.org/fred2/series/GNPC96))\n",
    "- US GNP implicit price deflator, \"prices\", ([GNPDEF](https://research.stlouisfed.org/fred2/series/GNPDEF))\n",
    "- US monetary base, \"money\", ([AMBSL](https://research.stlouisfed.org/fred2/series/AMBSL))\n",
    "\n",
    "The time frame in the original paper varied across series, but was broadly 1954-1989. Below we use data from the period 1948-2008 for all series. Although the unobserved components approach allows isolating a seasonal component within the model, the series considered in the paper, and here, are already seasonally adjusted.\n",
    "\n",
    "All data series considered here are taken from [Federal Reserve Economic Data (FRED)](https://research.stlouisfed.org/fred2/). Conveniently, the Python library [Pandas](https://pandas.pydata.org/) has the ability to download data from FRED directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Datasets\n",
    "from pandas_datareader.data import DataReader\n",
    "\n",
    "# Get the raw data\n",
    "start = '1948-01'\n",
    "end = '2008-01'\n",
    "us_gnp = DataReader('GNPC96', 'fred', start=start, end=end)\n",
    "us_gnp_deflator = DataReader('GNPDEF', 'fred', start=start, end=end)\n",
    "us_monetary_base = DataReader('AMBSL', 'fred', start=start, end=end).resample('QS').mean()\n",
    "recessions = DataReader('USRECQ', 'fred', start=start, end=end).resample('QS').last().values[:,0]\n",
    "\n",
    "# Construct the dataframe\n",
    "dta = pd.concat(map(np.log, (us_gnp, us_gnp_deflator, us_monetary_base)), axis=1)\n",
    "dta.columns = ['US GNP','US Prices','US monetary base']\n",
    "dates = dta.index._mpl_repr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a sense of these three variables over the timeframe, we can plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "ax = dta.plot(figsize=(13,3))\n",
    "ylim = ax.get_ylim()\n",
    "ax.xaxis.grid()\n",
    "ax.fill_between(dates, ylim[0]+1e-5, ylim[1]-1e-5, recessions, facecolor='k', alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "Since the data is already seasonally adjusted and there are no obvious explanatory variables, the generic model considered is:\n",
    "\n",
    "$$\n",
    "y_t = \\underbrace{\\mu_{t}}_{\\text{trend}} + \\underbrace{c_{t}}_{\\text{cycle}} + \\underbrace{\\varepsilon_t}_{\\text{irregular}}\n",
    "$$\n",
    "\n",
    "The irregular will be assumed to be white noise, and the cycle will be stochastic and damped. The final modeling choice is the specification to use for the trend component. Harvey and Jaeger consider two models:\n",
    "\n",
    "1. Local linear trend (the \"unrestricted\" model)\n",
    "2. Smooth trend (the \"restricted\" model, since we are forcing $\\sigma_\\eta = 0$)\n",
    "\n",
    "Below, we construct `kwargs` dictionaries for each of these model types. Notice that rather that there are two ways to specify the models. One way is to specify components directly, as in the table above. The other way is to use string names which map to various specifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Model specifications\n",
    "\n",
    "# Unrestricted model, using string specification\n",
    "unrestricted_model = {\n",
    "    'level': 'local linear trend', 'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "}\n",
    "\n",
    "# Unrestricted model, setting components directly\n",
    "# This is an equivalent, but less convenient, way to specify a\n",
    "# local linear trend model with a stochastic damped cycle:\n",
    "# unrestricted_model = {\n",
    "#     'irregular': True, 'level': True, 'stochastic_level': True, 'trend': True, 'stochastic_trend': True,\n",
    "#     'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "# }\n",
    "\n",
    "# The restricted model forces a smooth trend\n",
    "restricted_model = {\n",
    "    'level': 'smooth trend', 'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "}\n",
    "\n",
    "# Restricted model, setting components directly\n",
    "# This is an equivalent, but less convenient, way to specify a\n",
    "# smooth trend model with a stochastic damped cycle. Notice\n",
    "# that the difference from the local linear trend model is that\n",
    "# `stochastic_level=False` here.\n",
    "# unrestricted_model = {\n",
    "#     'irregular': True, 'level': True, 'stochastic_level': False, 'trend': True, 'stochastic_trend': True,\n",
    "#     'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "# }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now fit the following models:\n",
    "\n",
    "1. Output, unrestricted model\n",
    "2. Prices, unrestricted model\n",
    "3. Prices, restricted model\n",
    "4. Money, unrestricted model\n",
    "5. Money, restricted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "# Output\n",
    "output_mod = sm.tsa.UnobservedComponents(dta['US GNP'], **unrestricted_model)\n",
    "output_res = output_mod.fit(method='powell', disp=False)\n",
    "\n",
    "# Prices\n",
    "prices_mod = sm.tsa.UnobservedComponents(dta['US Prices'], **unrestricted_model)\n",
    "prices_res = prices_mod.fit(method='powell', disp=False)\n",
    "\n",
    "prices_restricted_mod = sm.tsa.UnobservedComponents(dta['US Prices'], **restricted_model)\n",
    "prices_restricted_res = prices_restricted_mod.fit(method='powell', disp=False)\n",
    "\n",
    "# Money\n",
    "money_mod = sm.tsa.UnobservedComponents(dta['US monetary base'], **unrestricted_model)\n",
    "money_res = money_mod.fit(method='powell', disp=False)\n",
    "\n",
    "money_restricted_mod = sm.tsa.UnobservedComponents(dta['US monetary base'], **restricted_model)\n",
    "money_restricted_res = money_restricted_mod.fit(method='powell', disp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have fit these models, there are a variety of ways to display the information. Looking at the model of US GNP, we can summarize the fit of the model using the `summary` method on the fit object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Unobserved Components Results                            \n",
      "=====================================================================================\n",
      "Dep. Variable:                        US GNP   No. Observations:                  241\n",
      "Model:                    local linear trend   Log Likelihood                 769.632\n",
      "                   + damped stochastic cycle   AIC                          -1527.263\n",
      "Date:                       Tue, 17 Dec 2019   BIC                          -1506.455\n",
      "Time:                               23:40:55   HQIC                         -1518.876\n",
      "Sample:                           01-01-1948                                         \n",
      "                                - 01-01-2008                                         \n",
      "Covariance Type:                         opg                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "sigma2.irregular  1.091e-06   7.29e-06      0.150      0.881   -1.32e-05    1.54e-05\n",
      "sigma2.level       4.08e-06   4.93e-05      0.083      0.934   -9.26e-05       0.000\n",
      "sigma2.trend      2.962e-06    1.4e-06      2.112      0.035    2.14e-07    5.71e-06\n",
      "sigma2.cycle       3.91e-05   2.57e-05      1.524      0.127   -1.12e-05    8.94e-05\n",
      "frequency.cycle      0.4454      0.047      9.477      0.000       0.353       0.537\n",
      "damping.cycle        0.8684      0.042     20.732      0.000       0.786       0.951\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       43.93   Jarque-Bera (JB):                 9.29\n",
      "Prob(Q):                              0.31   Prob(JB):                         0.01\n",
      "Heteroskedasticity (H):               0.27   Skew:                            -0.05\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.96\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "print(output_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For unobserved components models, and in particular when exploring stylized facts in line with point (2) from the introduction, it is often more instructive to plot the estimated unobserved components (e.g. the level, trend, and cycle) themselves to see if they provide a meaningful description of the data.\n",
    "\n",
    "The `plot_components` method of the fit object can be used to show plots and confidence intervals of each of the estimated states, as well as a plot of the observed data versus the one-step-ahead predictions of the model to assess fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/statespace/structural.py:1723: RuntimeWarning: invalid value encountered in sqrt\n",
      "  std_errors = np.sqrt(component_bunch['%s_cov' % which])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = output_res.plot_components(legend_loc='lower right', figsize=(15, 9));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, Harvey and Jaeger summarize the models in another way to highlight the relative importances of the trend and cyclical components; below we replicate their Table I. The values we find are broadly consistent with, but different in the particulars from, the values from their table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>$\\sigma_\\zeta^2$</th>\n",
       "      <th>$\\sigma_\\eta^2$</th>\n",
       "      <th>$\\sigma_\\kappa^2$</th>\n",
       "      <th>$\\rho$</th>\n",
       "      <th>$2 \\pi / \\lambda_c$</th>\n",
       "      <th>$\\sigma_\\varepsilon^2$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th>Time range</th>\n",
       "      <th>Restrictions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US GNP</th>\n",
       "      <th>1948:1-2008:1</th>\n",
       "      <th>None</th>\n",
       "      <td>40.8</td>\n",
       "      <td>29.62</td>\n",
       "      <td>391</td>\n",
       "      <td>0.87</td>\n",
       "      <td>14.11</td>\n",
       "      <td>10.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">US Prices</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1948:1-2008:1</th>\n",
       "      <th>None</th>\n",
       "      <td>47.05</td>\n",
       "      <td>28.34</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.95</td>\n",
       "      <td>7.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>21.95</td>\n",
       "      <td>-</td>\n",
       "      <td>49.29</td>\n",
       "      <td>0.89</td>\n",
       "      <td>15.36</td>\n",
       "      <td>5.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">US monetary base</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1948:1-2008:1</th>\n",
       "      <th>None</th>\n",
       "      <td>61.54</td>\n",
       "      <td>18.75</td>\n",
       "      <td>198.9</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>18.86</td>\n",
       "      <td>-</td>\n",
       "      <td>247.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.87</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    $\\sigma_\\zeta^2$  \\\n",
       "Series           Time range    Restrictions                            \n",
       "US GNP           1948:1-2008:1 None                             40.8   \n",
       "US Prices        1948:1-2008:1 None                            47.05   \n",
       "                               $\\sigma_\\eta^2 = 0$             21.95   \n",
       "US monetary base 1948:1-2008:1 None                            61.54   \n",
       "                               $\\sigma_\\eta^2 = 0$             18.86   \n",
       "\n",
       "                                                    $\\sigma_\\eta^2$  \\\n",
       "Series           Time range    Restrictions                           \n",
       "US GNP           1948:1-2008:1 None                           29.62   \n",
       "US Prices        1948:1-2008:1 None                           28.34   \n",
       "                               $\\sigma_\\eta^2 = 0$                -   \n",
       "US monetary base 1948:1-2008:1 None                           18.75   \n",
       "                               $\\sigma_\\eta^2 = 0$                -   \n",
       "\n",
       "                                                    $\\sigma_\\kappa^2$  $\\rho$  \\\n",
       "Series           Time range    Restrictions                                     \n",
       "US GNP           1948:1-2008:1 None                               391    0.87   \n",
       "US Prices        1948:1-2008:1 None                              0.01    0.92   \n",
       "                               $\\sigma_\\eta^2 = 0$              49.29    0.89   \n",
       "US monetary base 1948:1-2008:1 None                             198.9    0.89   \n",
       "                               $\\sigma_\\eta^2 = 0$              247.6    0.89   \n",
       "\n",
       "                                                    $2 \\pi / \\lambda_c$  \\\n",
       "Series           Time range    Restrictions                               \n",
       "US GNP           1948:1-2008:1 None                               14.11   \n",
       "US Prices        1948:1-2008:1 None                                9.95   \n",
       "                               $\\sigma_\\eta^2 = 0$                15.36   \n",
       "US monetary base 1948:1-2008:1 None                               23.28   \n",
       "                               $\\sigma_\\eta^2 = 0$                23.87   \n",
       "\n",
       "                                                    $\\sigma_\\varepsilon^2$  \n",
       "Series           Time range    Restrictions                                 \n",
       "US GNP           1948:1-2008:1 None                                  10.91  \n",
       "US Prices        1948:1-2008:1 None                                   7.44  \n",
       "                               $\\sigma_\\eta^2 = 0$                    5.02  \n",
       "US monetary base 1948:1-2008:1 None                                      0  \n",
       "                               $\\sigma_\\eta^2 = 0$                       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Table I\n",
    "table_i = np.zeros((5,6))\n",
    "\n",
    "start = dta.index[0]\n",
    "end = dta.index[-1]\n",
    "time_range = '%d:%d-%d:%d' % (start.year, start.quarter, end.year, end.quarter)\n",
    "models = [\n",
    "    ('US GNP', time_range, 'None'),\n",
    "    ('US Prices', time_range, 'None'),\n",
    "    ('US Prices', time_range, r'$\\sigma_\\eta^2 = 0$'),\n",
    "    ('US monetary base', time_range, 'None'),\n",
    "    ('US monetary base', time_range, r'$\\sigma_\\eta^2 = 0$'),\n",
    "]\n",
    "index = pd.MultiIndex.from_tuples(models, names=['Series', 'Time range', 'Restrictions'])\n",
    "parameter_symbols = [\n",
    "    r'$\\sigma_\\zeta^2$', r'$\\sigma_\\eta^2$', r'$\\sigma_\\kappa^2$', r'$\\rho$',\n",
    "    r'$2 \\pi / \\lambda_c$', r'$\\sigma_\\varepsilon^2$',\n",
    "]\n",
    "\n",
    "i = 0\n",
    "for res in (output_res, prices_res, prices_restricted_res, money_res, money_restricted_res):\n",
    "    if res.model.stochastic_level:\n",
    "        (sigma_irregular, sigma_level, sigma_trend,\n",
    "         sigma_cycle, frequency_cycle, damping_cycle) = res.params\n",
    "    else:\n",
    "        (sigma_irregular, sigma_level,\n",
    "         sigma_cycle, frequency_cycle, damping_cycle) = res.params\n",
    "        sigma_trend = np.nan\n",
    "    period_cycle = 2 * np.pi / frequency_cycle\n",
    "    \n",
    "    table_i[i, :] = [\n",
    "        sigma_level*1e7, sigma_trend*1e7,\n",
    "        sigma_cycle*1e7, damping_cycle, period_cycle,\n",
    "        sigma_irregular*1e7\n",
    "    ]\n",
    "    i += 1\n",
    "    \n",
    "pd.set_option('float_format', lambda x: '%.4g' % np.round(x, 2) if not np.isnan(x) else '-')\n",
    "table_i = pd.DataFrame(table_i, index=index, columns=parameter_symbols)\n",
    "table_i"
   ]
  }
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