{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    }
   ],
   "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": [
    {
     "data": {
      "image/png": "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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:                       Fri, 21 Feb 2020   BIC                          -1506.455\n",
      "Time:                               13:56:31   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   1.09e-06   7.29e-06      0.149      0.881   -1.32e-05    1.54e-05\n",
      "sigma2.level      4.075e-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      3.911e-05   2.57e-05      1.524      0.127   -1.12e-05    8.94e-05\n",
      "frequency.cycle      0.4453      0.047      9.476      0.000       0.353       0.537\n",
      "damping.cycle        0.8684      0.042     20.731      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",
      "/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",
      "/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>40.75</td>\n",
       "      <td>29.62</td>\n",
       "      <td>391.1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>14.11</td>\n",
       "      <td>10.9</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>46.08</td>\n",
       "      <td>29.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0.92</td>\n",
       "      <td>10.01</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>21.94</td>\n",
       "      <td>-</td>\n",
       "      <td>49.52</td>\n",
       "      <td>0.89</td>\n",
       "      <td>15.36</td>\n",
       "      <td>4.84</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>69.39</td>\n",
       "      <td>18.81</td>\n",
       "      <td>192.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>18.94</td>\n",
       "      <td>-</td>\n",
       "      <td>247</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.81</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                            40.75   \n",
       "US Prices        1948:1-2008:1 None                            46.08   \n",
       "                               $\\sigma_\\eta^2 = 0$             21.94   \n",
       "US monetary base 1948:1-2008:1 None                            69.39   \n",
       "                               $\\sigma_\\eta^2 = 0$             18.94   \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                           29.76   \n",
       "                               $\\sigma_\\eta^2 = 0$                -   \n",
       "US monetary base 1948:1-2008:1 None                           18.81   \n",
       "                               $\\sigma_\\eta^2 = 0$                -   \n",
       "\n",
       "                                                    $\\sigma_\\kappa^2$  $\\rho$  \\\n",
       "Series           Time range    Restrictions                                     \n",
       "US GNP           1948:1-2008:1 None                             391.1    0.87   \n",
       "US Prices        1948:1-2008:1 None                                 0    0.92   \n",
       "                               $\\sigma_\\eta^2 = 0$              49.52    0.89   \n",
       "US monetary base 1948:1-2008:1 None                             192.6    0.89   \n",
       "                               $\\sigma_\\eta^2 = 0$                247    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                               10.01   \n",
       "                               $\\sigma_\\eta^2 = 0$                15.36   \n",
       "US monetary base 1948:1-2008:1 None                                23.2   \n",
       "                               $\\sigma_\\eta^2 = 0$                23.81   \n",
       "\n",
       "                                                    $\\sigma_\\varepsilon^2$  \n",
       "Series           Time range    Restrictions                                 \n",
       "US GNP           1948:1-2008:1 None                                   10.9  \n",
       "US Prices        1948:1-2008:1 None                                    7.2  \n",
       "                               $\\sigma_\\eta^2 = 0$                    4.84  \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"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}