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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."
   ]
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   "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."
   ]
  },
  {
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   "execution_count": 2,
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   "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",
    "dta.index.freq = dta.index.inferred_freq\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:"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f273b505dd0>"
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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,
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:39.011969Z",
     "iopub.status.busy": "2021-02-02T07:02:39.010484Z",
     "iopub.status.idle": "2021-02-02T07:02:39.017281Z",
     "shell.execute_reply": "2021-02-02T07:02:39.018290Z"
    }
   },
   "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,
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:39.022946Z",
     "iopub.status.busy": "2021-02-02T07:02:39.021537Z",
     "iopub.status.idle": "2021-02-02T07:02:49.931256Z",
     "shell.execute_reply": "2021-02-02T07:02:49.932609Z"
    }
   },
   "outputs": [],
   "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,
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:49.936363Z",
     "iopub.status.busy": "2021-02-02T07:02:49.935623Z",
     "iopub.status.idle": "2021-02-02T07:02:49.986539Z",
     "shell.execute_reply": "2021-02-02T07:02:49.985804Z"
    }
   },
   "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                          -1527.264\n",
      "Date:                       Tue, 02 Feb 2021   BIC                          -1506.456\n",
      "Time:                               07:02:49   HQIC                         -1518.877\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      4.091e-06   4.93e-05      0.083      0.934   -9.26e-05       0.000\n",
      "sigma2.trend      2.961e-06    1.4e-06      2.112      0.035    2.14e-07    5.71e-06\n",
      "sigma2.cycle       3.91e-05   2.56e-05      1.524      0.127   -1.12e-05    8.94e-05\n",
      "frequency.cycle      0.4454      0.047      9.478      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 (L1) (Q):                   0.00   Jarque-Bera (JB):                 9.29\n",
      "Prob(Q):                              0.96   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,
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:50.009300Z",
     "iopub.status.busy": "2021-02-02T07:02:50.008523Z",
     "iopub.status.idle": "2021-02-02T07:02:52.953920Z",
     "shell.execute_reply": "2021-02-02T07:02:52.952512Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/statespace/structural.py:1722: 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:1722: RuntimeWarning: invalid value encountered in sqrt\n",
      "  std_errors = np.sqrt(component_bunch['%s_cov' % which])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/statespace/structural.py:1722: RuntimeWarning: invalid value encountered in sqrt\n",
      "  std_errors = np.sqrt(component_bunch['%s_cov' % which])\n"
     ]
    },
    {
     "data": {
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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,
    "execution": {
     "iopub.execute_input": "2021-02-02T07:02:52.977649Z",
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   "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>40.91</td>\n",
       "      <td>29.61</td>\n",
       "      <td>390.9</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>49.17</td>\n",
       "      <td>31.36</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.92</td>\n",
       "      <td>10</td>\n",
       "      <td>5.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>21.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.66</td>\n",
       "      <td>0.89</td>\n",
       "      <td>15.37</td>\n",
       "      <td>4.93</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>69.19</td>\n",
       "      <td>18.83</td>\n",
       "      <td>192.6</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>18.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.5</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.83</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                            40.91   \n",
       "US Prices        1948:1-2008:1 None                            49.17   \n",
       "                               $\\sigma_\\eta^2 = 0$             21.78   \n",
       "US monetary base 1948:1-2008:1 None                            69.19   \n",
       "                               $\\sigma_\\eta^2 = 0$             18.91   \n",
       "\n",
       "                                                    $\\sigma_\\eta^2$  \\\n",
       "Series           Time range    Restrictions                           \n",
       "US GNP           1948:1-2008:1 None                           29.61   \n",
       "US Prices        1948:1-2008:1 None                           31.36   \n",
       "                               $\\sigma_\\eta^2 = 0$              NaN   \n",
       "US monetary base 1948:1-2008:1 None                           18.83   \n",
       "                               $\\sigma_\\eta^2 = 0$              NaN   \n",
       "\n",
       "                                                    $\\sigma_\\kappa^2$  $\\rho$  \\\n",
       "Series           Time range    Restrictions                                     \n",
       "US GNP           1948:1-2008:1 None                             390.9    0.87   \n",
       "US Prices        1948:1-2008:1 None                              0.01    0.92   \n",
       "                               $\\sigma_\\eta^2 = 0$              49.66    0.89   \n",
       "US monetary base 1948:1-2008:1 None                             192.6    0.89   \n",
       "                               $\\sigma_\\eta^2 = 0$              247.5    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   \n",
       "                               $\\sigma_\\eta^2 = 0$                15.37   \n",
       "US monetary base 1948:1-2008:1 None                               23.21   \n",
       "                               $\\sigma_\\eta^2 = 0$                23.83   \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                                   5.89  \n",
       "                               $\\sigma_\\eta^2 = 0$                    4.93  \n",
       "US monetary base 1948:1-2008:1 None                                      0  \n",
       "                               $\\sigma_\\eta^2 = 0$                       0  "
      ]
     },
     "execution_count": 1,
     "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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