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    "# 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",
    "\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "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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   "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 = (\n",
    "    DataReader(\"AMBSL\", \"fred\", start=start, end=end).resample(\"QS\").mean()\n",
    ")\n",
    "recessions = (\n",
    "    DataReader(\"USRECQ\", \"fred\", start=start, end=end)\n",
    "    .resample(\"QS\")\n",
    "    .last()\n",
    "    .values[:, 0]\n",
    ")\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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",
      "text/plain": [
       "<Figure size 1300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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(\n",
    "    dates, ylim[0] + 1e-5, ylim[1] - 1e-5, recessions, facecolor=\"k\", alpha=0.1\n",
    ");"
   ]
  },
  {
   "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": "2026-07-29T17:22:58.606126Z",
     "iopub.status.busy": "2026-07-29T17:22:58.605830Z",
     "iopub.status.idle": "2026-07-29T17:22:58.613833Z",
     "shell.execute_reply": "2026-07-29T17:22:58.612311Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Model specifications\n",
    "\n",
    "# Unrestricted model, using string specification\n",
    "unrestricted_model = {\n",
    "    \"level\": \"local linear trend\",\n",
    "    \"cycle\": True,\n",
    "    \"damped_cycle\": True,\n",
    "    \"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\",\n",
    "    \"cycle\": True,\n",
    "    \"damped_cycle\": True,\n",
    "    \"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": "2026-07-29T17:22:58.616428Z",
     "iopub.status.busy": "2026-07-29T17:22:58.616123Z",
     "iopub.status.idle": "2026-07-29T17:22:59.934267Z",
     "shell.execute_reply": "2026-07-29T17:22:59.933118Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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(\n",
    "    dta[\"US Prices\"], **restricted_model\n",
    ")\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(\n",
    "    dta[\"US monetary base\"], **restricted_model\n",
    ")\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": "2026-07-29T17:22:59.936795Z",
     "iopub.status.busy": "2026-07-29T17:22:59.936522Z",
     "iopub.status.idle": "2026-07-29T17:22:59.954463Z",
     "shell.execute_reply": "2026-07-29T17:22:59.953017Z"
    },
    "jupyter": {
     "outputs_hidden": 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                 769.973\n",
      "                   + damped stochastic cycle   AIC                          -1527.945\n",
      "Date:                       Wed, 29 Jul 2026   BIC                          -1507.137\n",
      "Time:                               17:22:59   HQIC                         -1519.558\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  1.396e-06   7.34e-06      0.190      0.849    -1.3e-05    1.58e-05\n",
      "sigma2.level      2.789e-06   4.97e-05      0.056      0.955   -9.46e-05       0.000\n",
      "sigma2.trend      3.198e-06   1.48e-06      2.162      0.031    2.99e-07     6.1e-06\n",
      "sigma2.cycle      3.871e-05   2.55e-05      1.517      0.129   -1.13e-05    8.87e-05\n",
      "frequency.cycle      0.4495      0.047      9.548      0.000       0.357       0.542\n",
      "damping.cycle        0.8672      0.042     20.567      0.000       0.785       0.950\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 9.27\n",
      "Prob(Q):                              1.00   Prob(JB):                         0.01\n",
      "Heteroskedasticity (H):               0.26   Skew:                            -0.04\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.97\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": "2026-07-29T17:22:59.956931Z",
     "iopub.status.busy": "2026-07-29T17:22:59.956675Z",
     "iopub.status.idle": "2026-07-29T17:23:01.044054Z",
     "shell.execute_reply": "2026-07-29T17:23:01.043330Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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": "2026-07-29T17:23:01.048331Z",
     "iopub.status.busy": "2026-07-29T17:23:01.048064Z",
     "iopub.status.idle": "2026-07-29T17:23:01.070962Z",
     "shell.execute_reply": "2026-07-29T17:23:01.070086Z"
    },
    "jupyter": {
     "outputs_hidden": 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>27.89</td>\n",
       "      <td>31.98</td>\n",
       "      <td>387.1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>13.98</td>\n",
       "      <td>13.96</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>0</td>\n",
       "      <td>61.9</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>61.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0</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>63.6</td>\n",
       "      <td>18.99</td>\n",
       "      <td>196.4</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>18.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>247.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.88</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                            27.89   \n",
       "US Prices        1948:1-2008:1 None                                0   \n",
       "                               $\\sigma_\\eta^2 = 0$             61.91   \n",
       "US monetary base 1948:1-2008:1 None                             63.6   \n",
       "                               $\\sigma_\\eta^2 = 0$             18.85   \n",
       "\n",
       "                                                    $\\sigma_\\eta^2$  \\\n",
       "Series           Time range    Restrictions                           \n",
       "US GNP           1948:1-2008:1 None                           31.98   \n",
       "US Prices        1948:1-2008:1 None                            61.9   \n",
       "                               $\\sigma_\\eta^2 = 0$              NaN   \n",
       "US monetary base 1948:1-2008:1 None                           18.99   \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                             387.1    0.87   \n",
       "US Prices        1948:1-2008:1 None                              20.7    0.43   \n",
       "                               $\\sigma_\\eta^2 = 0$               20.7    0.43   \n",
       "US monetary base 1948:1-2008:1 None                             196.4    0.89   \n",
       "                               $\\sigma_\\eta^2 = 0$              247.3    0.89   \n",
       "\n",
       "                                                    $2 \\pi / \\lambda_c$  \\\n",
       "Series           Time range    Restrictions                               \n",
       "US GNP           1948:1-2008:1 None                               13.98   \n",
       "US Prices        1948:1-2008:1 None                                6.55   \n",
       "                               $\\sigma_\\eta^2 = 0$                 6.55   \n",
       "US monetary base 1948:1-2008:1 None                               23.16   \n",
       "                               $\\sigma_\\eta^2 = 0$                23.88   \n",
       "\n",
       "                                                    $\\sigma_\\varepsilon^2$  \n",
       "Series           Time range    Restrictions                                 \n",
       "US GNP           1948:1-2008:1 None                                  13.96  \n",
       "US Prices        1948:1-2008:1 None                                      0  \n",
       "                               $\\sigma_\\eta^2 = 0$                       0  \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 = f\"{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(\n",
    "    models, names=[\"Series\", \"Time range\", \"Restrictions\"]\n",
    ")\n",
    "parameter_symbols = [\n",
    "    r\"$\\sigma_\\zeta^2$\",\n",
    "    r\"$\\sigma_\\eta^2$\",\n",
    "    r\"$\\sigma_\\kappa^2$\",\n",
    "    r\"$\\rho$\",\n",
    "    r\"$2 \\pi / \\lambda_c$\",\n",
    "    r\"$\\sigma_\\varepsilon^2$\",\n",
    "]\n",
    "\n",
    "i = 0\n",
    "for res in (\n",
    "    output_res,\n",
    "    prices_res,\n",
    "    prices_restricted_res,\n",
    "    money_res,\n",
    "    money_restricted_res,\n",
    "):\n",
    "    if res.model.stochastic_level:\n",
    "        (\n",
    "            sigma_irregular,\n",
    "            sigma_level,\n",
    "            sigma_trend,\n",
    "            sigma_cycle,\n",
    "            frequency_cycle,\n",
    "            damping_cycle,\n",
    "        ) = res.params\n",
    "    else:\n",
    "        sigma_irregular, sigma_level, sigma_cycle, frequency_cycle, damping_cycle = (\n",
    "            res.params\n",
    "        )\n",
    "        sigma_trend = np.nan\n",
    "    period_cycle = 2 * np.pi / frequency_cycle\n",
    "\n",
    "    table_i[i, :] = [\n",
    "        sigma_level * 1e7,\n",
    "        sigma_trend * 1e7,\n",
    "        sigma_cycle * 1e7,\n",
    "        damping_cycle,\n",
    "        period_cycle,\n",
    "        sigma_irregular * 1e7,\n",
    "    ]\n",
    "    i += 1\n",
    "\n",
    "pd.set_option(\n",
    "    \"float_format\", lambda x: f\"{np.round(x, 2):.4g}\" if not np.isnan(x) else \"-\"\n",
    ")\n",
    "table_i = pd.DataFrame(table_i, index=index, columns=parameter_symbols)\n",
    "table_i"
   ]
  }
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