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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 = \"QS\"\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": "2026-10-05T18:57:25.958910Z",
     "iopub.status.busy": "2026-10-05T18:57:25.958496Z",
     "iopub.status.idle": "2026-10-05T18:57:25.974458Z",
     "shell.execute_reply": "2026-10-05T18:57:25.971164Z"
    },
    "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": "2026-10-05T18:57:25.978294Z",
     "iopub.status.busy": "2026-10-05T18:57:25.976598Z",
     "iopub.status.idle": "2026-10-05T18:57:28.492178Z",
     "shell.execute_reply": "2026-10-05T18:57:28.484835Z"
    },
    "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": "2026-10-05T18:57:28.496426Z",
     "iopub.status.busy": "2026-10-05T18:57:28.494539Z",
     "iopub.status.idle": "2026-10-05T18:57:28.523732Z",
     "shell.execute_reply": "2026-10-05T18:57:28.523160Z"
    },
    "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                 769.972\n",
      "                   + damped stochastic cycle   AIC                          -1527.945\n",
      "Date:                       Mon, 05 Oct 2026   BIC                          -1507.136\n",
      "Time:                               18:57:28   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    1.4e-06   7.35e-06      0.191      0.849    -1.3e-05    1.58e-05\n",
      "sigma2.level      2.776e-06   4.97e-05      0.056      0.955   -9.47e-05       0.000\n",
      "sigma2.trend      3.199e-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      0.4496      0.047      9.545      0.000       0.357       0.542\n",
      "damping.cycle        0.8672      0.042     20.561      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:                         3.96\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": "2026-10-05T18:57:28.527301Z",
     "iopub.status.busy": "2026-10-05T18:57:28.526533Z",
     "iopub.status.idle": "2026-10-05T18:57:29.976720Z",
     "shell.execute_reply": "2026-10-05T18:57:29.976155Z"
    },
    "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": "2026-10-05T18:57:29.981115Z",
     "iopub.status.busy": "2026-10-05T18:57:29.980222Z",
     "iopub.status.idle": "2026-10-05T18:57:30.003522Z",
     "shell.execute_reply": "2026-10-05T18:57:30.002966Z"
    },
    "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>27.76</td>\n",
       "      <td>31.99</td>\n",
       "      <td>387.1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>13.98</td>\n",
       "      <td>14</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>61.89</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>20.71</td>\n",
       "      <td>0.43</td>\n",
       "      <td>6.56</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>63.75</td>\n",
       "      <td>18.94</td>\n",
       "      <td>196.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>18.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>246.9</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.77</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                            27.76   \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                            63.75   \n",
       "                               $\\sigma_\\eta^2 = 0$             18.96   \n",
       "\n",
       "                                                    $\\sigma_\\eta^2$  \\\n",
       "Series           Time range    Restrictions                           \n",
       "US GNP           1948:1-2008:1 None                           31.99   \n",
       "US Prices        1948:1-2008:1 None                           61.89   \n",
       "                               $\\sigma_\\eta^2 = 0$              NaN   \n",
       "US monetary base 1948:1-2008:1 None                           18.94   \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",
       "                               $\\sigma_\\eta^2 = 0$              20.71    0.43   \n",
       "US monetary base 1948:1-2008:1 None                             196.3    0.89   \n",
       "                               $\\sigma_\\eta^2 = 0$              246.9    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",
       "                               $\\sigma_\\eta^2 = 0$                 6.56   \n",
       "US monetary base 1948:1-2008:1 None                               23.17   \n",
       "                               $\\sigma_\\eta^2 = 0$                23.77   \n",
       "\n",
       "                                                    $\\sigma_\\varepsilon^2$  \n",
       "Series           Time range    Restrictions                                 \n",
       "US GNP           1948:1-2008:1 None                                     14  \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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