{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SARIMAX: Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook replicates examples from the Stata ARIMA time series estimation and postestimation documentation.\n",
    "\n",
    "First, we replicate the four estimation examples http://www.stata.com/manuals13/tsarima.pdf:\n",
    "\n",
    "1. ARIMA(1,1,1) model on the U.S. Wholesale Price Index (WPI) dataset.\n",
    "2. Variation of example 1 which adds an MA(4) term to the ARIMA(1,1,1) specification to allow for an additive seasonal effect.\n",
    "3. ARIMA(2,1,0) x (1,1,0,12) model of monthly airline data. This example allows a multiplicative seasonal effect.\n",
    "4. ARMA(1,1) model with exogenous regressors; describes consumption as an autoregressive process on which also the money supply is assumed to be an explanatory variable.\n",
    "\n",
    "Second, we demonstrate postestimation capabilities to replicate http://www.stata.com/manuals13/tsarimapostestimation.pdf. The model from example 4 is used to demonstrate:\n",
    "\n",
    "1. One-step-ahead in-sample prediction\n",
    "2. n-step-ahead out-of-sample forecasting\n",
    "3. n-step-ahead in-sample dynamic prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:06.346680Z",
     "iopub.status.busy": "2026-06-27T20:40:06.346461Z",
     "iopub.status.idle": "2026-06-27T20:40:07.443839Z",
     "shell.execute_reply": "2026-06-27T20:40:07.442909Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:07.449561Z",
     "iopub.status.busy": "2026-06-27T20:40:07.449252Z",
     "iopub.status.idle": "2026-06-27T20:40:11.490761Z",
     "shell.execute_reply": "2026-06-27T20:40:11.489910Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from io import BytesIO\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Register converters to avoid warnings\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "plt.rc(\"figure\", figsize=(16, 8))\n",
    "plt.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Example 1: Arima\n",
    "\n",
    "As can be seen in the graphs from Example 2, the Wholesale price index (WPI) is growing over time (i.e. is not stationary). Therefore an ARMA model is not a good specification. In this first example, we consider a model where the original time series is assumed to be integrated of order 1, so that the difference is assumed to be stationary, and fit a model with one autoregressive lag and one moving average lag, as well as an intercept term.\n",
    "\n",
    "The postulated data process is then:\n",
    "\n",
    "$$\n",
    "\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "where $c$ is the intercept of the ARMA model, $\\Delta$ is the first-difference operator, and we assume $\\epsilon_{t} \\sim N(0, \\sigma^2)$. This can be rewritten to emphasize lag polynomials as (this will be useful in example 2, below):\n",
    "\n",
    "$$\n",
    "(1 - \\phi_1 L ) \\Delta y_t = c + (1 + \\theta_1 L) \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "where $L$ is the lag operator.\n",
    "\n",
    "Notice that one difference between the Stata output and the output below is that Stata estimates the following model:\n",
    "\n",
    "$$\n",
    "(\\Delta y_t - \\beta_0) = \\phi_1 ( \\Delta y_{t-1} - \\beta_0) + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "where $\\beta_0$ is the mean of the process $y_t$. This model is equivalent to the one estimated in the statsmodels SARIMAX class, but the interpretation is different. To see the equivalence, note that:\n",
    "\n",
    "$$\n",
    "(\\Delta y_t - \\beta_0) = \\phi_1 ( \\Delta y_{t-1} - \\beta_0) + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t} \\\\\n",
    "\\Delta y_t = (1 - \\phi_1) \\beta_0 + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "so that $c = (1 - \\phi_1) \\beta_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:11.492845Z",
     "iopub.status.busy": "2026-06-27T20:40:11.492498Z",
     "iopub.status.idle": "2026-06-27T20:40:12.480699Z",
     "shell.execute_reply": "2026-06-27T20:40:12.479911Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                    wpi   No. Observations:                  124\n",
      "Model:               SARIMAX(1, 1, 1)   Log Likelihood                -135.351\n",
      "Date:                Sat, 27 Jun 2026   AIC                            278.703\n",
      "Time:                        20:40:12   BIC                            289.951\n",
      "Sample:                    01-01-1960   HQIC                           283.272\n",
      "                         - 10-01-1990                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept      0.0943      0.068      1.389      0.165      -0.039       0.227\n",
      "ar.L1          0.8742      0.055     16.028      0.000       0.767       0.981\n",
      "ma.L1         -0.4120      0.100     -4.119      0.000      -0.608      -0.216\n",
      "sigma2         0.5257      0.053      9.849      0.000       0.421       0.630\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.09   Jarque-Bera (JB):                 9.78\n",
      "Prob(Q):                              0.77   Prob(JB):                         0.01\n",
      "Heteroskedasticity (H):              15.93   Skew:                             0.28\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         4.26\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Dataset\n",
    "wpi1 = requests.get(\"https://www.stata-press.com/data/r12/wpi1.dta\").content\n",
    "data = pd.read_stata(BytesIO(wpi1))\n",
    "data.index = data.t\n",
    "# Set the frequency\n",
    "data.index.freq = \"QS-OCT\"\n",
    "\n",
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(data[\"wpi\"], trend=\"c\", order=(1, 1, 1))\n",
    "res = mod.fit(disp=False)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus the maximum likelihood estimates imply that for the process above, we have:\n",
    "\n",
    "$$\n",
    "\\Delta y_t = 0.0943 + 0.8742 \\Delta y_{t-1} - 0.4120 \\epsilon_{t-1} + \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "where $\\epsilon_{t} \\sim N(0, 0.5257)$. Finally, recall that $c = (1 - \\phi_1) \\beta_0$, and here $c = 0.0943$ and $\\phi_1 = 0.8742$. To compare with the output from Stata, we could calculate the mean:\n",
    "\n",
    "$$\\beta_0 = \\frac{c}{1 - \\phi_1} = \\frac{0.0943}{1 - 0.8742} = 0.7496$$\n",
    "\n",
    "**Note**: This value is virtually identical to the value in the Stata documentation, $\\beta_0 = 0.7498$. The slight difference is likely down to rounding and subtle differences in stopping criterion of the numerical optimizers used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Example 2: Arima with additive seasonal effects\n",
    "\n",
    "This model is an extension of that from example 1. Here the data is assumed to follow the process:\n",
    "\n",
    "$$\n",
    "\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "The new part of this model is that there is allowed to be a annual seasonal effect (it is annual even though the periodicity is 4 because the dataset is quarterly). The second difference is that this model uses the log of the data rather than the level.\n",
    "\n",
    "Before estimating the dataset, graphs showing:\n",
    "\n",
    "1. The time series (in logs)\n",
    "2. The first difference of the time series (in logs)\n",
    "3. The autocorrelation function\n",
    "4. The partial autocorrelation function.\n",
    "\n",
    "From the first two graphs, we note that the original time series does not appear to be stationary, whereas the first-difference does. This supports either estimating an ARMA model on the first-difference of the data, or estimating an ARIMA model with 1 order of integration (recall that we are taking the latter approach). The last two graphs support the use of an ARMA(1,1,1) model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:12.496042Z",
     "iopub.status.busy": "2026-06-27T20:40:12.490836Z",
     "iopub.status.idle": "2026-06-27T20:40:12.540920Z",
     "shell.execute_reply": "2026-06-27T20:40:12.531910Z"
    }
   },
   "outputs": [],
   "source": [
    "# Dataset\n",
    "data = pd.read_stata(BytesIO(wpi1))\n",
    "data.index = data.t\n",
    "data.index.freq = \"QS-OCT\"\n",
    "\n",
    "data[\"ln_wpi\"] = np.log(data[\"wpi\"])\n",
    "data[\"D.ln_wpi\"] = data[\"ln_wpi\"].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:12.548865Z",
     "iopub.status.busy": "2026-06-27T20:40:12.548572Z",
     "iopub.status.idle": "2026-06-27T20:40:13.817535Z",
     "shell.execute_reply": "2026-06-27T20:40:13.816915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph data\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4))\n",
    "\n",
    "# Levels\n",
    "axes[0].plot(data.index._mpl_repr(), data[\"wpi\"], \"-\")\n",
    "axes[0].set(title=\"US Wholesale Price Index\")\n",
    "\n",
    "# Log difference\n",
    "axes[1].plot(data.index._mpl_repr(), data[\"D.ln_wpi\"], \"-\")\n",
    "axes[1].hlines(0, data.index[0], data.index[-1], \"r\")\n",
    "axes[1].set(title=\"US Wholesale Price Index - difference of logs\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:13.820449Z",
     "iopub.status.busy": "2026-06-27T20:40:13.820236Z",
     "iopub.status.idle": "2026-06-27T20:40:14.732643Z",
     "shell.execute_reply": "2026-06-27T20:40:14.731731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph data\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4))\n",
    "\n",
    "fig = sm.graphics.tsa.plot_acf(data.iloc[1:][\"D.ln_wpi\"], lags=40, ax=axes[0])\n",
    "fig = sm.graphics.tsa.plot_pacf(data.iloc[1:][\"D.ln_wpi\"], lags=40, ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand how to specify this model in statsmodels, first recall that from example 1 we used the following code to specify the ARIMA(1,1,1) model:\n",
    "\n",
    "```python\n",
    "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,1))\n",
    "```\n",
    "\n",
    "The `order` argument is a tuple of the form `(AR specification, Integration order, MA specification)`. The integration order must be an integer (for example, here we assumed one order of integration, so it was specified as 1. In a pure ARMA model where the underlying data is already stationary, it would be 0).\n",
    "\n",
    "For the AR specification and MA specification components, there are two possibilities. The first is to specify the **maximum degree** of the corresponding lag polynomial, in which case the component is an integer. For example, if we wanted to specify an ARIMA(1,1,4) process, we would use:\n",
    "\n",
    "```python\n",
    "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(1,1,4))\n",
    "```\n",
    "\n",
    "and the corresponding data process would be:\n",
    "\n",
    "$$\n",
    "y_t = c + \\phi_1 y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_2 \\epsilon_{t-2} + \\theta_3 \\epsilon_{t-3} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "or\n",
    "\n",
    "$$\n",
    "(1 - \\phi_1 L)\\Delta y_t = c + (1 + \\theta_1 L + \\theta_2 L^2 + \\theta_3 L^3 + \\theta_4 L^4) \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "When the specification parameter is given as a maximum degree of the lag polynomial, it implies that all polynomial terms up to that degree are included. Notice that this is *not* the model we want to use, because it would include terms for $\\epsilon_{t-2}$ and $\\epsilon_{t-3}$, which we do not want here.\n",
    "\n",
    "What we want is a polynomial that has terms for the 1st and 4th degrees, but leaves out the 2nd and 3rd terms. To do that, we need to provide a tuple for the specification parameter, where the tuple describes **the lag polynomial itself**. In particular, here we would want to use:\n",
    "\n",
    "```python\n",
    "ar = 1          # this is the maximum degree specification\n",
    "ma = (1,0,0,1)  # this is the lag polynomial specification\n",
    "mod = sm.tsa.statespace.SARIMAX(data['wpi'], trend='c', order=(ar,1,ma)))\n",
    "```\n",
    "\n",
    "This gives the following form for the process of the data:\n",
    "\n",
    "$$\n",
    "\\Delta y_t = c + \\phi_1 \\Delta y_{t-1} + \\theta_1 \\epsilon_{t-1} + \\theta_4 \\epsilon_{t-4} + \\epsilon_{t} \\\\\n",
    "(1 - \\phi_1 L)\\Delta y_t = c + (1 + \\theta_1 L + \\theta_4 L^4) \\epsilon_{t}\n",
    "$$\n",
    "\n",
    "which is what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:14.735700Z",
     "iopub.status.busy": "2026-06-27T20:40:14.735430Z",
     "iopub.status.idle": "2026-06-27T20:40:24.803136Z",
     "shell.execute_reply": "2026-06-27T20:40:24.799810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 SARIMAX Results                                 \n",
      "=================================================================================\n",
      "Dep. Variable:                    ln_wpi   No. Observations:                  124\n",
      "Model:             SARIMAX(1, 1, [1, 4])   Log Likelihood                 386.033\n",
      "Date:                   Sat, 27 Jun 2026   AIC                           -762.067\n",
      "Time:                           20:40:24   BIC                           -748.006\n",
      "Sample:                       01-01-1960   HQIC                          -756.355\n",
      "                            - 10-01-1990                                         \n",
      "Covariance Type:                     opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept      0.0024      0.002      1.481      0.139      -0.001       0.006\n",
      "ar.L1          0.7817      0.094      8.296      0.000       0.597       0.966\n",
      "ma.L1         -0.3991      0.126     -3.174      0.002      -0.645      -0.153\n",
      "ma.L4          0.3083      0.120      2.569      0.010       0.073       0.543\n",
      "sigma2         0.0001    9.8e-06     11.109      0.000    8.97e-05       0.000\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                45.25\n",
      "Prob(Q):                              0.91   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.58   Skew:                             0.28\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.92\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(data[\"ln_wpi\"], trend=\"c\", order=(1, 1, (1, 0, 0, 1)))\n",
    "res = mod.fit(disp=False)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Example 3: Airline Model\n",
    "\n",
    "In the previous example, we included a seasonal effect in an *additive* way, meaning that we added a term allowing the process to depend on the 4th MA lag. It may be instead that we want to model a seasonal effect in a multiplicative way. We often write the model then as an ARIMA $(p,d,q) \\times (P,D,Q)_s$, where the lowercase letters indicate the specification for the non-seasonal component, and the uppercase letters indicate the specification for the seasonal component; $s$ is the periodicity of the seasons (e.g. it is often 4 for quarterly data or 12 for monthly data). The data process can be written generically as:\n",
    "\n",
    "$$\n",
    "\\phi_p (L) \\tilde \\phi_P (L^s) \\Delta^d \\Delta_s^D y_t = A(t) + \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n",
    "$$\n",
    "\n",
    "where:\n",
    "\n",
    "- $\\phi_p (L)$ is the non-seasonal autoregressive lag polynomial\n",
    "- $\\tilde \\phi_P (L^s)$ is the seasonal autoregressive lag polynomial\n",
    "- $\\Delta^d \\Delta_s^D y_t$ is the time series, differenced $d$ times, and seasonally differenced $D$ times.\n",
    "- $A(t)$ is the trend polynomial (including the intercept)\n",
    "- $\\theta_q (L)$ is the non-seasonal moving average lag polynomial\n",
    "- $\\tilde \\theta_Q (L^s)$ is the seasonal moving average lag polynomial\n",
    "\n",
    "sometimes we rewrite this as:\n",
    "\n",
    "$$\n",
    "\\phi_p (L) \\tilde \\phi_P (L^s) y_t^* = A(t) + \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n",
    "$$\n",
    "\n",
    "where $y_t^* = \\Delta^d \\Delta_s^D y_t$. This emphasizes that just as in the simple case, after we take differences (here both non-seasonal and seasonal) to make the data stationary, the resulting model is just an ARMA model.\n",
    "\n",
    "As an example, consider the airline model ARIMA $(2,1,0) \\times (1,1,0)_{12}$, with an intercept. The data process can be written in the form above as:\n",
    "\n",
    "$$\n",
    "(1 - \\phi_1 L - \\phi_2 L^2) (1 - \\tilde \\phi_1 L^{12}) \\Delta \\Delta_{12} y_t = c + \\epsilon_t\n",
    "$$\n",
    "\n",
    "Here, we have:\n",
    "\n",
    "- $\\phi_p (L) = (1 - \\phi_1 L - \\phi_2 L^2)$\n",
    "- $\\tilde \\phi_P (L^s) = (1 - \\phi_1 L^12)$\n",
    "- $d = 1, D = 1, s=12$ indicating that $y_t^*$ is derived from $y_t$ by taking first-differences and then taking 12-th differences.\n",
    "- $A(t) = c$ is the *constant* trend polynomial (i.e. just an intercept)\n",
    "- $\\theta_q (L) = \\tilde \\theta_Q (L^s) = 1$ (i.e. there is no moving average effect)\n",
    "\n",
    "It may still be confusing to see the two lag polynomials in front of the time-series variable, but notice that we can multiply the lag polynomials together to get the following model:\n",
    "\n",
    "$$\n",
    "(1 - \\phi_1 L - \\phi_2 L^2 - \\tilde \\phi_1 L^{12} + \\phi_1 \\tilde \\phi_1 L^{13} + \\phi_2 \\tilde \\phi_1 L^{14} ) y_t^* = c + \\epsilon_t\n",
    "$$\n",
    "\n",
    "which can be rewritten as:\n",
    "\n",
    "$$\n",
    "y_t^* = c + \\phi_1 y_{t-1}^* + \\phi_2 y_{t-2}^* + \\tilde \\phi_1 y_{t-12}^* - \\phi_1 \\tilde \\phi_1 y_{t-13}^* - \\phi_2 \\tilde \\phi_1 y_{t-14}^* + \\epsilon_t\n",
    "$$\n",
    "\n",
    "This is similar to the additively seasonal model from example 2, but the coefficients in front of the autoregressive lags are actually combinations of the underlying seasonal and non-seasonal parameters.\n",
    "\n",
    "Specifying the model in statsmodels is done simply by adding the `seasonal_order` argument, which accepts a tuple of the form `(Seasonal AR specification, Seasonal Integration order, Seasonal MA, Seasonal periodicity)`. The seasonal AR and MA specifications, as before, can be expressed as a maximum polynomial degree or as the lag polynomial itself. Seasonal periodicity is an integer.\n",
    "\n",
    "For the airline model ARIMA $(2,1,0) \\times (1,1,0)_{12}$ with an intercept, the command is:\n",
    "\n",
    "```python\n",
    "mod = sm.tsa.statespace.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:24.805243Z",
     "iopub.status.busy": "2026-06-27T20:40:24.805044Z",
     "iopub.status.idle": "2026-06-27T20:40:30.100546Z",
     "shell.execute_reply": "2026-06-27T20:40:30.099919Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                      \n",
      "==========================================================================================\n",
      "Dep. Variable:                       D.DS12.lnair   No. Observations:                  131\n",
      "Model:             SARIMAX(2, 0, 0)x(1, 0, 0, 12)   Log Likelihood                 240.821\n",
      "Date:                            Sat, 27 Jun 2026   AIC                           -473.643\n",
      "Time:                                    20:40:30   BIC                           -462.142\n",
      "Sample:                                02-01-1950   HQIC                          -468.970\n",
      "                                     - 12-01-1960                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.4057      0.080     -5.045      0.000      -0.563      -0.248\n",
      "ar.L2         -0.0799      0.099     -0.809      0.419      -0.274       0.114\n",
      "ar.S.L12      -0.4723      0.072     -6.592      0.000      -0.613      -0.332\n",
      "sigma2         0.0014      0.000      8.403      0.000       0.001       0.002\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                 0.72\n",
      "Prob(Q):                              0.91   Prob(JB):                         0.70\n",
      "Heteroskedasticity (H):               0.54   Skew:                             0.14\n",
      "Prob(H) (two-sided):                  0.04   Kurtosis:                         3.23\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Dataset\n",
    "air2 = requests.get(\"https://www.stata-press.com/data/r12/air2.dta\").content\n",
    "data = pd.read_stata(BytesIO(air2))\n",
    "data.index = pd.date_range(\n",
    "    start=datetime(int(data.time[0]), 1, 1), periods=len(data), freq=\"MS\"\n",
    ")\n",
    "data[\"lnair\"] = np.log(data[\"air\"])\n",
    "\n",
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(\n",
    "    data[\"lnair\"],\n",
    "    order=(2, 1, 0),\n",
    "    seasonal_order=(1, 1, 0, 12),\n",
    "    simple_differencing=True,\n",
    ")\n",
    "res = mod.fit(disp=False)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that here we used an additional argument `simple_differencing=True`. This controls how the order of integration is handled in ARIMA models. If `simple_differencing=True`, then the time series provided as `endog` is literally differenced and an ARMA model is fit to the resulting new time series. This implies that a number of initial periods are lost to the differencing process, however it may be necessary either to compare results to other packages (e.g. Stata's `arima` always uses  simple differencing) or if the seasonal periodicity is large.\n",
    "\n",
    "The default is `simple_differencing=False`, in which case the integration component is implemented as part of the state space formulation, and all of the original data can be used in estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Example 4: ARMAX (Friedman)\n",
    "\n",
    "This model demonstrates the use of explanatory variables (the X part of ARMAX). When exogenous regressors are included, the SARIMAX module uses the concept of \"regression with SARIMA errors\" (see http://robjhyndman.com/hyndsight/arimax/ for details of regression with ARIMA errors versus alternative specifications), so that the model is specified as:\n",
    "\n",
    "$$\n",
    "y_t = \\beta_t x_t + u_t \\\\\n",
    "        \\phi_p (L) \\tilde \\phi_P (L^s) \\Delta^d \\Delta_s^D u_t = A(t) +\n",
    "            \\theta_q (L) \\tilde \\theta_Q (L^s) \\epsilon_t\n",
    "$$\n",
    "\n",
    "Notice that the first equation is just a linear regression, and the second equation just describes the process followed by the error component as SARIMA (as was described in example 3). One reason for this specification is that the estimated parameters have their natural interpretations.\n",
    "\n",
    "This specification nests many simpler specifications. For example, regression with AR(2) errors is:\n",
    "\n",
    "$$\n",
    "y_t = \\beta_t x_t + u_t \\\\\n",
    "(1 - \\phi_1 L - \\phi_2 L^2) u_t = A(t) + \\epsilon_t\n",
    "$$\n",
    "\n",
    "The model considered in this example is regression with ARMA(1,1) errors. The process is then written:\n",
    "\n",
    "$$\n",
    "\\text{consump}_t = \\beta_0 + \\beta_1 \\text{m2}_t + u_t \\\\\n",
    "(1 - \\phi_1 L) u_t = (1 - \\theta_1 L) \\epsilon_t\n",
    "$$\n",
    "\n",
    "Notice that $\\beta_0$ is, as described in example 1 above, *not* the same thing as an intercept specified by `trend='c'`. Whereas in the examples above we estimated the intercept of the model via the trend polynomial, here, we demonstrate how to estimate $\\beta_0$ itself by adding a constant to the exogenous dataset. In the output, the $beta_0$ is called `const`, whereas above the intercept $c$ was called `intercept` in the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:30.103220Z",
     "iopub.status.busy": "2026-06-27T20:40:30.103013Z",
     "iopub.status.idle": "2026-06-27T20:40:32.028308Z",
     "shell.execute_reply": "2026-06-27T20:40:32.027534Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                consump   No. Observations:                   92\n",
      "Model:               SARIMAX(1, 0, 1)   Log Likelihood                -340.508\n",
      "Date:                Sat, 27 Jun 2026   AIC                            691.015\n",
      "Time:                        20:40:31   BIC                            703.624\n",
      "Sample:                    01-01-1959   HQIC                           696.105\n",
      "                         - 10-01-1981                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -36.0605     56.643     -0.637      0.524    -147.079      74.958\n",
      "m2             1.1220      0.036     30.824      0.000       1.051       1.193\n",
      "ar.L1          0.9348      0.041     22.717      0.000       0.854       1.016\n",
      "ma.L1          0.3091      0.089      3.488      0.000       0.135       0.483\n",
      "sigma2        93.2556     10.889      8.565      0.000      71.914     114.597\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):                23.49\n",
      "Prob(Q):                              0.84   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):              22.51   Skew:                             0.17\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.45\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Dataset\n",
    "friedman2 = requests.get(\"https://www.stata-press.com/data/r12/friedman2.dta\").content\n",
    "data = pd.read_stata(BytesIO(friedman2))\n",
    "data.index = data.time\n",
    "data.index.freq = \"QS-OCT\"\n",
    "\n",
    "# Variables\n",
    "endog = data.loc[\"1959\":\"1981\", \"consump\"]\n",
    "exog = sm.add_constant(data.loc[\"1959\":\"1981\", \"m2\"])\n",
    "\n",
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(endog, exog, order=(1, 0, 1))\n",
    "res = mod.fit(disp=False)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Postestimation: Example 1 - Dynamic Forecasting\n",
    "\n",
    "Here we describe some of the post-estimation capabilities of statsmodels' SARIMAX.\n",
    "\n",
    "First, using the model from example, we estimate the parameters using data that *excludes the last few observations* (this is a little artificial as an example, but it allows considering performance of out-of-sample forecasting and facilitates comparison to Stata's documentation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:32.031711Z",
     "iopub.status.busy": "2026-06-27T20:40:32.031496Z",
     "iopub.status.idle": "2026-06-27T20:40:34.056358Z",
     "shell.execute_reply": "2026-06-27T20:40:34.055496Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                consump   No. Observations:                   77\n",
      "Model:               SARIMAX(1, 0, 1)   Log Likelihood                -243.316\n",
      "Date:                Sat, 27 Jun 2026   AIC                            496.633\n",
      "Time:                        20:40:34   BIC                            508.352\n",
      "Sample:                    01-01-1959   HQIC                           501.320\n",
      "                         - 01-01-1978                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.6775     18.492      0.037      0.971     -35.567      36.922\n",
      "m2             1.0379      0.021     50.328      0.000       0.997       1.078\n",
      "ar.L1          0.8775      0.059     14.859      0.000       0.762       0.993\n",
      "ma.L1          0.2771      0.108      2.571      0.010       0.066       0.488\n",
      "sigma2        31.6991      4.683      6.769      0.000      22.520      40.878\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.32   Jarque-Bera (JB):                 6.05\n",
      "Prob(Q):                              0.57   Prob(JB):                         0.05\n",
      "Heteroskedasticity (H):               6.09   Skew:                             0.57\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.76\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Dataset\n",
    "raw = pd.read_stata(BytesIO(friedman2))\n",
    "raw.index = raw.time\n",
    "raw.index.freq = \"QS-OCT\"\n",
    "data = raw.loc[:\"1981\"]\n",
    "\n",
    "# Variables\n",
    "endog = data.loc[\"1959\":, \"consump\"]\n",
    "exog = sm.add_constant(data.loc[\"1959\":, \"m2\"])\n",
    "nobs = endog.shape[0]\n",
    "\n",
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(\n",
    "    endog.loc[:\"1978-01-01\"], exog=exog.loc[:\"1978-01-01\"], order=(1, 0, 1)\n",
    ")\n",
    "fit_res = mod.fit(disp=False, maxiter=250)\n",
    "print(fit_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to get results for the full dataset but using the estimated parameters (on a subset of the data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:34.066402Z",
     "iopub.status.busy": "2026-06-27T20:40:34.066181Z",
     "iopub.status.idle": "2026-06-27T20:40:34.119843Z",
     "shell.execute_reply": "2026-06-27T20:40:34.119029Z"
    }
   },
   "outputs": [],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(endog, exog=exog, order=(1, 0, 1))\n",
    "res = mod.filter(fit_res.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `predict` command is first applied here to get in-sample predictions. We use the `full_results=True` argument to allow us to calculate confidence intervals (the default output of `predict` is just the predicted values).\n",
    "\n",
    "With no other arguments, `predict` returns the one-step-ahead in-sample predictions for the entire sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:34.124802Z",
     "iopub.status.busy": "2026-06-27T20:40:34.124330Z",
     "iopub.status.idle": "2026-06-27T20:40:34.139613Z",
     "shell.execute_reply": "2026-06-27T20:40:34.138724Z"
    }
   },
   "outputs": [],
   "source": [
    "# In-sample one-step-ahead predictions\n",
    "predict = res.get_prediction()\n",
    "predict_ci = predict.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get *dynamic predictions*. One-step-ahead prediction uses the true values of the endogenous values at each step to predict the next in-sample value. Dynamic predictions use one-step-ahead prediction up to some point in the dataset (specified by the `dynamic` argument); after that, the previous *predicted* endogenous values are used in place of the true endogenous values for each new predicted element.\n",
    "\n",
    "The `dynamic` argument is specified to be an *offset* relative to the `start` argument. If `start` is not specified, it is assumed to be `0`.\n",
    "\n",
    "Here we perform dynamic prediction starting in the first quarter of 1978."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:34.146735Z",
     "iopub.status.busy": "2026-06-27T20:40:34.146522Z",
     "iopub.status.idle": "2026-06-27T20:40:34.165660Z",
     "shell.execute_reply": "2026-06-27T20:40:34.164842Z"
    }
   },
   "outputs": [],
   "source": [
    "# Dynamic predictions\n",
    "predict_dy = res.get_prediction(dynamic=\"1978-01-01\")\n",
    "predict_dy_ci = predict_dy.conf_int()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can graph the one-step-ahead and dynamic predictions (and the corresponding confidence intervals) to see their relative performance. Notice that up to the point where dynamic prediction begins (1978:Q1), the two are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:34.168935Z",
     "iopub.status.busy": "2026-06-27T20:40:34.168700Z",
     "iopub.status.idle": "2026-06-27T20:40:34.961597Z",
     "shell.execute_reply": "2026-06-27T20:40:34.960915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph\n",
    "fig, ax = plt.subplots(figsize=(9, 4))\n",
    "npre = 4\n",
    "ax.set(title=\"Personal consumption\", xlabel=\"Date\", ylabel=\"Billions of dollars\")\n",
    "\n",
    "# Plot data points\n",
    "data.loc[\"1977-07-01\":, \"consump\"].plot(ax=ax, style=\"o\", label=\"Observed\")\n",
    "\n",
    "# Plot predictions\n",
    "predict.predicted_mean.loc[\"1977-07-01\":].plot(\n",
    "    ax=ax, style=\"r--\", label=\"One-step-ahead forecast\"\n",
    ")\n",
    "ci = predict_ci.loc[\"1977-07-01\":]\n",
    "ax.fill_between(ci.index, ci.iloc[:, 0], ci.iloc[:, 1], color=\"r\", alpha=0.1)\n",
    "predict_dy.predicted_mean.loc[\"1977-07-01\":].plot(\n",
    "    ax=ax, style=\"g\", label=\"Dynamic forecast (1978)\"\n",
    ")\n",
    "ci = predict_dy_ci.loc[\"1977-07-01\":]\n",
    "ax.fill_between(ci.index, ci.iloc[:, 0], ci.iloc[:, 1], color=\"g\", alpha=0.1)\n",
    "\n",
    "legend = ax.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, graph the prediction *error*. It is obvious that, as one would suspect, one-step-ahead prediction is considerably better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-27T20:40:34.966807Z",
     "iopub.status.busy": "2026-06-27T20:40:34.966590Z",
     "iopub.status.idle": "2026-06-27T20:40:35.487522Z",
     "shell.execute_reply": "2026-06-27T20:40:35.486571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prediction error\n",
    "\n",
    "# Graph\n",
    "fig, ax = plt.subplots(figsize=(9, 4))\n",
    "npre = 4\n",
    "ax.set(title=\"Forecast error\", xlabel=\"Date\", ylabel=\"Forecast - Actual\")\n",
    "\n",
    "# In-sample one-step-ahead predictions and 95% confidence intervals\n",
    "predict_error = predict.predicted_mean - endog\n",
    "predict_error.loc[\"1977-10-01\":].plot(ax=ax, label=\"One-step-ahead forecast\")\n",
    "ci = predict_ci.loc[\"1977-10-01\":].copy()\n",
    "ci.iloc[:, 0] -= endog.loc[\"1977-10-01\":]\n",
    "ci.iloc[:, 1] -= endog.loc[\"1977-10-01\":]\n",
    "ax.fill_between(ci.index, ci.iloc[:, 0], ci.iloc[:, 1], alpha=0.1)\n",
    "\n",
    "# Dynamic predictions and 95% confidence intervals\n",
    "predict_dy_error = predict_dy.predicted_mean - endog\n",
    "predict_dy_error.loc[\"1977-10-01\":].plot(\n",
    "    ax=ax, style=\"r\", label=\"Dynamic forecast (1978)\"\n",
    ")\n",
    "ci = predict_dy_ci.loc[\"1977-10-01\":].copy()\n",
    "ci.iloc[:, 0] -= endog.loc[\"1977-10-01\":]\n",
    "ci.iloc[:, 1] -= endog.loc[\"1977-10-01\":]\n",
    "ax.fill_between(ci.index, ci.iloc[:, 0], ci.iloc[:, 1], color=\"r\", alpha=0.1)\n",
    "\n",
    "legend = ax.legend(loc=\"lower left\")\n",
    "legend.get_frame().set_facecolor(\"w\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.14.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}