{
 "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": "2024-04-19T16:43:12.335009Z",
     "iopub.status.busy": "2024-04-19T16:43:12.334779Z",
     "iopub.status.idle": "2024-04-19T16:43:13.910713Z",
     "shell.execute_reply": "2024-04-19T16:43:13.909956Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-19T16:43:13.921272Z",
     "iopub.status.busy": "2024-04-19T16:43:13.919524Z",
     "iopub.status.idle": "2024-04-19T16:43:16.906836Z",
     "shell.execute_reply": "2024-04-19T16:43:16.906156Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import norm\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import requests\n",
    "from io import BytesIO\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": "2024-04-19T16:43:16.910506Z",
     "iopub.status.busy": "2024-04-19T16:43:16.909960Z",
     "iopub.status.idle": "2024-04-19T16:43:17.633330Z",
     "shell.execute_reply": "2024-04-19T16:43:17.628260Z"
    }
   },
   "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:                Fri, 19 Apr 2024   AIC                            278.703\n",
      "Time:                        16:43:17   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": "2024-04-19T16:43:17.638491Z",
     "iopub.status.busy": "2024-04-19T16:43:17.638206Z",
     "iopub.status.idle": "2024-04-19T16:43:17.659221Z",
     "shell.execute_reply": "2024-04-19T16:43:17.658626Z"
    }
   },
   "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": "2024-04-19T16:43:17.662785Z",
     "iopub.status.busy": "2024-04-19T16:43:17.661518Z",
     "iopub.status.idle": "2024-04-19T16:43:19.295472Z",
     "shell.execute_reply": "2024-04-19T16:43:19.294720Z"
    }
   },
   "outputs": [
    {
     "data": {
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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": "2024-04-19T16:43:19.298842Z",
     "iopub.status.busy": "2024-04-19T16:43:19.298475Z",
     "iopub.status.idle": "2024-04-19T16:43:20.715932Z",
     "shell.execute_reply": "2024-04-19T16:43:20.715285Z"
    }
   },
   "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": "2024-04-19T16:43:20.719296Z",
     "iopub.status.busy": "2024-04-19T16:43:20.718887Z",
     "iopub.status.idle": "2024-04-19T16:43:50.248801Z",
     "shell.execute_reply": "2024-04-19T16:43:50.248075Z"
    }
   },
   "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                 386.034\n",
      "Date:                   Fri, 19 Apr 2024   AIC                           -762.067\n",
      "Time:                           16:43:50   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      1.485      0.138      -0.001       0.006\n",
      "ar.L1          0.7808      0.094      8.269      0.000       0.596       0.966\n",
      "ma.L1         -0.3993      0.126     -3.174      0.002      -0.646      -0.153\n",
      "ma.L4          0.3087      0.120      2.572      0.010       0.073       0.544\n",
      "sigma2         0.0001    9.8e-06     11.114      0.000    8.97e-05       0.000\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):                45.15\n",
      "Prob(Q):                              0.90   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.58   Skew:                             0.29\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.91\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": "2024-04-19T16:43:50.252346Z",
     "iopub.status.busy": "2024-04-19T16:43:50.252095Z",
     "iopub.status.idle": "2024-04-19T16:44:04.271853Z",
     "shell.execute_reply": "2024-04-19T16:44:04.271226Z"
    }
   },
   "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:                            Fri, 19 Apr 2024   AIC                           -473.643\n",
      "Time:                                    16:44:04   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(start=datetime(int(data.time[0]), 1, 1), periods=len(data), freq='MS')\n",
    "data['lnair'] = np.log(data['air'])\n",
    "\n",
    "# Fit the model\n",
    "mod = sm.tsa.statespace.SARIMAX(data['lnair'], order=(2,1,0), seasonal_order=(1,1,0,12), simple_differencing=True)\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": "2024-04-19T16:44:04.279543Z",
     "iopub.status.busy": "2024-04-19T16:44:04.278848Z",
     "iopub.status.idle": "2024-04-19T16:44:05.561807Z",
     "shell.execute_reply": "2024-04-19T16:44:05.561060Z"
    }
   },
   "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:                Fri, 19 Apr 2024   AIC                            691.015\n",
      "Time:                        16:44:05   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        -36.0606     56.643     -0.637      0.524    -147.078      74.957\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       1.015\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": "2024-04-19T16:44:05.565047Z",
     "iopub.status.busy": "2024-04-19T16:44:05.564744Z",
     "iopub.status.idle": "2024-04-19T16:44:06.609401Z",
     "shell.execute_reply": "2024-04-19T16:44:06.608667Z"
    }
   },
   "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:                Fri, 19 Apr 2024   AIC                            496.633\n",
      "Time:                        16:44:06   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          0.6782     18.492      0.037      0.971     -35.565      36.922\n",
      "m2             1.0379      0.021     50.329      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      2.572      0.010       0.066       0.488\n",
      "sigma2        31.6979      4.683      6.769      0.000      22.520      40.876\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(endog.loc[:'1978-01-01'], exog=exog.loc[:'1978-01-01'], order=(1,0,1))\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": "2024-04-19T16:44:06.612622Z",
     "iopub.status.busy": "2024-04-19T16:44:06.612279Z",
     "iopub.status.idle": "2024-04-19T16:44:06.651406Z",
     "shell.execute_reply": "2024-04-19T16:44:06.650713Z"
    }
   },
   "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": "2024-04-19T16:44:06.654569Z",
     "iopub.status.busy": "2024-04-19T16:44:06.654336Z",
     "iopub.status.idle": "2024-04-19T16:44:06.670125Z",
     "shell.execute_reply": "2024-04-19T16:44:06.669427Z"
    }
   },
   "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": "2024-04-19T16:44:06.673154Z",
     "iopub.status.busy": "2024-04-19T16:44:06.672924Z",
     "iopub.status.idle": "2024-04-19T16:44:06.689896Z",
     "shell.execute_reply": "2024-04-19T16:44:06.684611Z"
    }
   },
   "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": "2024-04-19T16:44:06.693044Z",
     "iopub.status.busy": "2024-04-19T16:44:06.692814Z",
     "iopub.status.idle": "2024-04-19T16:44:07.332491Z",
     "shell.execute_reply": "2024-04-19T16:44:07.331794Z"
    }
   },
   "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(ax=ax, style='r--', label='One-step-ahead forecast')\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(ax=ax, style='g', label='Dynamic forecast (1978)')\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": "2024-04-19T16:44:07.335773Z",
     "iopub.status.busy": "2024-04-19T16:44:07.335522Z",
     "iopub.status.idle": "2024-04-19T16:44:07.966265Z",
     "shell.execute_reply": "2024-04-19T16:44:07.965590Z"
    }
   },
   "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(ax=ax, style='r', label='Dynamic forecast (1978)')\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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}