{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8732de12-d3f2-4a09-8c39-e5c52a5ac94a",
   "metadata": {},
   "source": [
    "# Autoregressive Distributed Lag (ARDL) models\n",
    "\n",
    "\n",
    "## ARDL Models\n",
    "\n",
    "Autoregressive Distributed Lag (ARDL) models extend Autoregressive models with lags of explanatory variables. While ARDL models are technically AR-X models, the key difference is that ARDL models focus on the exogenous variables and selecting the correct lag structure from both the endogenous variable and the exogenous variables.  ARDL models are also closely related to Vector Autoregressions, and a single ARDL is effectively one row of a VAR.  The key distinction is that an ARDL assumes that the exogenous variables are exogenous in the sense that it is not necessary to include the endogenous variable as a predictor of the exogenous variables.\n",
    "\n",
    "The full specification of ARDL models is\n",
    "\n",
    "$$\n",
    "Y_t = \\underset{\\text{Constant and Trend}}{\\underbrace{\\delta_0 + \\delta_1 t + \\ldots + \\delta_k t^k}} \n",
    "      + \\underset{\\text{Seasonal}}{\\underbrace{\\sum_{i=0}^{s-1} \\gamma_i S_i}}\n",
    "      + \\underset{\\text{Autoregressive}}{\\underbrace{\\sum_{p=1}^P \\phi_p Y_{t-p}}}\n",
    "      + \\underset{\\text{Distributed Lag}}{\\underbrace{\\sum_{k=1}^M \\sum_{j=0}^{Q_k} \\beta_{k,j} X_{k, t-j}}}\n",
    "      + \\underset{\\text{Fixed}}{\\underbrace{Z_t \\Gamma}} + \\epsilon_t\n",
    "$$\n",
    "\n",
    "The terms in the model are:\n",
    "\n",
    "* $\\delta_i$: constant and deterministic time regressors.  Set using `trend`.\n",
    "* $S_i$ are seasonal dummies which are included if `seasonal=True`.\n",
    "* $X_{k,t-j}$ are the exogenous regressors. There are a number of formats that can be used to specify which lags are included. Note that the included lag lengths do no need to be the same. If `causal=True`, then the lags start with lag 1. Otherwise lags begin with 0 so that the model included the contemporaneous relationship between $Y_t$ and $X_t$.\n",
    "* $Z_t$ are any other fixed regressors that are not part of the distributed lag specification. In practice these regressors may be included when they do no contribute to the long run-relationship between $Y_t$ and the vector of exogenous variables $X_t$.\n",
    "* $\\{\\epsilon_t\\}$ is assumed to be a White Noise process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f7bb53a8-63a9-4f11-a9db-eb09280d457d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:09:59.965816Z",
     "iopub.status.busy": "2026-07-28T19:09:59.965542Z",
     "iopub.status.idle": "2026-07-28T19:10:02.232856Z",
     "shell.execute_reply": "2026-07-28T19:10:02.231138Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.mpl.rc(\"figure\", figsize=(16, 6))\n",
    "sns.mpl.rc(\"font\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2df3123-3ef9-4bc5-bc3f-c2d3f2fe945d",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "This notebook makes use of money demand data from Denmark, as first used in  S. Johansen and K. Juselius (1990).  The key variables are:\n",
    "\n",
    "* `lrm`: Log of real money measured using M2\n",
    "* `lry`: Log of real income\n",
    "* `ibo`: Interest rate on bonds\n",
    "* `ide`: Interest rate of bank deposits\n",
    "\n",
    "The standard model uses `lrm` as the dependent variable and the other three as exogenous drivers.\n",
    "\n",
    "Johansen, S. and Juselius, K. (1990), Maximum Likelihood Estimation and Inference on Cointegration – with Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52, 2, 169–210.\n",
    "\n",
    "We start by loading the data and examining it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e835b518-53f6-45de-9c93-d397eaa08831",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:02.238790Z",
     "iopub.status.busy": "2026-07-28T19:10:02.238390Z",
     "iopub.status.idle": "2026-07-28T19:10:03.016804Z",
     "shell.execute_reply": "2026-07-28T19:10:03.015261Z"
    }
   },
   "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>lrm</th>\n",
       "      <th>lry</th>\n",
       "      <th>ibo</th>\n",
       "      <th>ide</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-07-01</th>\n",
       "      <td>12.056189</td>\n",
       "      <td>6.098992</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.067941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-10-01</th>\n",
       "      <td>12.071628</td>\n",
       "      <td>6.080706</td>\n",
       "      <td>0.114267</td>\n",
       "      <td>0.075396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>12.027952</td>\n",
       "      <td>6.061175</td>\n",
       "      <td>0.119333</td>\n",
       "      <td>0.076653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-04-01</th>\n",
       "      <td>12.039788</td>\n",
       "      <td>6.063730</td>\n",
       "      <td>0.117333</td>\n",
       "      <td>0.076259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-07-01</th>\n",
       "      <td>12.015294</td>\n",
       "      <td>6.050830</td>\n",
       "      <td>0.118967</td>\n",
       "      <td>0.075163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lrm       lry       ibo       ide\n",
       "period                                             \n",
       "1986-07-01  12.056189  6.098992  0.111500  0.067941\n",
       "1986-10-01  12.071628  6.080706  0.114267  0.075396\n",
       "1987-01-01  12.027952  6.061175  0.119333  0.076653\n",
       "1987-04-01  12.039788  6.063730  0.117333  0.076259\n",
       "1987-07-01  12.015294  6.050830  0.118967  0.075163"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.datasets.danish_data import load\n",
    "from statsmodels.tsa.api import ARDL\n",
    "from statsmodels.tsa.ardl import ardl_select_order\n",
    "\n",
    "data = load().data\n",
    "data = data[[\"lrm\", \"lry\", \"ibo\", \"ide\"]]\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32139ec2-4e69-4e53-a5b9-a6aef64edefe",
   "metadata": {},
   "source": [
    "We plot the demeaned data so that all series appear on the same scale. The `lrm` series appears to be non-stationary, as does `lry`. The stationarity of the other two is less obvious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ca52a18-3752-4c65-9043-6c91ba543d44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:03.018819Z",
     "iopub.status.busy": "2026-07-28T19:10:03.018565Z",
     "iopub.status.idle": "2026-07-28T19:10:03.408027Z",
     "shell.execute_reply": "2026-07-28T19:10:03.406749Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = (data - data.mean()).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176dacd0-70c0-456a-969f-3c4ae980948f",
   "metadata": {},
   "source": [
    "### Model Selection\n",
    "\n",
    "`ardl_select_order` can be used to automatically select the order. Here we use min the minimum AIC among all modes that consider up to 3 lags of the endogenous variable and 3 lags of each exogenous variable. `trend=\"c\"` indicates that a constant should be included in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2da134f2-5cf5-484c-833e-b70ddef76d4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:03.410375Z",
     "iopub.status.busy": "2026-07-28T19:10:03.410126Z",
     "iopub.status.idle": "2026-07-28T19:10:03.539615Z",
     "shell.execute_reply": "2026-07-28T19:10:03.538963Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal order is: (3, 1, 3, 2)\n"
     ]
    }
   ],
   "source": [
    "sel_res = ardl_select_order(\n",
    "    data.lrm, 3, data[[\"lry\", \"ibo\", \"ide\"]], 3, ic=\"aic\", trend=\"c\"\n",
    ")\n",
    "print(f\"The optimal order is: {sel_res.model.ardl_order}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ecf7091-5de4-4bbb-8952-326ca32b99b6",
   "metadata": {},
   "source": [
    "The optimal order is returned as the number of lags of the endogenous variable followed by each of the exogenous regressors.  The attribute `model` on `sel_res` contains the model `ARDL` specification which can be used to call `fit`. Here we look at the summary where the `L#` indicates that lag length (e.g., `L0` is no lag, i.e., $X_{k,t}$, `L2` is 2 lags, i.e., $X_{k,t-2}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5416d92-6b08-4b09-ac70-bccb4711dd7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:03.544312Z",
     "iopub.status.busy": "2026-07-28T19:10:03.544087Z",
     "iopub.status.idle": "2026-07-28T19:10:03.590573Z",
     "shell.execute_reply": "2026-07-28T19:10:03.589161Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARDL Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>lrm</td>       <th>  No. Observations:  </th>    <td>55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>ARDL(3, 1, 3, 2)</td> <th>  Log Likelihood     </th>  <td>139.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.017</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-251.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:03</td>     <th>  BIC                </th> <td>-223.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>10-01-1974</td>    <th>  HQIC               </th> <td>-240.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 07-01-1987</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    2.6202</td> <td>    0.568</td> <td>    4.615</td> <td> 0.000</td> <td>    1.472</td> <td>    3.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th> <td>    0.3192</td> <td>    0.137</td> <td>    2.336</td> <td> 0.025</td> <td>    0.043</td> <td>    0.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L2</th> <td>    0.5326</td> <td>    0.132</td> <td>    4.024</td> <td> 0.000</td> <td>    0.265</td> <td>    0.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L3</th> <td>   -0.2687</td> <td>    0.102</td> <td>   -2.631</td> <td> 0.012</td> <td>   -0.475</td> <td>   -0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L0</th> <td>    0.6728</td> <td>    0.131</td> <td>    5.129</td> <td> 0.000</td> <td>    0.407</td> <td>    0.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th> <td>   -0.2574</td> <td>    0.147</td> <td>   -1.749</td> <td> 0.088</td> <td>   -0.555</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L0</th> <td>   -1.0785</td> <td>    0.322</td> <td>   -3.353</td> <td> 0.002</td> <td>   -1.729</td> <td>   -0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th> <td>   -0.1062</td> <td>    0.586</td> <td>   -0.181</td> <td> 0.857</td> <td>   -1.291</td> <td>    1.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L2</th> <td>    0.2877</td> <td>    0.569</td> <td>    0.505</td> <td> 0.616</td> <td>   -0.863</td> <td>    1.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L3</th> <td>   -0.9947</td> <td>    0.393</td> <td>   -2.534</td> <td> 0.015</td> <td>   -1.789</td> <td>   -0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L0</th> <td>    0.1255</td> <td>    0.554</td> <td>    0.226</td> <td> 0.822</td> <td>   -0.996</td> <td>    1.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th> <td>   -0.3280</td> <td>    0.721</td> <td>   -0.455</td> <td> 0.652</td> <td>   -1.787</td> <td>    1.131</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L2</th> <td>    1.4079</td> <td>    0.552</td> <td>    2.550</td> <td> 0.015</td> <td>    0.291</td> <td>    2.524</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &       lrm        & \\textbf{  No. Observations:  } &     55      \\\\\n",
       "\\textbf{Model:}         & ARDL(3, 1, 3, 2) & \\textbf{  Log Likelihood     } &  139.513    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.017     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } &  -251.026   \\\\\n",
       "\\textbf{Time:}          &     19:10:03     & \\textbf{  BIC                } &  -223.708   \\\\\n",
       "\\textbf{Sample:}        &    10-01-1974    & \\textbf{  HQIC               } &  -240.553   \\\\\n",
       "\\textbf{}               &   - 07-01-1987   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &       2.6202  &        0.568     &     4.615  &         0.000        &        1.472    &        3.769     \\\\\n",
       "\\textbf{lrm.L1} &       0.3192  &        0.137     &     2.336  &         0.025        &        0.043    &        0.596     \\\\\n",
       "\\textbf{lrm.L2} &       0.5326  &        0.132     &     4.024  &         0.000        &        0.265    &        0.800     \\\\\n",
       "\\textbf{lrm.L3} &      -0.2687  &        0.102     &    -2.631  &         0.012        &       -0.475    &       -0.062     \\\\\n",
       "\\textbf{lry.L0} &       0.6728  &        0.131     &     5.129  &         0.000        &        0.407    &        0.938     \\\\\n",
       "\\textbf{lry.L1} &      -0.2574  &        0.147     &    -1.749  &         0.088        &       -0.555    &        0.040     \\\\\n",
       "\\textbf{ibo.L0} &      -1.0785  &        0.322     &    -3.353  &         0.002        &       -1.729    &       -0.428     \\\\\n",
       "\\textbf{ibo.L1} &      -0.1062  &        0.586     &    -0.181  &         0.857        &       -1.291    &        1.079     \\\\\n",
       "\\textbf{ibo.L2} &       0.2877  &        0.569     &     0.505  &         0.616        &       -0.863    &        1.439     \\\\\n",
       "\\textbf{ibo.L3} &      -0.9947  &        0.393     &    -2.534  &         0.015        &       -1.789    &       -0.201     \\\\\n",
       "\\textbf{ide.L0} &       0.1255  &        0.554     &     0.226  &         0.822        &       -0.996    &        1.247     \\\\\n",
       "\\textbf{ide.L1} &      -0.3280  &        0.721     &    -0.455  &         0.652        &       -1.787    &        1.131     \\\\\n",
       "\\textbf{ide.L2} &       1.4079  &        0.552     &     2.550  &         0.015        &        0.291    &        2.524     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{ARDL Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARDL Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    lrm   No. Observations:                   55\n",
       "Model:               ARDL(3, 1, 3, 2)   Log Likelihood                 139.513\n",
       "Method:               Conditional MLE   S.D. of innovations              0.017\n",
       "Date:                Tue, 28 Jul 2026   AIC                           -251.026\n",
       "Time:                        19:10:03   BIC                           -223.708\n",
       "Sample:                    10-01-1974   HQIC                          -240.553\n",
       "                         - 07-01-1987                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.6202      0.568      4.615      0.000       1.472       3.769\n",
       "lrm.L1         0.3192      0.137      2.336      0.025       0.043       0.596\n",
       "lrm.L2         0.5326      0.132      4.024      0.000       0.265       0.800\n",
       "lrm.L3        -0.2687      0.102     -2.631      0.012      -0.475      -0.062\n",
       "lry.L0         0.6728      0.131      5.129      0.000       0.407       0.938\n",
       "lry.L1        -0.2574      0.147     -1.749      0.088      -0.555       0.040\n",
       "ibo.L0        -1.0785      0.322     -3.353      0.002      -1.729      -0.428\n",
       "ibo.L1        -0.1062      0.586     -0.181      0.857      -1.291       1.079\n",
       "ibo.L2         0.2877      0.569      0.505      0.616      -0.863       1.439\n",
       "ibo.L3        -0.9947      0.393     -2.534      0.015      -1.789      -0.201\n",
       "ide.L0         0.1255      0.554      0.226      0.822      -0.996       1.247\n",
       "ide.L1        -0.3280      0.721     -0.455      0.652      -1.787       1.131\n",
       "ide.L2         1.4079      0.552      2.550      0.015       0.291       2.524\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = sel_res.model.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f52544-1804-4266-80ea-10556b315003",
   "metadata": {},
   "source": [
    "### Global searches\n",
    "\n",
    "The selection criteria can be switched the BIC which chooses a smaller model. Here we also use the `glob=True` option to perform a global search which considers models with any subset of lags up to the maximum lag allowed (3 here).  This option lets the model selection choose non-contiguous lag specifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb24d882-facd-4e61-ad72-0a2ac5e362c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:03.593300Z",
     "iopub.status.busy": "2026-07-28T19:10:03.592555Z",
     "iopub.status.idle": "2026-07-28T19:10:07.487117Z",
     "shell.execute_reply": "2026-07-28T19:10:07.486342Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 0, 3, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_res = ardl_select_order(\n",
    "    data.lrm, 3, data[[\"lry\", \"ibo\", \"ide\"]], 3, ic=\"bic\", trend=\"c\", glob=True\n",
    ")\n",
    "sel_res.model.ardl_order"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c7de3e-8370-4e9d-adbd-fa9ffcddaa8c",
   "metadata": {},
   "source": [
    "While the `ardl_order` shows the largest included lag of each variable, `ar_lags` and `dl_lags` show the specific lags included.  The AR component is regular in the sense that all 3 lags are included.  The DL component is not since `ibo` selects only lags 0 and 3 and ide selects only lags 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51c98acb-9409-4305-85a0-c62bda98807b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.496935Z",
     "iopub.status.busy": "2026-07-28T19:10:07.496653Z",
     "iopub.status.idle": "2026-07-28T19:10:07.504239Z",
     "shell.execute_reply": "2026-07-28T19:10:07.503349Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_res.model.ar_lags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "546ec94c-6327-4d98-ad8e-a3d62961394b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.507404Z",
     "iopub.status.busy": "2026-07-28T19:10:07.507183Z",
     "iopub.status.idle": "2026-07-28T19:10:07.514051Z",
     "shell.execute_reply": "2026-07-28T19:10:07.513248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lry': [0], 'ibo': [0, 3], 'ide': [2]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_res.model.dl_lags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5b5fcf-f2d0-41bb-8f08-5d3daff450ca",
   "metadata": {},
   "source": [
    "We can take a look at the best performing models according to the BIC which are stored in the `bic` property. `ibo` at lags 0 and 3 is consistently selected, as is `ide` at either lag 2 or 3, and `lry` at lag 0. The selected AR lags vary more, although all of the best specifications select some. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b3b6f0bf-8408-466b-99c9-0bce16213fa9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.515962Z",
     "iopub.status.busy": "2026-07-28T19:10:07.515738Z",
     "iopub.status.idle": "2026-07-28T19:10:07.523287Z",
     "shell.execute_reply": "2026-07-28T19:10:07.522538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: ((1, 2, 3), {'lry': (0,), 'ibo': (0, 3), 'ide': (2,)})\n",
      "2: ((1, 2, 3), {'lry': (0, 1), 'ibo': (0, 3), 'ide': (2,)})\n",
      "3: ((2,), {'lry': (0,), 'ibo': (0, 3), 'ide': (3,)})\n",
      "4: ((1, 2, 3), {'lry': (0, 2), 'ibo': (0, 3), 'ide': (2,)})\n",
      "5: ((2,), {'lry': (0, 2), 'ibo': (0, 3), 'ide': (2,)})\n",
      "6: ((2,), {'lry': (0, 3), 'ibo': (0, 3), 'ide': (2,)})\n",
      "7: ((2, 3), {'lry': (0,), 'ibo': (0, 3), 'ide': (2,)})\n",
      "8: ((1, 2, 3), {'lry': (0,), 'ibo': (0, 3), 'ide': (2, 3)})\n",
      "9: ((2,), {'lry': (0,), 'ibo': (0, 3), 'ide': (2, 3)})\n",
      "10: ((1, 2), {'lry': (0,), 'ibo': (0, 3), 'ide': (3,)})\n"
     ]
    }
   ],
   "source": [
    "for i, val in enumerate(sel_res.bic.head(10)):\n",
    "    print(f\"{i+1}: {val}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b4151f-4308-4b97-a7b0-1a4a4e231d9f",
   "metadata": {},
   "source": [
    "### Direct Parameterization\n",
    "\n",
    "ARDL models can be directly specified using the `ARDL` class.  The first argument is the endogenous variable ($Y_t$). The second is the AR lags. It can be a constant, in which case lags 1, 2, ..., $P$ are included, or a list of specific lags indices to include (e.g., `[1, 4]`).  The third are the exogenous variables, and the fourth is the list of lags to include.  This can be one of\n",
    "\n",
    "* An `int`: Include lags 0, 1, ..., Q\n",
    "* A dict with column names when `exog` is a `DataFrame` or numeric column locations when `exog` is a NumPy array (e.g., `{0:1, 1: 2, 2:3}`, would match the specification below if a NumPy array was used.\n",
    "* A dict with column names (DataFrames) or integers (NumPy arrays) that contains a list of specific lags to include (e.g., `{\"lry\":[0,2], \"ibo\":[1,2]}`).\n",
    "\n",
    "The specification below matches that model selected by `ardl_select_order`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "edc6cca1-acda-4ff0-886a-fcb69866e077",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.525573Z",
     "iopub.status.busy": "2026-07-28T19:10:07.525354Z",
     "iopub.status.idle": "2026-07-28T19:10:07.588169Z",
     "shell.execute_reply": "2026-07-28T19:10:07.587352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARDL Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>lrm</td>       <th>  No. Observations:  </th>    <td>55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>ARDL(2, 1, 2, 3)</td> <th>  Log Likelihood     </th>  <td>136.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.019</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-246.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:07</td>     <th>  BIC                </th> <td>-220.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>10-01-1974</td>    <th>  HQIC               </th> <td>-236.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 07-01-1987</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    2.4440</td> <td>    0.546</td> <td>    4.479</td> <td> 0.000</td> <td>    1.342</td> <td>    3.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th> <td>    0.2695</td> <td>    0.139</td> <td>    1.944</td> <td> 0.059</td> <td>   -0.011</td> <td>    0.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L2</th> <td>    0.3409</td> <td>    0.114</td> <td>    2.993</td> <td> 0.005</td> <td>    0.111</td> <td>    0.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L0</th> <td>    0.6344</td> <td>    0.145</td> <td>    4.368</td> <td> 0.000</td> <td>    0.341</td> <td>    0.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th> <td>   -0.2426</td> <td>    0.159</td> <td>   -1.527</td> <td> 0.134</td> <td>   -0.563</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L0</th> <td>   -1.1316</td> <td>    0.359</td> <td>   -3.157</td> <td> 0.003</td> <td>   -1.856</td> <td>   -0.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th> <td>    0.1056</td> <td>    0.640</td> <td>    0.165</td> <td> 0.870</td> <td>   -1.186</td> <td>    1.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L2</th> <td>   -0.8347</td> <td>    0.497</td> <td>   -1.679</td> <td> 0.101</td> <td>   -1.839</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L0</th> <td>    0.2849</td> <td>    0.614</td> <td>    0.464</td> <td> 0.645</td> <td>   -0.954</td> <td>    1.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th> <td>    0.0433</td> <td>    0.805</td> <td>    0.054</td> <td> 0.957</td> <td>   -1.582</td> <td>    1.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L2</th> <td>    0.4429</td> <td>    0.770</td> <td>    0.575</td> <td> 0.568</td> <td>   -1.112</td> <td>    1.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L3</th> <td>    0.3671</td> <td>    0.515</td> <td>    0.713</td> <td> 0.480</td> <td>   -0.673</td> <td>    1.408</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &       lrm        & \\textbf{  No. Observations:  } &     55      \\\\\n",
       "\\textbf{Model:}         & ARDL(2, 1, 2, 3) & \\textbf{  Log Likelihood     } &  136.252    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.019     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } &  -246.504   \\\\\n",
       "\\textbf{Time:}          &     19:10:07     & \\textbf{  BIC                } &  -220.890   \\\\\n",
       "\\textbf{Sample:}        &    10-01-1974    & \\textbf{  HQIC               } &  -236.654   \\\\\n",
       "\\textbf{}               &   - 07-01-1987   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &       2.4440  &        0.546     &     4.479  &         0.000        &        1.342    &        3.546     \\\\\n",
       "\\textbf{lrm.L1} &       0.2695  &        0.139     &     1.944  &         0.059        &       -0.011    &        0.550     \\\\\n",
       "\\textbf{lrm.L2} &       0.3409  &        0.114     &     2.993  &         0.005        &        0.111    &        0.571     \\\\\n",
       "\\textbf{lry.L0} &       0.6344  &        0.145     &     4.368  &         0.000        &        0.341    &        0.928     \\\\\n",
       "\\textbf{lry.L1} &      -0.2426  &        0.159     &    -1.527  &         0.134        &       -0.563    &        0.078     \\\\\n",
       "\\textbf{ibo.L0} &      -1.1316  &        0.359     &    -3.157  &         0.003        &       -1.856    &       -0.408     \\\\\n",
       "\\textbf{ibo.L1} &       0.1056  &        0.640     &     0.165  &         0.870        &       -1.186    &        1.397     \\\\\n",
       "\\textbf{ibo.L2} &      -0.8347  &        0.497     &    -1.679  &         0.101        &       -1.839    &        0.170     \\\\\n",
       "\\textbf{ide.L0} &       0.2849  &        0.614     &     0.464  &         0.645        &       -0.954    &        1.524     \\\\\n",
       "\\textbf{ide.L1} &       0.0433  &        0.805     &     0.054  &         0.957        &       -1.582    &        1.669     \\\\\n",
       "\\textbf{ide.L2} &       0.4429  &        0.770     &     0.575  &         0.568        &       -1.112    &        1.998     \\\\\n",
       "\\textbf{ide.L3} &       0.3671  &        0.515     &     0.713  &         0.480        &       -0.673    &        1.408     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{ARDL Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARDL Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    lrm   No. Observations:                   55\n",
       "Model:               ARDL(2, 1, 2, 3)   Log Likelihood                 136.252\n",
       "Method:               Conditional MLE   S.D. of innovations              0.019\n",
       "Date:                Tue, 28 Jul 2026   AIC                           -246.504\n",
       "Time:                        19:10:07   BIC                           -220.890\n",
       "Sample:                    10-01-1974   HQIC                          -236.654\n",
       "                         - 07-01-1987                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.4440      0.546      4.479      0.000       1.342       3.546\n",
       "lrm.L1         0.2695      0.139      1.944      0.059      -0.011       0.550\n",
       "lrm.L2         0.3409      0.114      2.993      0.005       0.111       0.571\n",
       "lry.L0         0.6344      0.145      4.368      0.000       0.341       0.928\n",
       "lry.L1        -0.2426      0.159     -1.527      0.134      -0.563       0.078\n",
       "ibo.L0        -1.1316      0.359     -3.157      0.003      -1.856      -0.408\n",
       "ibo.L1         0.1056      0.640      0.165      0.870      -1.186       1.397\n",
       "ibo.L2        -0.8347      0.497     -1.679      0.101      -1.839       0.170\n",
       "ide.L0         0.2849      0.614      0.464      0.645      -0.954       1.524\n",
       "ide.L1         0.0433      0.805      0.054      0.957      -1.582       1.669\n",
       "ide.L2         0.4429      0.770      0.575      0.568      -1.112       1.998\n",
       "ide.L3         0.3671      0.515      0.713      0.480      -0.673       1.408\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = ARDL(\n",
    "    data.lrm, 2, data[[\"lry\", \"ibo\", \"ide\"]], {\"lry\": 1, \"ibo\": 2, \"ide\": 3}, trend=\"c\"\n",
    ").fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c879d6b-da6e-4fd7-b6fd-28d1f334d397",
   "metadata": {},
   "source": [
    "### NumPy Data\n",
    "\n",
    "Below we see how the specification of ARDL models differs when using NumPy arrays.  The key difference is that the keys in the dictionary are now integers which indicate the column of `x` to use. This model is identical to the previously fit model and all key value match exactly (e.g., Log Likelihood)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fcbead5-eef8-431a-83cc-6333dc1257e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.591369Z",
     "iopub.status.busy": "2026-07-28T19:10:07.590921Z",
     "iopub.status.idle": "2026-07-28T19:10:07.649328Z",
     "shell.execute_reply": "2026-07-28T19:10:07.648645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARDL Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>    <td>55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>ARDL(2, 1, 2, 3)</td> <th>  Log Likelihood     </th>  <td>136.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.019</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-246.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:07</td>     <th>  BIC                </th> <td>-220.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>3</td>        <th>  HQIC               </th> <td>-236.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td>55</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    2.4440</td> <td>    0.546</td> <td>    4.479</td> <td> 0.000</td> <td>    1.342</td> <td>    3.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>y.L1</th>  <td>    0.2695</td> <td>    0.139</td> <td>    1.944</td> <td> 0.059</td> <td>   -0.011</td> <td>    0.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>y.L2</th>  <td>    0.3409</td> <td>    0.114</td> <td>    2.993</td> <td> 0.005</td> <td>    0.111</td> <td>    0.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0.L0</th> <td>    0.6344</td> <td>    0.145</td> <td>    4.368</td> <td> 0.000</td> <td>    0.341</td> <td>    0.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x0.L1</th> <td>   -0.2426</td> <td>    0.159</td> <td>   -1.527</td> <td> 0.134</td> <td>   -0.563</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1.L0</th> <td>   -1.1316</td> <td>    0.359</td> <td>   -3.157</td> <td> 0.003</td> <td>   -1.856</td> <td>   -0.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1.L1</th> <td>    0.1056</td> <td>    0.640</td> <td>    0.165</td> <td> 0.870</td> <td>   -1.186</td> <td>    1.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1.L2</th> <td>   -0.8347</td> <td>    0.497</td> <td>   -1.679</td> <td> 0.101</td> <td>   -1.839</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2.L0</th> <td>    0.2849</td> <td>    0.614</td> <td>    0.464</td> <td> 0.645</td> <td>   -0.954</td> <td>    1.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2.L1</th> <td>    0.0433</td> <td>    0.805</td> <td>    0.054</td> <td> 0.957</td> <td>   -1.582</td> <td>    1.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2.L2</th> <td>    0.4429</td> <td>    0.770</td> <td>    0.575</td> <td> 0.568</td> <td>   -1.112</td> <td>    1.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2.L3</th> <td>    0.3671</td> <td>    0.515</td> <td>    0.713</td> <td> 0.480</td> <td>   -0.673</td> <td>    1.408</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &        y         & \\textbf{  No. Observations:  } &     55      \\\\\n",
       "\\textbf{Model:}         & ARDL(2, 1, 2, 3) & \\textbf{  Log Likelihood     } &  136.252    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.019     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } &  -246.504   \\\\\n",
       "\\textbf{Time:}          &     19:10:07     & \\textbf{  BIC                } &  -220.890   \\\\\n",
       "\\textbf{Sample:}        &        3         & \\textbf{  HQIC               } &  -236.654   \\\\\n",
       "\\textbf{}               &        55        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       2.4440  &        0.546     &     4.479  &         0.000        &        1.342    &        3.546     \\\\\n",
       "\\textbf{y.L1}  &       0.2695  &        0.139     &     1.944  &         0.059        &       -0.011    &        0.550     \\\\\n",
       "\\textbf{y.L2}  &       0.3409  &        0.114     &     2.993  &         0.005        &        0.111    &        0.571     \\\\\n",
       "\\textbf{x0.L0} &       0.6344  &        0.145     &     4.368  &         0.000        &        0.341    &        0.928     \\\\\n",
       "\\textbf{x0.L1} &      -0.2426  &        0.159     &    -1.527  &         0.134        &       -0.563    &        0.078     \\\\\n",
       "\\textbf{x1.L0} &      -1.1316  &        0.359     &    -3.157  &         0.003        &       -1.856    &       -0.408     \\\\\n",
       "\\textbf{x1.L1} &       0.1056  &        0.640     &     0.165  &         0.870        &       -1.186    &        1.397     \\\\\n",
       "\\textbf{x1.L2} &      -0.8347  &        0.497     &    -1.679  &         0.101        &       -1.839    &        0.170     \\\\\n",
       "\\textbf{x2.L0} &       0.2849  &        0.614     &     0.464  &         0.645        &       -0.954    &        1.524     \\\\\n",
       "\\textbf{x2.L1} &       0.0433  &        0.805     &     0.054  &         0.957        &       -1.582    &        1.669     \\\\\n",
       "\\textbf{x2.L2} &       0.4429  &        0.770     &     0.575  &         0.568        &       -1.112    &        1.998     \\\\\n",
       "\\textbf{x2.L3} &       0.3671  &        0.515     &     0.713  &         0.480        &       -0.673    &        1.408     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{ARDL Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARDL Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   55\n",
       "Model:               ARDL(2, 1, 2, 3)   Log Likelihood                 136.252\n",
       "Method:               Conditional MLE   S.D. of innovations              0.019\n",
       "Date:                Tue, 28 Jul 2026   AIC                           -246.504\n",
       "Time:                        19:10:07   BIC                           -220.890\n",
       "Sample:                             3   HQIC                          -236.654\n",
       "                                   55                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.4440      0.546      4.479      0.000       1.342       3.546\n",
       "y.L1           0.2695      0.139      1.944      0.059      -0.011       0.550\n",
       "y.L2           0.3409      0.114      2.993      0.005       0.111       0.571\n",
       "x0.L0          0.6344      0.145      4.368      0.000       0.341       0.928\n",
       "x0.L1         -0.2426      0.159     -1.527      0.134      -0.563       0.078\n",
       "x1.L0         -1.1316      0.359     -3.157      0.003      -1.856      -0.408\n",
       "x1.L1          0.1056      0.640      0.165      0.870      -1.186       1.397\n",
       "x1.L2         -0.8347      0.497     -1.679      0.101      -1.839       0.170\n",
       "x2.L0          0.2849      0.614      0.464      0.645      -0.954       1.524\n",
       "x2.L1          0.0433      0.805      0.054      0.957      -1.582       1.669\n",
       "x2.L2          0.4429      0.770      0.575      0.568      -1.112       1.998\n",
       "x2.L3          0.3671      0.515      0.713      0.480      -0.673       1.408\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.asarray(data.lrm)\n",
    "x = np.asarray(data[[\"lry\", \"ibo\", \"ide\"]])\n",
    "res = ARDL(y, 2, x, {0: 1, 1: 2, 2: 3}, trend=\"c\").fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c00153-e811-4482-bfdc-b319a440e27b",
   "metadata": {},
   "source": [
    "### Causal models\n",
    "\n",
    "Using the `causal=True` flag eliminates lag 0 from the DL components, so that all variables included in the model are known at time $t-1$ when modeling $Y_t$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e29bae15-3ad8-4614-a0e7-5e4ed9022d96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.651774Z",
     "iopub.status.busy": "2026-07-28T19:10:07.651535Z",
     "iopub.status.idle": "2026-07-28T19:10:07.701097Z",
     "shell.execute_reply": "2026-07-28T19:10:07.698730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARDL Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>lrm</td>       <th>  No. Observations:  </th>    <td>55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>ARDL(2, 1, 2, 3)</td> <th>  Log Likelihood     </th>  <td>121.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.025</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-222.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:07</td>     <th>  BIC                </th> <td>-202.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>10-01-1974</td>    <th>  HQIC               </th> <td>-214.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 07-01-1987</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    2.3885</td> <td>    0.696</td> <td>    3.434</td> <td> 0.001</td> <td>    0.987</td> <td>    3.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th> <td>    0.4294</td> <td>    0.169</td> <td>    2.538</td> <td> 0.015</td> <td>    0.088</td> <td>    0.770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L2</th> <td>    0.2777</td> <td>    0.139</td> <td>    2.003</td> <td> 0.051</td> <td>   -0.002</td> <td>    0.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th> <td>    0.2064</td> <td>    0.139</td> <td>    1.481</td> <td> 0.146</td> <td>   -0.074</td> <td>    0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th> <td>   -1.2787</td> <td>    0.467</td> <td>   -2.736</td> <td> 0.009</td> <td>   -2.221</td> <td>   -0.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L2</th> <td>   -0.3403</td> <td>    0.568</td> <td>   -0.600</td> <td> 0.552</td> <td>   -1.484</td> <td>    0.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th> <td>   -0.1234</td> <td>    0.746</td> <td>   -0.165</td> <td> 0.869</td> <td>   -1.627</td> <td>    1.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L2</th> <td>    0.5001</td> <td>    0.978</td> <td>    0.511</td> <td> 0.612</td> <td>   -1.471</td> <td>    2.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L3</th> <td>    0.6106</td> <td>    0.656</td> <td>    0.931</td> <td> 0.357</td> <td>   -0.711</td> <td>    1.933</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &       lrm        & \\textbf{  No. Observations:  } &     55      \\\\\n",
       "\\textbf{Model:}         & ARDL(2, 1, 2, 3) & \\textbf{  Log Likelihood     } &  121.130    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.025     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } &  -222.260   \\\\\n",
       "\\textbf{Time:}          &     19:10:07     & \\textbf{  BIC                } &  -202.557   \\\\\n",
       "\\textbf{Sample:}        &    10-01-1974    & \\textbf{  HQIC               } &  -214.683   \\\\\n",
       "\\textbf{}               &   - 07-01-1987   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &       2.3885  &        0.696     &     3.434  &         0.001        &        0.987    &        3.790     \\\\\n",
       "\\textbf{lrm.L1} &       0.4294  &        0.169     &     2.538  &         0.015        &        0.088    &        0.770     \\\\\n",
       "\\textbf{lrm.L2} &       0.2777  &        0.139     &     2.003  &         0.051        &       -0.002    &        0.557     \\\\\n",
       "\\textbf{lry.L1} &       0.2064  &        0.139     &     1.481  &         0.146        &       -0.074    &        0.487     \\\\\n",
       "\\textbf{ibo.L1} &      -1.2787  &        0.467     &    -2.736  &         0.009        &       -2.221    &       -0.337     \\\\\n",
       "\\textbf{ibo.L2} &      -0.3403  &        0.568     &    -0.600  &         0.552        &       -1.484    &        0.804     \\\\\n",
       "\\textbf{ide.L1} &      -0.1234  &        0.746     &    -0.165  &         0.869        &       -1.627    &        1.380     \\\\\n",
       "\\textbf{ide.L2} &       0.5001  &        0.978     &     0.511  &         0.612        &       -1.471    &        2.472     \\\\\n",
       "\\textbf{ide.L3} &       0.6106  &        0.656     &     0.931  &         0.357        &       -0.711    &        1.933     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{ARDL Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARDL Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    lrm   No. Observations:                   55\n",
       "Model:               ARDL(2, 1, 2, 3)   Log Likelihood                 121.130\n",
       "Method:               Conditional MLE   S.D. of innovations              0.025\n",
       "Date:                Tue, 28 Jul 2026   AIC                           -222.260\n",
       "Time:                        19:10:07   BIC                           -202.557\n",
       "Sample:                    10-01-1974   HQIC                          -214.683\n",
       "                         - 07-01-1987                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.3885      0.696      3.434      0.001       0.987       3.790\n",
       "lrm.L1         0.4294      0.169      2.538      0.015       0.088       0.770\n",
       "lrm.L2         0.2777      0.139      2.003      0.051      -0.002       0.557\n",
       "lry.L1         0.2064      0.139      1.481      0.146      -0.074       0.487\n",
       "ibo.L1        -1.2787      0.467     -2.736      0.009      -2.221      -0.337\n",
       "ibo.L2        -0.3403      0.568     -0.600      0.552      -1.484       0.804\n",
       "ide.L1        -0.1234      0.746     -0.165      0.869      -1.627       1.380\n",
       "ide.L2         0.5001      0.978      0.511      0.612      -1.471       2.472\n",
       "ide.L3         0.6106      0.656      0.931      0.357      -0.711       1.933\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = ARDL(\n",
    "    data.lrm,\n",
    "    2,\n",
    "    data[[\"lry\", \"ibo\", \"ide\"]],\n",
    "    {\"lry\": 1, \"ibo\": 2, \"ide\": 3},\n",
    "    trend=\"c\",\n",
    "    causal=True,\n",
    ").fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac0a3be-996b-4ade-8d96-13629e661600",
   "metadata": {},
   "source": [
    "## Unconstrained Error Correction Models (UECM)\n",
    "\n",
    "Unconstrained Error Correction Models reparameterize ARDL model to focus on the long-run component of a time series.  The reparameterized model is\n",
    "\n",
    "$$\n",
    "\\Delta Y_t = \\underset{\\text{Constant and Trend}}{\\underbrace{\\delta_0 + \\delta_1 t + \\ldots + \\delta_k t^k}} \n",
    "      + \\underset{\\text{Seasonal}}{\\underbrace{\\sum_{i=0}^{s-1} \\gamma_i S_i}}\n",
    "      + \\underset{\\text{Long-Run}}{\\underbrace{\\lambda_0 Y_{t-1} + \\sum_{b=1}^M \\lambda_i  X_{b,t-1}}}\n",
    "      + \\underset{\\text{Autoregressive}}{\\underbrace{\\sum_{p=1}^P \\phi_p \\Delta Y_{t-p}}}\n",
    "      + \\underset{\\text{Distributed Lag}}{\\underbrace{\\sum_{k=1}^M \\sum_{j=0}^{Q_k} \\beta_{k,j} \\Delta X_{k, t-j}}}\n",
    "      + \\underset{\\text{Fixed}}{\\underbrace{Z_t \\Gamma}} + \\epsilon_t\n",
    "$$\n",
    "\n",
    "\n",
    "Most of the components are the same.  The key differences are:\n",
    "\n",
    "* The levels only enter at lag 1\n",
    "* All other lags of $Y_t$ or $X_{k,t}$ are differenced\n",
    "\n",
    "Due to their structure, UECM models _do not_ support irregular lag specifications, and so lags specifications must be integers. The AR lag length must be an integer or `None`, while the DL lag specification can be an integer or a dictionary of integers.  Other options such as `trend`, `seasonal`, and `causal` are identical.\n",
    "\n",
    "Below we select a model and then using the class method `from_ardl` to construct the UECM.  The parameter estimates prefixed with `D.` are differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9645823c-8d25-487e-a34a-5350f0c11912",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:07.705225Z",
     "iopub.status.busy": "2026-07-28T19:10:07.705022Z",
     "iopub.status.idle": "2026-07-28T19:10:07.998269Z",
     "shell.execute_reply": "2026-07-28T19:10:07.997557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>UECM Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>D.lrm</td>      <th>  No. Observations:  </th>    <td>55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>UECM(3, 1, 3, 2)</td> <th>  Log Likelihood     </th>  <td>139.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.017</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-251.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:07</td>     <th>  BIC                </th> <td>-223.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>10-01-1974</td>    <th>  HQIC               </th> <td>-240.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 07-01-1987</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>    2.6202</td> <td>    0.568</td> <td>    4.615</td> <td> 0.000</td> <td>    1.472</td> <td>    3.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th>   <td>   -0.4169</td> <td>    0.092</td> <td>   -4.548</td> <td> 0.000</td> <td>   -0.602</td> <td>   -0.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th>   <td>    0.4154</td> <td>    0.118</td> <td>    3.532</td> <td> 0.001</td> <td>    0.177</td> <td>    0.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th>   <td>   -1.8917</td> <td>    0.391</td> <td>   -4.837</td> <td> 0.000</td> <td>   -2.683</td> <td>   -1.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th>   <td>    1.2053</td> <td>    0.447</td> <td>    2.697</td> <td> 0.010</td> <td>    0.301</td> <td>    2.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.lrm.L1</th> <td>   -0.2639</td> <td>    0.102</td> <td>   -2.590</td> <td> 0.013</td> <td>   -0.470</td> <td>   -0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.lrm.L2</th> <td>    0.2687</td> <td>    0.102</td> <td>    2.631</td> <td> 0.012</td> <td>    0.062</td> <td>    0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.lry.L0</th> <td>    0.6728</td> <td>    0.131</td> <td>    5.129</td> <td> 0.000</td> <td>    0.407</td> <td>    0.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ibo.L0</th> <td>   -1.0785</td> <td>    0.322</td> <td>   -3.353</td> <td> 0.002</td> <td>   -1.729</td> <td>   -0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ibo.L1</th> <td>    0.7070</td> <td>    0.469</td> <td>    1.508</td> <td> 0.140</td> <td>   -0.241</td> <td>    1.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ibo.L2</th> <td>    0.9947</td> <td>    0.393</td> <td>    2.534</td> <td> 0.015</td> <td>    0.201</td> <td>    1.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ide.L0</th> <td>    0.1255</td> <td>    0.554</td> <td>    0.226</td> <td> 0.822</td> <td>   -0.996</td> <td>    1.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ide.L1</th> <td>   -1.4079</td> <td>    0.552</td> <td>   -2.550</td> <td> 0.015</td> <td>   -2.524</td> <td>   -0.291</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &      D.lrm       & \\textbf{  No. Observations:  } &     55      \\\\\n",
       "\\textbf{Model:}         & UECM(3, 1, 3, 2) & \\textbf{  Log Likelihood     } &  139.513    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.017     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } &  -251.026   \\\\\n",
       "\\textbf{Time:}          &     19:10:07     & \\textbf{  BIC                } &  -223.708   \\\\\n",
       "\\textbf{Sample:}        &    10-01-1974    & \\textbf{  HQIC               } &  -240.553   \\\\\n",
       "\\textbf{}               &   - 07-01-1987   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}    &       2.6202  &        0.568     &     4.615  &         0.000        &        1.472    &        3.769     \\\\\n",
       "\\textbf{lrm.L1}   &      -0.4169  &        0.092     &    -4.548  &         0.000        &       -0.602    &       -0.231     \\\\\n",
       "\\textbf{lry.L1}   &       0.4154  &        0.118     &     3.532  &         0.001        &        0.177    &        0.653     \\\\\n",
       "\\textbf{ibo.L1}   &      -1.8917  &        0.391     &    -4.837  &         0.000        &       -2.683    &       -1.101     \\\\\n",
       "\\textbf{ide.L1}   &       1.2053  &        0.447     &     2.697  &         0.010        &        0.301    &        2.109     \\\\\n",
       "\\textbf{D.lrm.L1} &      -0.2639  &        0.102     &    -2.590  &         0.013        &       -0.470    &       -0.058     \\\\\n",
       "\\textbf{D.lrm.L2} &       0.2687  &        0.102     &     2.631  &         0.012        &        0.062    &        0.475     \\\\\n",
       "\\textbf{D.lry.L0} &       0.6728  &        0.131     &     5.129  &         0.000        &        0.407    &        0.938     \\\\\n",
       "\\textbf{D.ibo.L0} &      -1.0785  &        0.322     &    -3.353  &         0.002        &       -1.729    &       -0.428     \\\\\n",
       "\\textbf{D.ibo.L1} &       0.7070  &        0.469     &     1.508  &         0.140        &       -0.241    &        1.655     \\\\\n",
       "\\textbf{D.ibo.L2} &       0.9947  &        0.393     &     2.534  &         0.015        &        0.201    &        1.789     \\\\\n",
       "\\textbf{D.ide.L0} &       0.1255  &        0.554     &     0.226  &         0.822        &       -0.996    &        1.247     \\\\\n",
       "\\textbf{D.ide.L1} &      -1.4079  &        0.552     &    -2.550  &         0.015        &       -2.524    &       -0.291     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{UECM Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              UECM Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                  D.lrm   No. Observations:                   55\n",
       "Model:               UECM(3, 1, 3, 2)   Log Likelihood                 139.513\n",
       "Method:               Conditional MLE   S.D. of innovations              0.017\n",
       "Date:                Tue, 28 Jul 2026   AIC                           -251.026\n",
       "Time:                        19:10:07   BIC                           -223.708\n",
       "Sample:                    10-01-1974   HQIC                          -240.553\n",
       "                         - 07-01-1987                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.6202      0.568      4.615      0.000       1.472       3.769\n",
       "lrm.L1        -0.4169      0.092     -4.548      0.000      -0.602      -0.231\n",
       "lry.L1         0.4154      0.118      3.532      0.001       0.177       0.653\n",
       "ibo.L1        -1.8917      0.391     -4.837      0.000      -2.683      -1.101\n",
       "ide.L1         1.2053      0.447      2.697      0.010       0.301       2.109\n",
       "D.lrm.L1      -0.2639      0.102     -2.590      0.013      -0.470      -0.058\n",
       "D.lrm.L2       0.2687      0.102      2.631      0.012       0.062       0.475\n",
       "D.lry.L0       0.6728      0.131      5.129      0.000       0.407       0.938\n",
       "D.ibo.L0      -1.0785      0.322     -3.353      0.002      -1.729      -0.428\n",
       "D.ibo.L1       0.7070      0.469      1.508      0.140      -0.241       1.655\n",
       "D.ibo.L2       0.9947      0.393      2.534      0.015       0.201       1.789\n",
       "D.ide.L0       0.1255      0.554      0.226      0.822      -0.996       1.247\n",
       "D.ide.L1      -1.4079      0.552     -2.550      0.015      -2.524      -0.291\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.api import UECM\n",
    "\n",
    "sel_res = ardl_select_order(\n",
    "    data.lrm, 3, data[[\"lry\", \"ibo\", \"ide\"]], 3, ic=\"aic\", trend=\"c\"\n",
    ")\n",
    "\n",
    "ecm = UECM.from_ardl(sel_res.model)\n",
    "ecm_res = ecm.fit()\n",
    "ecm_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ecf6cab-9477-4492-b83a-40fe92665697",
   "metadata": {},
   "source": [
    "### Cointegrating Relationships\n",
    "\n",
    "Because the focus is on the long-run relationship, the results of UECM model fits contains a number of properties that focus on the long-run relationship. These are all prefixed `ci_`, for cointegrating.  `ci_summary` contains the normalized estimates of the cointegrating relationship and associated estimated values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fed4cc4e-100c-4b6d-bea4-6ed180bda5df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:08.000791Z",
     "iopub.status.busy": "2026-07-28T19:10:08.000563Z",
     "iopub.status.idle": "2026-07-28T19:10:08.033091Z",
     "shell.execute_reply": "2026-07-28T19:10:08.029719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Cointegrating Vector</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>   -6.2857</td> <td>    0.772</td> <td>   -8.143</td> <td> 0.000</td> <td>   -7.847</td> <td>   -4.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th> <td>    1.0000</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th> <td>   -0.9965</td> <td>    0.124</td> <td>   -8.041</td> <td> 0.000</td> <td>   -1.247</td> <td>   -0.746</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th> <td>    4.5381</td> <td>    0.520</td> <td>    8.722</td> <td> 0.000</td> <td>    3.486</td> <td>    5.591</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th> <td>   -2.8915</td> <td>    0.995</td> <td>   -2.906</td> <td> 0.004</td> <td>   -4.904</td> <td>   -0.879</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccccc}\n",
       "\\toprule\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &      -6.2857  &        0.772     &    -8.143  &         0.000        &       -7.847    &       -4.724     \\\\\n",
       "\\textbf{lrm.L1} &       1.0000  &            0     &       nan  &           nan        &        1.000    &        1.000     \\\\\n",
       "\\textbf{lry.L1} &      -0.9965  &        0.124     &    -8.041  &         0.000        &       -1.247    &       -0.746     \\\\\n",
       "\\textbf{ibo.L1} &       4.5381  &        0.520     &     8.722  &         0.000        &        3.486    &        5.591     \\\\\n",
       "\\textbf{ide.L1} &      -2.8915  &        0.995     &    -2.906  &         0.004        &       -4.904    &       -0.879     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Cointegrating Vector}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             Cointegrating Vector                             \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -6.2857      0.772     -8.143      0.000      -7.847      -4.724\n",
       "lrm.L1         1.0000          0        nan        nan       1.000       1.000\n",
       "lry.L1        -0.9965      0.124     -8.041      0.000      -1.247      -0.746\n",
       "ibo.L1         4.5381      0.520      8.722      0.000       3.486       5.591\n",
       "ide.L1        -2.8915      0.995     -2.906      0.004      -4.904      -0.879\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecm_res.ci_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d730dc12-b3c9-427e-89da-2fcabb5deea7",
   "metadata": {},
   "source": [
    "`ci_resids` contains the long-run residual, which is the error the drives figure changes in $\\Delta Y_t$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8e12dc7-f12e-442d-b0cd-d081e8f0447b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:08.035426Z",
     "iopub.status.busy": "2026-07-28T19:10:08.035224Z",
     "iopub.status.idle": "2026-07-28T19:10:08.616145Z",
     "shell.execute_reply": "2026-07-28T19:10:08.615495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = ecm_res.ci_resids.plot(title=\"Cointegrating Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4cd8e8-97d7-49d6-8c49-ebdd2b08f04f",
   "metadata": {},
   "source": [
    "### Seasonal Dummies\n",
    "\n",
    "Here we add seasonal terms, which appear to be statistically significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3618d391-0136-4d59-9ee8-33eef938f2bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:08.622228Z",
     "iopub.status.busy": "2026-07-28T19:10:08.621983Z",
     "iopub.status.idle": "2026-07-28T19:10:08.689892Z",
     "shell.execute_reply": "2026-07-28T19:10:08.688337Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>UECM Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>D.lrm</td>         <th>  No. Observations:  </th>    <td>55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>Seas. UECM(2, 2, 2, 2)</td> <th>  Log Likelihood     </th>  <td>150.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>            <td>Conditional MLE</td>    <th>  S.D. of innovations</th>   <td>0.014</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 28 Jul 2026</td>    <th>  AIC                </th> <td>-269.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:10:08</td>        <th>  BIC                </th> <td>-237.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>              <td>07-01-1974</td>       <th>  HQIC               </th> <td>-257.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                    <td>- 07-01-1987</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>    1.6501</td> <td>    0.429</td> <td>    3.848</td> <td> 0.000</td> <td>    0.782</td> <td>    2.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(2,4)</th>   <td>    0.0328</td> <td>    0.011</td> <td>    2.943</td> <td> 0.006</td> <td>    0.010</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(3,4)</th>   <td>    0.0153</td> <td>    0.008</td> <td>    1.965</td> <td> 0.057</td> <td>   -0.000</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(4,4)</th>   <td>    0.0460</td> <td>    0.009</td> <td>    5.060</td> <td> 0.000</td> <td>    0.028</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th>   <td>   -0.2543</td> <td>    0.072</td> <td>   -3.530</td> <td> 0.001</td> <td>   -0.400</td> <td>   -0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th>   <td>    0.2437</td> <td>    0.100</td> <td>    2.444</td> <td> 0.019</td> <td>    0.042</td> <td>    0.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th>   <td>   -1.2113</td> <td>    0.297</td> <td>   -4.083</td> <td> 0.000</td> <td>   -1.812</td> <td>   -0.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th>   <td>    0.6537</td> <td>    0.384</td> <td>    1.701</td> <td> 0.097</td> <td>   -0.124</td> <td>    1.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.lrm.L1</th> <td>    0.0377</td> <td>    0.156</td> <td>    0.241</td> <td> 0.811</td> <td>   -0.279</td> <td>    0.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.lry.L0</th> <td>    0.4624</td> <td>    0.121</td> <td>    3.827</td> <td> 0.000</td> <td>    0.218</td> <td>    0.707</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.lry.L1</th> <td>    0.0457</td> <td>    0.130</td> <td>    0.351</td> <td> 0.727</td> <td>   -0.218</td> <td>    0.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ibo.L0</th> <td>   -0.9562</td> <td>    0.312</td> <td>   -3.062</td> <td> 0.004</td> <td>   -1.588</td> <td>   -0.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ibo.L1</th> <td>    0.4133</td> <td>    0.360</td> <td>    1.148</td> <td> 0.258</td> <td>   -0.315</td> <td>    1.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ide.L0</th> <td>   -0.3945</td> <td>    0.505</td> <td>   -0.781</td> <td> 0.440</td> <td>   -1.417</td> <td>    0.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.ide.L1</th> <td>   -0.1430</td> <td>    0.466</td> <td>   -0.307</td> <td> 0.761</td> <td>   -1.086</td> <td>    0.800</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &         D.lrm          & \\textbf{  No. Observations:  } &     55      \\\\\n",
       "\\textbf{Model:}         & Seas. UECM(2, 2, 2, 2) & \\textbf{  Log Likelihood     } &  150.609    \\\\\n",
       "\\textbf{Method:}        &    Conditional MLE     & \\textbf{  S.D. of innovations} &   0.014     \\\\\n",
       "\\textbf{Date:}          &    Tue, 28 Jul 2026    & \\textbf{  AIC                } &  -269.218   \\\\\n",
       "\\textbf{Time:}          &        19:10:08        & \\textbf{  BIC                } &  -237.694   \\\\\n",
       "\\textbf{Sample:}        &       07-01-1974       & \\textbf{  HQIC               } &  -257.096   \\\\\n",
       "\\textbf{}               &      - 07-01-1987      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}    &       1.6501  &        0.429     &     3.848  &         0.000        &        0.782    &        2.518     \\\\\n",
       "\\textbf{s(2,4)}   &       0.0328  &        0.011     &     2.943  &         0.006        &        0.010    &        0.055     \\\\\n",
       "\\textbf{s(3,4)}   &       0.0153  &        0.008     &     1.965  &         0.057        &       -0.000    &        0.031     \\\\\n",
       "\\textbf{s(4,4)}   &       0.0460  &        0.009     &     5.060  &         0.000        &        0.028    &        0.064     \\\\\n",
       "\\textbf{lrm.L1}   &      -0.2543  &        0.072     &    -3.530  &         0.001        &       -0.400    &       -0.108     \\\\\n",
       "\\textbf{lry.L1}   &       0.2437  &        0.100     &     2.444  &         0.019        &        0.042    &        0.446     \\\\\n",
       "\\textbf{ibo.L1}   &      -1.2113  &        0.297     &    -4.083  &         0.000        &       -1.812    &       -0.611     \\\\\n",
       "\\textbf{ide.L1}   &       0.6537  &        0.384     &     1.701  &         0.097        &       -0.124    &        1.432     \\\\\n",
       "\\textbf{D.lrm.L1} &       0.0377  &        0.156     &     0.241  &         0.811        &       -0.279    &        0.354     \\\\\n",
       "\\textbf{D.lry.L0} &       0.4624  &        0.121     &     3.827  &         0.000        &        0.218    &        0.707     \\\\\n",
       "\\textbf{D.lry.L1} &       0.0457  &        0.130     &     0.351  &         0.727        &       -0.218    &        0.309     \\\\\n",
       "\\textbf{D.ibo.L0} &      -0.9562  &        0.312     &    -3.062  &         0.004        &       -1.588    &       -0.324     \\\\\n",
       "\\textbf{D.ibo.L1} &       0.4133  &        0.360     &     1.148  &         0.258        &       -0.315    &        1.142     \\\\\n",
       "\\textbf{D.ide.L0} &      -0.3945  &        0.505     &    -0.781  &         0.440        &       -1.417    &        0.628     \\\\\n",
       "\\textbf{D.ide.L1} &      -0.1430  &        0.466     &    -0.307  &         0.761        &       -1.086    &        0.800     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{UECM Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                UECM Model Results                                \n",
       "==================================================================================\n",
       "Dep. Variable:                      D.lrm   No. Observations:                   55\n",
       "Model:             Seas. UECM(2, 2, 2, 2)   Log Likelihood                 150.609\n",
       "Method:                   Conditional MLE   S.D. of innovations              0.014\n",
       "Date:                    Tue, 28 Jul 2026   AIC                           -269.218\n",
       "Time:                            19:10:08   BIC                           -237.694\n",
       "Sample:                        07-01-1974   HQIC                          -257.096\n",
       "                             - 07-01-1987                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.6501      0.429      3.848      0.000       0.782       2.518\n",
       "s(2,4)         0.0328      0.011      2.943      0.006       0.010       0.055\n",
       "s(3,4)         0.0153      0.008      1.965      0.057      -0.000       0.031\n",
       "s(4,4)         0.0460      0.009      5.060      0.000       0.028       0.064\n",
       "lrm.L1        -0.2543      0.072     -3.530      0.001      -0.400      -0.108\n",
       "lry.L1         0.2437      0.100      2.444      0.019       0.042       0.446\n",
       "ibo.L1        -1.2113      0.297     -4.083      0.000      -1.812      -0.611\n",
       "ide.L1         0.6537      0.384      1.701      0.097      -0.124       1.432\n",
       "D.lrm.L1       0.0377      0.156      0.241      0.811      -0.279       0.354\n",
       "D.lry.L0       0.4624      0.121      3.827      0.000       0.218       0.707\n",
       "D.lry.L1       0.0457      0.130      0.351      0.727      -0.218       0.309\n",
       "D.ibo.L0      -0.9562      0.312     -3.062      0.004      -1.588      -0.324\n",
       "D.ibo.L1       0.4133      0.360      1.148      0.258      -0.315       1.142\n",
       "D.ide.L0      -0.3945      0.505     -0.781      0.440      -1.417       0.628\n",
       "D.ide.L1      -0.1430      0.466     -0.307      0.761      -1.086       0.800\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecm = UECM(data.lrm, 2, data[[\"lry\", \"ibo\", \"ide\"]], 2, seasonal=True)\n",
    "seasonal_ecm_res = ecm.fit()\n",
    "seasonal_ecm_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e31719ac-78c4-4c8f-8fe9-81a77454ae82",
   "metadata": {},
   "source": [
    "All deterministic terms are included in the `ci_` prefixed terms.  Here we see the normalized seasonal effects in the summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0ac53f2-990a-4379-bf1a-e031c0482150",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:08.692367Z",
     "iopub.status.busy": "2026-07-28T19:10:08.692143Z",
     "iopub.status.idle": "2026-07-28T19:10:08.730562Z",
     "shell.execute_reply": "2026-07-28T19:10:08.729644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Cointegrating Vector</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>   -6.4899</td> <td>    1.155</td> <td>   -5.621</td> <td> 0.000</td> <td>   -8.827</td> <td>   -4.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(2,4)</th> <td>   -0.1291</td> <td>    0.066</td> <td>   -1.956</td> <td> 0.050</td> <td>   -0.263</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(3,4)</th> <td>   -0.0603</td> <td>    0.039</td> <td>   -1.545</td> <td> 0.122</td> <td>   -0.139</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(4,4)</th> <td>   -0.1810</td> <td>    0.071</td> <td>   -2.562</td> <td> 0.010</td> <td>   -0.324</td> <td>   -0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lrm.L1</th> <td>    1.0000</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lry.L1</th> <td>   -0.9585</td> <td>    0.187</td> <td>   -5.137</td> <td> 0.000</td> <td>   -1.336</td> <td>   -0.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ibo.L1</th> <td>    4.7641</td> <td>    0.826</td> <td>    5.768</td> <td> 0.000</td> <td>    3.092</td> <td>    6.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ide.L1</th> <td>   -2.5708</td> <td>    1.461</td> <td>   -1.760</td> <td> 0.078</td> <td>   -5.529</td> <td>    0.387</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccccc}\n",
       "\\toprule\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &      -6.4899  &        1.155     &    -5.621  &         0.000        &       -8.827    &       -4.152     \\\\\n",
       "\\textbf{s(2,4)} &      -0.1291  &        0.066     &    -1.956  &         0.050        &       -0.263    &        0.005     \\\\\n",
       "\\textbf{s(3,4)} &      -0.0603  &        0.039     &    -1.545  &         0.122        &       -0.139    &        0.019     \\\\\n",
       "\\textbf{s(4,4)} &      -0.1810  &        0.071     &    -2.562  &         0.010        &       -0.324    &       -0.038     \\\\\n",
       "\\textbf{lrm.L1} &       1.0000  &            0     &       nan  &           nan        &        1.000    &        1.000     \\\\\n",
       "\\textbf{lry.L1} &      -0.9585  &        0.187     &    -5.137  &         0.000        &       -1.336    &       -0.581     \\\\\n",
       "\\textbf{ibo.L1} &       4.7641  &        0.826     &     5.768  &         0.000        &        3.092    &        6.436     \\\\\n",
       "\\textbf{ide.L1} &      -2.5708  &        1.461     &    -1.760  &         0.078        &       -5.529    &        0.387     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Cointegrating Vector}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             Cointegrating Vector                             \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -6.4899      1.155     -5.621      0.000      -8.827      -4.152\n",
       "s(2,4)        -0.1291      0.066     -1.956      0.050      -0.263       0.005\n",
       "s(3,4)        -0.0603      0.039     -1.545      0.122      -0.139       0.019\n",
       "s(4,4)        -0.1810      0.071     -2.562      0.010      -0.324      -0.038\n",
       "lrm.L1         1.0000          0        nan        nan       1.000       1.000\n",
       "lry.L1        -0.9585      0.187     -5.137      0.000      -1.336      -0.581\n",
       "ibo.L1         4.7641      0.826      5.768      0.000       3.092       6.436\n",
       "ide.L1        -2.5708      1.461     -1.760      0.078      -5.529       0.387\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasonal_ecm_res.ci_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09de88a7-ba21-4126-8b63-1f8d201f1412",
   "metadata": {},
   "source": [
    "The residuals are somewhat more random in appearance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb22aa49-5c7b-4e4e-8382-4b2df05f9980",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:08.738141Z",
     "iopub.status.busy": "2026-07-28T19:10:08.737890Z",
     "iopub.status.idle": "2026-07-28T19:10:09.326641Z",
     "shell.execute_reply": "2026-07-28T19:10:09.325964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = seasonal_ecm_res.ci_resids.plot(title=\"Cointegrating Error with Seasonality\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a394c3ac-c4de-4e13-8286-c578afa22095",
   "metadata": {},
   "source": [
    "## The relationship between Consumption and Growth\n",
    "\n",
    "Here we look at an example from Greene's _Econometric analysis_ which focuses on teh long-run relationship between consumption and growth. We start by downloading the raw data.\n",
    "\n",
    "Greene, W. H. (2000). Econometric analysis 4th edition. International edition, New Jersey: Prentice Hall, 201-215."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db8d65eb-2ea5-4ced-bd72-45159dd72787",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.329823Z",
     "iopub.status.busy": "2026-07-28T19:10:09.329097Z",
     "iopub.status.idle": "2026-07-28T19:10:09.517300Z",
     "shell.execute_reply": "2026-07-28T19:10:09.511833Z"
    }
   },
   "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>Year</th>\n",
       "      <th>qtr</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinvs realgovt realdpi cpi_u M1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1610.5</td>\n",
       "      <td>1058.9</td>\n",
       "      <td>198.1&nbsp;&nbsp; 361.0&nbsp;&nbsp;1186.1&nbsp;&nbsp;70.6&nbsp;&nbsp; 110.20</td>\n",
       "      <td>1.12</td>\n",
       "      <td>6.4</td>\n",
       "      <td>149.461</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1658.8</td>\n",
       "      <td>1075.9</td>\n",
       "      <td>220.4&nbsp;&nbsp; 366.4&nbsp;&nbsp;1178.1&nbsp;&nbsp;71.4&nbsp;&nbsp; 111.75</td>\n",
       "      <td>1.17</td>\n",
       "      <td>5.6</td>\n",
       "      <td>150.260</td>\n",
       "      <td>4.5071</td>\n",
       "      <td>-3.3404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>239.7&nbsp;&nbsp; 359.6&nbsp;&nbsp;1196.5&nbsp;&nbsp;73.2&nbsp;&nbsp; 112.95</td>\n",
       "      <td>1.23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>151.064</td>\n",
       "      <td>9.9590</td>\n",
       "      <td>-8.7290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1753.9</td>\n",
       "      <td>1097.6</td>\n",
       "      <td>271.8&nbsp;&nbsp; 382.5&nbsp;&nbsp;1210.0&nbsp;&nbsp;74.9&nbsp;&nbsp; 113.93</td>\n",
       "      <td>1.35</td>\n",
       "      <td>4.2</td>\n",
       "      <td>151.871</td>\n",
       "      <td>9.1834</td>\n",
       "      <td>-7.8301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1951.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1773.5</td>\n",
       "      <td>1122.8</td>\n",
       "      <td>242.9&nbsp;&nbsp; 421.9&nbsp;&nbsp;1207.9&nbsp;&nbsp;77.3&nbsp;&nbsp; 115.08</td>\n",
       "      <td>1.40</td>\n",
       "      <td>3.5</td>\n",
       "      <td>152.393</td>\n",
       "      <td>12.6160</td>\n",
       "      <td>-11.2160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  qtr  realgdp  realcons    realinvs realgovt realdpi cpi_u M1  \\\n",
       "0  1950.0  1.0   1610.5    1058.9  198.1   361.0  1186.1  70.6   110.20   \n",
       "1  1950.0  2.0   1658.8    1075.9  220.4   366.4  1178.1  71.4   111.75   \n",
       "2  1950.0  3.0   1723.0    1131.0  239.7   359.6  1196.5  73.2   112.95   \n",
       "3  1950.0  4.0   1753.9    1097.6  271.8   382.5  1210.0  74.9   113.93   \n",
       "4  1951.0  1.0   1773.5    1122.8  242.9   421.9  1207.9  77.3   115.08   \n",
       "\n",
       "   tbilrate  unemp      pop     infl  realint  \n",
       "0      1.12    6.4  149.461   0.0000   0.0000  \n",
       "1      1.17    5.6  150.260   4.5071  -3.3404  \n",
       "2      1.23    4.6  151.064   9.9590  -8.7290  \n",
       "3      1.35    4.2  151.871   9.1834  -7.8301  \n",
       "4      1.40    3.5  152.393  12.6160 -11.2160  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greene = pd.read_fwf(\n",
    "    \"https://raw.githubusercontent.com/statsmodels/smdatasets/main/data/autoregressive-distributed-lag/green/ardl_data.txt\"\n",
    ")\n",
    "greene.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c3b0d83-e9b2-4f60-86b3-d7672fd73fe9",
   "metadata": {},
   "source": [
    "We then transform the index to be a pandas `DatetimeIndex` so that we can easily use seasonal terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96110bd7-6445-45c6-838b-cb091c526a9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.520154Z",
     "iopub.status.busy": "2026-07-28T19:10:09.519944Z",
     "iopub.status.idle": "2026-07-28T19:10:09.580755Z",
     "shell.execute_reply": "2026-07-28T19:10:09.577451Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5843/2457281465.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  index = pd.to_datetime(\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>qtr</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinvs realgovt realdpi cpi_u M1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1610.5</td>\n",
       "      <td>1058.9</td>\n",
       "      <td>198.1&nbsp;&nbsp; 361.0&nbsp;&nbsp;1186.1&nbsp;&nbsp;70.6&nbsp;&nbsp; 110.20</td>\n",
       "      <td>1.12</td>\n",
       "      <td>6.4</td>\n",
       "      <td>149.461</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1658.8</td>\n",
       "      <td>1075.9</td>\n",
       "      <td>220.4&nbsp;&nbsp; 366.4&nbsp;&nbsp;1178.1&nbsp;&nbsp;71.4&nbsp;&nbsp; 111.75</td>\n",
       "      <td>1.17</td>\n",
       "      <td>5.6</td>\n",
       "      <td>150.260</td>\n",
       "      <td>4.5071</td>\n",
       "      <td>-3.3404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>239.7&nbsp;&nbsp; 359.6&nbsp;&nbsp;1196.5&nbsp;&nbsp;73.2&nbsp;&nbsp; 112.95</td>\n",
       "      <td>1.23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>151.064</td>\n",
       "      <td>9.9590</td>\n",
       "      <td>-8.7290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1753.9</td>\n",
       "      <td>1097.6</td>\n",
       "      <td>271.8&nbsp;&nbsp; 382.5&nbsp;&nbsp;1210.0&nbsp;&nbsp;74.9&nbsp;&nbsp; 113.93</td>\n",
       "      <td>1.35</td>\n",
       "      <td>4.2</td>\n",
       "      <td>151.871</td>\n",
       "      <td>9.1834</td>\n",
       "      <td>-7.8301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>1951.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1773.5</td>\n",
       "      <td>1122.8</td>\n",
       "      <td>242.9&nbsp;&nbsp; 421.9&nbsp;&nbsp;1207.9&nbsp;&nbsp;77.3&nbsp;&nbsp; 115.08</td>\n",
       "      <td>1.40</td>\n",
       "      <td>3.5</td>\n",
       "      <td>152.393</td>\n",
       "      <td>12.6160</td>\n",
       "      <td>-11.2160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  qtr  realgdp  realcons  \\\n",
       "1950-01-01  1950.0  1.0   1610.5    1058.9   \n",
       "1950-04-01  1950.0  2.0   1658.8    1075.9   \n",
       "1950-07-01  1950.0  3.0   1723.0    1131.0   \n",
       "1950-10-01  1950.0  4.0   1753.9    1097.6   \n",
       "1951-01-01  1951.0  1.0   1773.5    1122.8   \n",
       "\n",
       "              realinvs realgovt realdpi cpi_u M1  tbilrate  unemp      pop  \\\n",
       "1950-01-01  198.1   361.0  1186.1  70.6   110.20      1.12    6.4  149.461   \n",
       "1950-04-01  220.4   366.4  1178.1  71.4   111.75      1.17    5.6  150.260   \n",
       "1950-07-01  239.7   359.6  1196.5  73.2   112.95      1.23    4.6  151.064   \n",
       "1950-10-01  271.8   382.5  1210.0  74.9   113.93      1.35    4.2  151.871   \n",
       "1951-01-01  242.9   421.9  1207.9  77.3   115.08      1.40    3.5  152.393   \n",
       "\n",
       "               infl  realint  \n",
       "1950-01-01   0.0000   0.0000  \n",
       "1950-04-01   4.5071  -3.3404  \n",
       "1950-07-01   9.9590  -8.7290  \n",
       "1950-10-01   9.1834  -7.8301  \n",
       "1951-01-01  12.6160 -11.2160  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.to_datetime(\n",
    "    greene.Year.astype(\"int\").astype(\"str\")\n",
    "    + \"Q\"\n",
    "    + greene.qtr.astype(\"int\").astype(\"str\")\n",
    ")\n",
    "greene.index = index\n",
    "greene.index.freq = greene.index.inferred_freq\n",
    "greene.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7f7b30-72a5-4263-92f4-4365f9d0cbfb",
   "metadata": {},
   "source": [
    "We defined `g` as the log of real gdp and `c` as the log of real consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "78428267-7756-434f-899a-f40ae180e7e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.589518Z",
     "iopub.status.busy": "2026-07-28T19:10:09.589305Z",
     "iopub.status.idle": "2026-07-28T19:10:09.597120Z",
     "shell.execute_reply": "2026-07-28T19:10:09.594365Z"
    }
   },
   "outputs": [],
   "source": [
    "greene[\"c\"] = np.log(greene.realcons)\n",
    "greene[\"g\"] = np.log(greene.realgdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9def354e-f815-40e2-a1b0-f1e3f0f89d0d",
   "metadata": {},
   "source": [
    "### Lag Length Selection\n",
    "\n",
    "The selected model contains 5 lags of consumption and 2 of growth (0 and 1). Here we include seasonal terms although these are not significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d5160337-96e1-429c-8cee-7dd096e1ee04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.601442Z",
     "iopub.status.busy": "2026-07-28T19:10:09.601238Z",
     "iopub.status.idle": "2026-07-28T19:10:09.643314Z",
     "shell.execute_reply": "2026-07-28T19:10:09.642746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_res = ardl_select_order(\n",
    "    greene.c, 8, greene[[\"g\"]], 8, trend=\"c\", seasonal=True, ic=\"aic\"\n",
    ")\n",
    "ardl = sel_res.model\n",
    "ardl.ardl_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "deba851b-1617-4ab2-acc5-0909d1835edc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.650952Z",
     "iopub.status.busy": "2026-07-28T19:10:09.650663Z",
     "iopub.status.idle": "2026-07-28T19:10:09.705600Z",
     "shell.execute_reply": "2026-07-28T19:10:09.704760Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARDL Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>c</td>        <th>  No. Observations:  </th>    <td>204</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>         <td>Seas. ARDL(5, 1)</td> <th>  Log Likelihood     </th>  <td>747.939</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.006</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-1471.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:09</td>     <th>  BIC                </th> <td>-1432.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>04-01-1951</td>    <th>  HQIC               </th> <td>-1455.883</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 10-01-2000</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>   -0.0586</td> <td>    0.028</td> <td>   -2.103</td> <td> 0.037</td> <td>   -0.113</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(2,4)</th> <td>   -0.0001</td> <td>    0.001</td> <td>   -0.087</td> <td> 0.930</td> <td>   -0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(3,4)</th> <td>    0.0012</td> <td>    0.001</td> <td>    1.026</td> <td> 0.306</td> <td>   -0.001</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s(4,4)</th> <td>    0.0003</td> <td>    0.001</td> <td>    0.296</td> <td> 0.768</td> <td>   -0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L1</th>   <td>    0.8545</td> <td>    0.064</td> <td>   13.263</td> <td> 0.000</td> <td>    0.727</td> <td>    0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L2</th>   <td>    0.2588</td> <td>    0.082</td> <td>    3.151</td> <td> 0.002</td> <td>    0.097</td> <td>    0.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L3</th>   <td>   -0.1566</td> <td>    0.072</td> <td>   -2.190</td> <td> 0.030</td> <td>   -0.298</td> <td>   -0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L4</th>   <td>   -0.1941</td> <td>    0.070</td> <td>   -2.754</td> <td> 0.006</td> <td>   -0.333</td> <td>   -0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L5</th>   <td>    0.1695</td> <td>    0.048</td> <td>    3.495</td> <td> 0.001</td> <td>    0.074</td> <td>    0.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>g.L0</th>   <td>    0.5476</td> <td>    0.048</td> <td>   11.350</td> <td> 0.000</td> <td>    0.452</td> <td>    0.643</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>g.L1</th>   <td>   -0.4757</td> <td>    0.051</td> <td>   -9.311</td> <td> 0.000</td> <td>   -0.576</td> <td>   -0.375</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &        c         & \\textbf{  No. Observations:  } &    204      \\\\\n",
       "\\textbf{Model:}         & Seas. ARDL(5, 1) & \\textbf{  Log Likelihood     } &  747.939    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.006     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } & -1471.878   \\\\\n",
       "\\textbf{Time:}          &     19:10:09     & \\textbf{  BIC                } & -1432.358   \\\\\n",
       "\\textbf{Sample:}        &    04-01-1951    & \\textbf{  HQIC               } & -1455.883   \\\\\n",
       "\\textbf{}               &   - 10-01-2000   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &      -0.0586  &        0.028     &    -2.103  &         0.037        &       -0.113    &       -0.004     \\\\\n",
       "\\textbf{s(2,4)} &      -0.0001  &        0.001     &    -0.087  &         0.930        &       -0.002    &        0.002     \\\\\n",
       "\\textbf{s(3,4)} &       0.0012  &        0.001     &     1.026  &         0.306        &       -0.001    &        0.004     \\\\\n",
       "\\textbf{s(4,4)} &       0.0003  &        0.001     &     0.296  &         0.768        &       -0.002    &        0.003     \\\\\n",
       "\\textbf{c.L1}   &       0.8545  &        0.064     &    13.263  &         0.000        &        0.727    &        0.982     \\\\\n",
       "\\textbf{c.L2}   &       0.2588  &        0.082     &     3.151  &         0.002        &        0.097    &        0.421     \\\\\n",
       "\\textbf{c.L3}   &      -0.1566  &        0.072     &    -2.190  &         0.030        &       -0.298    &       -0.016     \\\\\n",
       "\\textbf{c.L4}   &      -0.1941  &        0.070     &    -2.754  &         0.006        &       -0.333    &       -0.055     \\\\\n",
       "\\textbf{c.L5}   &       0.1695  &        0.048     &     3.495  &         0.001        &        0.074    &        0.265     \\\\\n",
       "\\textbf{g.L0}   &       0.5476  &        0.048     &    11.350  &         0.000        &        0.452    &        0.643     \\\\\n",
       "\\textbf{g.L1}   &      -0.4757  &        0.051     &    -9.311  &         0.000        &       -0.576    &       -0.375     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{ARDL Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARDL Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      c   No. Observations:                  204\n",
       "Model:               Seas. ARDL(5, 1)   Log Likelihood                 747.939\n",
       "Method:               Conditional MLE   S.D. of innovations              0.006\n",
       "Date:                Tue, 28 Jul 2026   AIC                          -1471.878\n",
       "Time:                        19:10:09   BIC                          -1432.358\n",
       "Sample:                    04-01-1951   HQIC                         -1455.883\n",
       "                         - 10-01-2000                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0586      0.028     -2.103      0.037      -0.113      -0.004\n",
       "s(2,4)        -0.0001      0.001     -0.087      0.930      -0.002       0.002\n",
       "s(3,4)         0.0012      0.001      1.026      0.306      -0.001       0.004\n",
       "s(4,4)         0.0003      0.001      0.296      0.768      -0.002       0.003\n",
       "c.L1           0.8545      0.064     13.263      0.000       0.727       0.982\n",
       "c.L2           0.2588      0.082      3.151      0.002       0.097       0.421\n",
       "c.L3          -0.1566      0.072     -2.190      0.030      -0.298      -0.016\n",
       "c.L4          -0.1941      0.070     -2.754      0.006      -0.333      -0.055\n",
       "c.L5           0.1695      0.048      3.495      0.001       0.074       0.265\n",
       "g.L0           0.5476      0.048     11.350      0.000       0.452       0.643\n",
       "g.L1          -0.4757      0.051     -9.311      0.000      -0.576      -0.375\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = ardl.fit(use_t=True)\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc10eb0-4a92-4885-a774-91d3192b1200",
   "metadata": {},
   "source": [
    "`from_ardl` is a simple way to get the equivalent UECM specification. Here we rerun the selection without the seasonal terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4f97468-beaa-47d0-9078-e7eee3d3da64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.713905Z",
     "iopub.status.busy": "2026-07-28T19:10:09.713665Z",
     "iopub.status.idle": "2026-07-28T19:10:09.803745Z",
     "shell.execute_reply": "2026-07-28T19:10:09.800907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>UECM Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>D.c</td>       <th>  No. Observations:  </th>    <td>204</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>UECM(5, 1)</td>    <th>  Log Likelihood     </th>  <td>747.131</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.006</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-1476.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:09</td>     <th>  BIC                </th> <td>-1446.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>04-01-1951</td>    <th>  HQIC               </th> <td>-1464.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 10-01-2000</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>   -0.0586</td> <td>    0.028</td> <td>   -2.115</td> <td> 0.036</td> <td>   -0.113</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L1</th>   <td>   -0.0684</td> <td>    0.028</td> <td>   -2.406</td> <td> 0.017</td> <td>   -0.125</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>g.L1</th>   <td>    0.0725</td> <td>    0.030</td> <td>    2.400</td> <td> 0.017</td> <td>    0.013</td> <td>    0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.c.L1</th> <td>   -0.0767</td> <td>    0.059</td> <td>   -1.302</td> <td> 0.195</td> <td>   -0.193</td> <td>    0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.c.L2</th> <td>    0.1842</td> <td>    0.053</td> <td>    3.495</td> <td> 0.001</td> <td>    0.080</td> <td>    0.288</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.c.L3</th> <td>    0.0198</td> <td>    0.049</td> <td>    0.404</td> <td> 0.687</td> <td>   -0.077</td> <td>    0.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.c.L4</th> <td>   -0.1664</td> <td>    0.048</td> <td>   -3.460</td> <td> 0.001</td> <td>   -0.261</td> <td>   -0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.g.L0</th> <td>    0.5440</td> <td>    0.048</td> <td>   11.372</td> <td> 0.000</td> <td>    0.450</td> <td>    0.638</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &       D.c        & \\textbf{  No. Observations:  } &    204      \\\\\n",
       "\\textbf{Model:}         &    UECM(5, 1)    & \\textbf{  Log Likelihood     } &  747.131    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.006     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } & -1476.262   \\\\\n",
       "\\textbf{Time:}          &     19:10:09     & \\textbf{  BIC                } & -1446.622   \\\\\n",
       "\\textbf{Sample:}        &    04-01-1951    & \\textbf{  HQIC               } & -1464.266   \\\\\n",
       "\\textbf{}               &   - 10-01-2000   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &      -0.0586  &        0.028     &    -2.115  &         0.036        &       -0.113    &       -0.004     \\\\\n",
       "\\textbf{c.L1}   &      -0.0684  &        0.028     &    -2.406  &         0.017        &       -0.125    &       -0.012     \\\\\n",
       "\\textbf{g.L1}   &       0.0725  &        0.030     &     2.400  &         0.017        &        0.013    &        0.132     \\\\\n",
       "\\textbf{D.c.L1} &      -0.0767  &        0.059     &    -1.302  &         0.195        &       -0.193    &        0.040     \\\\\n",
       "\\textbf{D.c.L2} &       0.1842  &        0.053     &     3.495  &         0.001        &        0.080    &        0.288     \\\\\n",
       "\\textbf{D.c.L3} &       0.0198  &        0.049     &     0.404  &         0.687        &       -0.077    &        0.116     \\\\\n",
       "\\textbf{D.c.L4} &      -0.1664  &        0.048     &    -3.460  &         0.001        &       -0.261    &       -0.072     \\\\\n",
       "\\textbf{D.g.L0} &       0.5440  &        0.048     &    11.372  &         0.000        &        0.450    &        0.638     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{UECM Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              UECM Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    D.c   No. Observations:                  204\n",
       "Model:                     UECM(5, 1)   Log Likelihood                 747.131\n",
       "Method:               Conditional MLE   S.D. of innovations              0.006\n",
       "Date:                Tue, 28 Jul 2026   AIC                          -1476.262\n",
       "Time:                        19:10:09   BIC                          -1446.622\n",
       "Sample:                    04-01-1951   HQIC                         -1464.266\n",
       "                         - 10-01-2000                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0586      0.028     -2.115      0.036      -0.113      -0.004\n",
       "c.L1          -0.0684      0.028     -2.406      0.017      -0.125      -0.012\n",
       "g.L1           0.0725      0.030      2.400      0.017       0.013       0.132\n",
       "D.c.L1        -0.0767      0.059     -1.302      0.195      -0.193       0.040\n",
       "D.c.L2         0.1842      0.053      3.495      0.001       0.080       0.288\n",
       "D.c.L3         0.0198      0.049      0.404      0.687      -0.077       0.116\n",
       "D.c.L4        -0.1664      0.048     -3.460      0.001      -0.261      -0.072\n",
       "D.g.L0         0.5440      0.048     11.372      0.000       0.450       0.638\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_res = ardl_select_order(greene.c, 8, greene[[\"g\"]], 8, trend=\"c\", ic=\"aic\")\n",
    "\n",
    "uecm = UECM.from_ardl(sel_res.model)\n",
    "uecm_res = uecm.fit()\n",
    "uecm_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71443176-fca6-4105-80c8-eb18261f9724",
   "metadata": {},
   "source": [
    "We see that for every % increase in consumption, we need a 1.05% increase in gdp. In other words, the saving rate is estimated to be around 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "43a66215-0416-4c6e-8c9f-423d87944a47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.806274Z",
     "iopub.status.busy": "2026-07-28T19:10:09.806015Z",
     "iopub.status.idle": "2026-07-28T19:10:09.838021Z",
     "shell.execute_reply": "2026-07-28T19:10:09.832312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Cointegrating Vector</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.8572</td> <td>    0.112</td> <td>    7.632</td> <td> 0.000</td> <td>    0.636</td> <td>    1.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L1</th>  <td>    1.0000</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>g.L1</th>  <td>   -1.0590</td> <td>    0.013</td> <td>  -83.157</td> <td> 0.000</td> <td>   -1.084</td> <td>   -1.034</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccccc}\n",
       "\\toprule\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.8572  &        0.112     &     7.632  &         0.000        &        0.636    &        1.079     \\\\\n",
       "\\textbf{c.L1}  &       1.0000  &            0     &       nan  &           nan        &        1.000    &        1.000     \\\\\n",
       "\\textbf{g.L1}  &      -1.0590  &        0.013     &   -83.157  &         0.000        &       -1.084    &       -1.034     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Cointegrating Vector}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             Cointegrating Vector                             \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.8572      0.112      7.632      0.000       0.636       1.079\n",
       "c.L1           1.0000          0        nan        nan       1.000       1.000\n",
       "g.L1          -1.0590      0.013    -83.157      0.000      -1.084      -1.034\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uecm_res.ci_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9e97dfd1-0c68-42db-b5f9-5038dd5719d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:09.840117Z",
     "iopub.status.busy": "2026-07-28T19:10:09.839934Z",
     "iopub.status.idle": "2026-07-28T19:10:10.326412Z",
     "shell.execute_reply": "2026-07-28T19:10:10.323653Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = uecm_res.ci_resids.plot(title=\"Cointegrating Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8eb317-74fc-4028-8cce-c17a311dff40",
   "metadata": {},
   "source": [
    "### Direct Specification of `UECM` models\n",
    "\n",
    "`UECM` can be used to directly specify model lag lengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a4180f7b-65e8-4389-b68a-a7567aca759e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:10.330901Z",
     "iopub.status.busy": "2026-07-28T19:10:10.330648Z",
     "iopub.status.idle": "2026-07-28T19:10:10.373178Z",
     "shell.execute_reply": "2026-07-28T19:10:10.369588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>UECM Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>D.c</td>       <th>  No. Observations:  </th>    <td>204</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>UECM(2, 1)</td>    <th>  Log Likelihood     </th>  <td>724.499</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>         <td>Conditional MLE</td> <th>  S.D. of innovations</th>   <td>0.007</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Tue, 28 Jul 2026</td> <th>  AIC                </th> <td>-1436.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:10:10</td>     <th>  BIC                </th> <td>-1417.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>07-01-1950</td>    <th>  HQIC               </th> <td>-1428.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 10-01-2000</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>   -0.0778</td> <td>    0.030</td> <td>   -2.601</td> <td> 0.010</td> <td>   -0.137</td> <td>   -0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L1</th>   <td>   -0.0885</td> <td>    0.031</td> <td>   -2.854</td> <td> 0.005</td> <td>   -0.150</td> <td>   -0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>g.L1</th>   <td>    0.0939</td> <td>    0.033</td> <td>    2.857</td> <td> 0.005</td> <td>    0.029</td> <td>    0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.c.L1</th> <td>   -0.1926</td> <td>    0.058</td> <td>   -3.340</td> <td> 0.001</td> <td>   -0.306</td> <td>   -0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>D.g.L0</th> <td>    0.6395</td> <td>    0.053</td> <td>   12.050</td> <td> 0.000</td> <td>    0.535</td> <td>    0.744</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:} &       D.c        & \\textbf{  No. Observations:  } &    204      \\\\\n",
       "\\textbf{Model:}         &    UECM(2, 1)    & \\textbf{  Log Likelihood     } &  724.499    \\\\\n",
       "\\textbf{Method:}        & Conditional MLE  & \\textbf{  S.D. of innovations} &   0.007     \\\\\n",
       "\\textbf{Date:}          & Tue, 28 Jul 2026 & \\textbf{  AIC                } & -1436.998   \\\\\n",
       "\\textbf{Time:}          &     19:10:10     & \\textbf{  BIC                } & -1417.149   \\\\\n",
       "\\textbf{Sample:}        &    07-01-1950    & \\textbf{  HQIC               } & -1428.967   \\\\\n",
       "\\textbf{}               &   - 10-01-2000   & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const}  &      -0.0778  &        0.030     &    -2.601  &         0.010        &       -0.137    &       -0.019     \\\\\n",
       "\\textbf{c.L1}   &      -0.0885  &        0.031     &    -2.854  &         0.005        &       -0.150    &       -0.027     \\\\\n",
       "\\textbf{g.L1}   &       0.0939  &        0.033     &     2.857  &         0.005        &        0.029    &        0.159     \\\\\n",
       "\\textbf{D.c.L1} &      -0.1926  &        0.058     &    -3.340  &         0.001        &       -0.306    &       -0.079     \\\\\n",
       "\\textbf{D.g.L0} &       0.6395  &        0.053     &    12.050  &         0.000        &        0.535    &        0.744     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{UECM Model Results}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              UECM Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                    D.c   No. Observations:                  204\n",
       "Model:                     UECM(2, 1)   Log Likelihood                 724.499\n",
       "Method:               Conditional MLE   S.D. of innovations              0.007\n",
       "Date:                Tue, 28 Jul 2026   AIC                          -1436.998\n",
       "Time:                        19:10:10   BIC                          -1417.149\n",
       "Sample:                    07-01-1950   HQIC                         -1428.967\n",
       "                         - 10-01-2000                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0778      0.030     -2.601      0.010      -0.137      -0.019\n",
       "c.L1          -0.0885      0.031     -2.854      0.005      -0.150      -0.027\n",
       "g.L1           0.0939      0.033      2.857      0.005       0.029       0.159\n",
       "D.c.L1        -0.1926      0.058     -3.340      0.001      -0.306      -0.079\n",
       "D.g.L0         0.6395      0.053     12.050      0.000       0.535       0.744\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uecm = UECM(greene.c, 2, greene[[\"g\"]], 1, trend=\"c\")\n",
    "uecm_res = uecm.fit()\n",
    "uecm_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9259982-b1e9-4384-bd2e-f7c41a7e1165",
   "metadata": {},
   "source": [
    "The changes in the lag structure make little difference in the estimated long-run relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "90124be5-398c-4261-ac16-415454d7f468",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:10.375967Z",
     "iopub.status.busy": "2026-07-28T19:10:10.375691Z",
     "iopub.status.idle": "2026-07-28T19:10:10.399933Z",
     "shell.execute_reply": "2026-07-28T19:10:10.397190Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Cointegrating Vector</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.8789</td> <td>    0.096</td> <td>    9.183</td> <td> 0.000</td> <td>    0.690</td> <td>    1.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>c.L1</th>  <td>    1.0000</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>g.L1</th>  <td>   -1.0605</td> <td>    0.011</td> <td>  -94.148</td> <td> 0.000</td> <td>   -1.083</td> <td>   -1.038</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lcccccc}\n",
       "\\toprule\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       0.8789  &        0.096     &     9.183  &         0.000        &        0.690    &        1.068     \\\\\n",
       "\\textbf{c.L1}  &       1.0000  &            0     &       nan  &           nan        &        1.000    &        1.000     \\\\\n",
       "\\textbf{g.L1}  &      -1.0605  &        0.011     &   -94.148  &         0.000        &       -1.083    &       -1.038     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{Cointegrating Vector}\n",
       "\\end{center}"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             Cointegrating Vector                             \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.8789      0.096      9.183      0.000       0.690       1.068\n",
       "c.L1           1.0000          0        nan        nan       1.000       1.000\n",
       "g.L1          -1.0605      0.011    -94.148      0.000      -1.083      -1.038\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uecm_res.ci_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c7dfb3-e2e6-48d6-9ae4-c9ad8404cf84",
   "metadata": {},
   "source": [
    "## Bounds Testing\n",
    "\n",
    "`UECMResults` expose the bounds test of Pesaran, Shin, and Smith (2001).  This test facilitates testing whether there is a level relationship between a set of variables without identifying which variables are I(1). This test provides two sets of critical and p-values.  If the test statistic is below the critical value for the lower bound, then there appears to be no levels relationship irrespective of the order or integration in the $X$ variables.  If it is above the upper bound, then there appears to be a levels relationship again, irrespective of the order of integration of the $X$ variables. There are 5 cases covered in the paper that include different combinations of deterministic regressors in the model or the test.\n",
    "\n",
    "\n",
    "$$\\Delta Y_{t}=\\delta_{0} + \\delta_{1}t + Z_{t-1}\\beta + \\sum_{j=0}^{P}\\Delta X_{t-j}\\Gamma + \\epsilon_{t}$$\n",
    "\n",
    "where $Z_{t-1}$ includes both $Y_{t-1}$ and $X_{t-1}$.\n",
    "\n",
    "The cases determine which deterministic terms are included in the model and which are tested as part of the test.\n",
    "\n",
    "1. No deterministic terms\n",
    "2. Constant included in both the model and the test\n",
    "3. Constant included in the model but not in the test\n",
    "4. Constant and trend included in the model, only trend included in the test\n",
    "5. Constant and trend included in the model, neither included in the test\n",
    "\n",
    "Here we run the test on the Danish money demand data set. Here we see the test statistic is above the 95% critical value for both the lower and upper.\n",
    "\n",
    "\n",
    "Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a05b5719-4d4c-4dbf-888a-06b2d6f08183",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:10.401889Z",
     "iopub.status.busy": "2026-07-28T19:10:10.401666Z",
     "iopub.status.idle": "2026-07-28T19:10:10.437207Z",
     "shell.execute_reply": "2026-07-28T19:10:10.436378Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundsTestResult\n",
       "Stat: 5.07063\n",
       "Upper P-value: 0.0068\n",
       "Lower P-value: 0.000751\n",
       "Null: No Cointegration\n",
       "Alternative: Possible Cointegration"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecm = UECM(data.lrm, 3, data[[\"lry\", \"ibo\", \"ide\"]], 3, trend=\"c\")\n",
    "ecm_fit = ecm.fit()\n",
    "bounds_test = ecm_fit.bounds_test(case=4)\n",
    "bounds_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba3abe46-ea62-4ee8-833a-d7a8acb57197",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:10.440915Z",
     "iopub.status.busy": "2026-07-28T19:10:10.439055Z",
     "iopub.status.idle": "2026-07-28T19:10:10.452946Z",
     "shell.execute_reply": "2026-07-28T19:10:10.452283Z"
    }
   },
   "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>lower</th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90.0</th>\n",
       "      <td>2.690222</td>\n",
       "      <td>3.523115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95.0</th>\n",
       "      <td>3.069910</td>\n",
       "      <td>3.957893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.0</th>\n",
       "      <td>3.877333</td>\n",
       "      <td>4.867071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9</th>\n",
       "      <td>4.940583</td>\n",
       "      <td>6.040331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               lower     upper\n",
       "percentile                    \n",
       "90.0        2.690222  3.523115\n",
       "95.0        3.069910  3.957893\n",
       "99.0        3.877333  4.867071\n",
       "99.9        4.940583  6.040331"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bounds_test.crit_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16640819-83e7-451b-8fe9-a76cc5598204",
   "metadata": {},
   "source": [
    "Case 3 also rejects the null of no levels relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "717c3deb-fac2-40a2-b719-be3f8b28d747",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-28T19:10:10.456849Z",
     "iopub.status.busy": "2026-07-28T19:10:10.456516Z",
     "iopub.status.idle": "2026-07-28T19:10:10.483084Z",
     "shell.execute_reply": "2026-07-28T19:10:10.482540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundsTestResult\n",
       "Stat: 5.99305\n",
       "Upper P-value: 0.00205\n",
       "Lower P-value: 0.000138\n",
       "Null: No Cointegration\n",
       "Alternative: Possible Cointegration"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecm = UECM(data.lrm, 3, data[[\"lry\", \"ibo\", \"ide\"]], 3, trend=\"c\")\n",
    "ecm_fit = ecm.fit()\n",
    "bounds_test = ecm_fit.bounds_test(case=3)\n",
    "bounds_test"
   ]
  }
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