21_heterogeneity_endogenous_regimes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acddadb6",
   "metadata": {},
   "source": [
    "# Spatial Heterogeneity - Endogenous Regimes\n",
    "\n",
    "### Luc Anselin\n",
    "\n",
    "### 02/11/2025"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211469a7",
   "metadata": {},
   "source": [
    "## Preliminaries\n",
    "\n",
    "In this final noteboook devoted to spatial heterogeneity, we consider endogenous regimes obtained as a result of spatially constrained clustering using the *Skater* algorithm. Instead of having the regimes specified beforehand, their optimal configuration is obtained by incorporating the estimation functionality in a Skater clustering algorithm. This is implemented by means of `spreg.OLS_Endog_Regimes`, `spreg.TSLS_Endog_Regimes` and `spreg.GM_Lag_Endog_Regimes`. Since the method is based on a measure of fit, it is not appropriate for error models.\n",
    "\n",
    "In addition to the same modules as before, we now also use two helper functions to summarize the cluster statistics (`cluster_stats`) and map the cluster layout (`cluster_map`). These helper functions are part of the `spatial_cluster_course` module available from https://github.com/lanselin/notebooks_for_spatial_clustering.\n",
    "\n",
    "Note that this notebook requires `spreg` version 1.8.2. or later. Use `pip install -U spreg` to get the latest version."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7788a327",
   "metadata": {},
   "source": [
    "### Modules Needed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd0e1676",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "os.environ['USE_PYGEOS'] = '0'\n",
    "import geopandas as gpd\n",
    "from libpysal.io import open\n",
    "from libpysal.examples import get_path\n",
    "from spreg import OLS_Endog_Regimes, GM_Lag_Endog_Regimes\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6011a647",
   "metadata": {},
   "source": [
    "### Functions Used\n",
    "\n",
    "- from geopandas:\n",
    "  - read_file\n",
    "  - plot\n",
    "  \n",
    "- from libpysal:\n",
    "  - libpsyal.examples.get_path\n",
    "  - libpysal.io.open\n",
    "\n",
    "- from spreg:\n",
    "  - OLS_Endog_Regimes\n",
    "  - GM_Lag_Endog_Regimes\n",
    "\n",
    "- helper functions:\n",
    "  - cluster_stats\n",
    "  - cluster_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797cf730",
   "metadata": {},
   "source": [
    "### Data, Weights and Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61854073",
   "metadata": {},
   "source": [
    "As in the previous notebooks, all data sets, weights files and variables are specified at the top, so that they can be easily changed to other examples.\n",
    "\n",
    "Data sets and weights are again from the **chicagoSDOH** sample data set:\n",
    "\n",
    "- **Chi-SDOH.shp,shx,dbf,prj**: socio-economic indicators of health for 2014 in 791 Chicago tracts\n",
    "- **Chi-SDOH_q.gal**: queen contiguity spatial weights created with *GeoDa*\n",
    "\n",
    "The weights are used in row-standardized form.\n",
    "\n",
    "The initial model specification has **YPLL_rate** (an index measuring premature mortality, i.e., higher values are worse health outcomes) as the dependent variable, and **HIS_ct** (economic hardship index), **Blk14P** (percent Black population), and **Hisp14P** \n",
    "(percent Hispanic population) as the explanatory variables. Note that there is *no regime variable*. This is determined internally.\n",
    "\n",
    "The various initializations are carried out in two steps:\n",
    "\n",
    "- first, all file names and variable names are defined\n",
    "- second, the files are read and variable vectors/matrices constructed\n",
    "\n",
    "The first step allows for customization to other examples, the second step is agnostic to the actual files and variables that were specified. To keep the code simple, there are no error checks for missing files or mismatches in the variable names."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4171efc9",
   "metadata": {},
   "source": [
    "#### Specify file and variable names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78e719bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "infileshp = get_path(\"Chi-SDOH.shp\")            # input shape file with data\n",
    "infileq = get_path(\"Chi-SDOH_q.gal\")            # queen contiguity weights from GeoDa\n",
    "y_name = 'YPLL_rate'\n",
    "x_names = ['Blk14P','Hisp14P','HIS_ct']\n",
    "ds_name = 'Chi-SDOH'\n",
    "w_name = 'Chi-SDOH_q'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ed505d",
   "metadata": {},
   "source": [
    "#### Read files and extract variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06320547",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = gpd.read_file(infileshp)\n",
    "wq =  open(infileq).read()    # queen contiguity weights\n",
    "wq.transform = 'r'    # row-transform the weights\n",
    "y = dfs[y_name]\n",
    "x = dfs[x_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92fc357",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66ca2b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_stats(clustlabels):\n",
    "    \"\"\"\n",
    "    Creates a data frame with cluster labels and cardinality\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    clustlabels     : cluster labels from a scikit-learn cluster class\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    clustframe      : a pandas dataframe with columns Labels and Cardinality\n",
    "    \"\"\"\n",
    "\n",
    "    totclust,clustcount = np.unique(clustlabels,return_counts=True)\n",
    "    cl = np.array((totclust,clustcount)).T\n",
    "    clustframe = pd.DataFrame(data=cl,columns=[\"Labels\",\"Cardinality\"])\n",
    "    return(clustframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a869de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cluster_map(gdf, clustlabels, title='Clusters', grid_shape=(1, 1), figsize=(5, 5), cmap='Set2', show_axis=False, baselayer=None, markersize=10):\n",
    "    \"\"\"\n",
    "    Plot multiple cluster maps in a grid. Can handle both single and multiple maps.\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    gdf          : geodataframe with the polygons\n",
    "    clustlabels  : list or single array of cluster labels\n",
    "    title        : list or single string of titles for each subplot\n",
    "    grid_shape   : tuple defining the grid layout (default = (1,1))\n",
    "    figsize      : figure size, default = (5,5)\n",
    "    cmap         : colormap, default = 'Set2'\n",
    "    show_axis    : flag to show axis, default = False\n",
    "    baselayer    : geodataframe with the baselayer, default = None\n",
    "    markersize   : size of the markers, if any, default = 10\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    None\n",
    "    \"\"\"\n",
    "    if not isinstance(clustlabels, (list, tuple)):\n",
    "        clustlabels = [clustlabels]\n",
    "    if not isinstance(title, (list, tuple)):\n",
    "        title = [title]\n",
    "    \n",
    "    num_maps = len(clustlabels) \n",
    "    \n",
    "    fig, axes = plt.subplots(grid_shape[0], grid_shape[1], figsize=figsize)\n",
    "    axes = np.array(axes).flatten()\n",
    "    \n",
    "    for i in range(num_maps):\n",
    "        gdf_temp = gdf.copy()  \n",
    "        gdf_temp['cluster'] = np.array(clustlabels[i]).astype(str) \n",
    "        if baselayer is not None:\n",
    "            baselayer.plot(ax=axes[i], color=(1, 1, 1, 0), edgecolor='black')\n",
    "        gdf_temp.plot(column='cluster', ax=axes[i], legend=True, cmap=cmap,\n",
    "                      legend_kwds={'bbox_to_anchor': (1, 0.5), 'loc': 'center left'}, markersize=markersize)\n",
    "        \n",
    "        if not show_axis:\n",
    "            axes[i].axis('off') \n",
    "        \n",
    "        axes[i].set_title(title[i]) \n",
    "    \n",
    "    plt.tight_layout()  \n",
    "    plt.show()  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3811575",
   "metadata": {},
   "source": [
    "## OLS with Endogenous Regimes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbff30f5",
   "metadata": {},
   "source": [
    "### Given number of regimes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd275fc6",
   "metadata": {},
   "source": [
    "The first case we consider is when the number of regimes is pre-determined. This is set with the `n_clusters` argument, equal to 4 in the following example. Another important argument that is in addition to the standard OLS regression arguments (`y`,`x`,`wq`,`spat_diag`) is `quorum`. This determines the minimum number of observations in each regime. The default setting is the maximum of either 30 or 10*k, where k is the number of regimes. In the example below, we set `quorum = 100`.\n",
    "\n",
    "The call to `OLS_Endog_Regimes` creates a regression object that contains all the usual attributes and methods, as well as some additional ones. The coefficient estimates are obtained by means of `print` `summary`. The organization of the output is identical to that for exogenous regimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e905f601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION RESULTS\n",
      "------------------\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 0\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 0_YPLL_rate                Number of Observations:         254\n",
      "Mean dependent var  :   7323.8783                Number of Variables   :           4\n",
      "S.D. dependent var  :   3372.7662                Degrees of Freedom    :         250\n",
      "R-squared           :      0.4051\n",
      "Adjusted R-squared  :      0.3979\n",
      "Sum squared residual: 1.71219e+09                F-statistic           :     56.7410\n",
      "Sigma-square        : 6848778.923                Prob(F-statistic)     :     5.2e-28\n",
      "S.E. of regression  :    2617.017                Log likelihood        :   -2357.321\n",
      "Sigma-square ML     : 6740924.136                Akaike info criterion :    4722.643\n",
      "S.E of regression ML:   2596.3290                Schwarz criterion     :    4736.792\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          0_CONSTANT      -264.87700       699.48479        -0.37867         0.70525\n",
      "            0_Blk14P        38.10930         9.28991         4.10222         0.00006\n",
      "           0_Hisp14P       -24.12993        14.65396        -1.64665         0.10089\n",
      "            0_HIS_ct       107.81897        19.26234         5.59740         0.00000\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          11.817\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2       2313.696           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         87.382           0.0000\n",
      "Koenker-Bassett test              3         10.900           0.0123\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 1\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 1_YPLL_rate                Number of Observations:         115\n",
      "Mean dependent var  :   3671.2886                Number of Variables   :           4\n",
      "S.D. dependent var  :   1406.2425                Degrees of Freedom    :         111\n",
      "R-squared           :      0.1324\n",
      "Adjusted R-squared  :      0.1089\n",
      "Sum squared residual: 1.95594e+08                F-statistic           :      5.6452\n",
      "Sigma-square        : 1762111.535                Prob(F-statistic)     :    0.001228\n",
      "S.E. of regression  :    1327.445                Log likelihood        :    -988.109\n",
      "Sigma-square ML     : 1700820.699                Akaike info criterion :    1984.217\n",
      "S.E of regression ML:   1304.1552                Schwarz criterion     :    1995.197\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          1_CONSTANT      5143.27517       620.69944         8.28626         0.00000\n",
      "            1_Blk14P        33.56547        12.48711         2.68801         0.00829\n",
      "           1_Hisp14P        -0.74194         6.92104        -0.10720         0.91482\n",
      "            1_HIS_ct       -33.67966        17.88241        -1.88340         0.06226\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          15.420\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          7.979           0.0185\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         47.332           0.0000\n",
      "Koenker-Bassett test              3         31.180           0.0000\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 2\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 2_YPLL_rate                Number of Observations:         284\n",
      "Mean dependent var  :   4172.3705                Number of Variables   :           4\n",
      "S.D. dependent var  :   3026.7228                Degrees of Freedom    :         280\n",
      "R-squared           :      0.7375\n",
      "Adjusted R-squared  :      0.7347\n",
      "Sum squared residual: 6.80564e+08                F-statistic           :    262.2155\n",
      "Sigma-square        : 2430587.468                Prob(F-statistic)     :     5.5e-81\n",
      "S.E. of regression  :    1559.034                Log likelihood        :   -2488.882\n",
      "Sigma-square ML     : 2396353.842                Akaike info criterion :    4985.763\n",
      "S.E of regression ML:   1548.0161                Schwarz criterion     :    5000.359\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          2_CONSTANT       741.27565       285.94811         2.59234         0.01003\n",
      "            2_Blk14P        49.37630         6.30117         7.83605         0.00000\n",
      "           2_Hisp14P         2.40728         9.07367         0.26530         0.79097\n",
      "            2_HIS_ct        61.03952        14.37741         4.24552         0.00003\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          11.881\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2        101.574           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         42.660           0.0000\n",
      "Koenker-Bassett test              3         18.261           0.0004\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 3\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 3_YPLL_rate                Number of Observations:         138\n",
      "Mean dependent var  :   3631.4150                Number of Variables   :           4\n",
      "S.D. dependent var  :   1119.5510                Degrees of Freedom    :         134\n",
      "R-squared           :      0.2105\n",
      "Adjusted R-squared  :      0.1929\n",
      "Sum squared residual: 1.35563e+08                F-statistic           :     11.9119\n",
      "Sigma-square        : 1011660.682                Prob(F-statistic)     :   5.785e-07\n",
      "S.E. of regression  :    1005.813                Log likelihood        :   -1147.854\n",
      "Sigma-square ML     :  982337.184                Akaike info criterion :    2303.708\n",
      "S.E of regression ML:    991.1292                Schwarz criterion     :    2315.417\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          3_CONSTANT      3384.59256       464.25207         7.29042         0.00000\n",
      "            3_Blk14P        60.39102        11.05812         5.46124         0.00000\n",
      "           3_Hisp14P        -7.84065         5.46427        -1.43490         0.15365\n",
      "            3_HIS_ct         8.44796        16.57260         0.50976         0.61106\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          17.448\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          4.975           0.0831\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3          4.451           0.2167\n",
      "Koenker-Bassett test              3          3.958           0.2661\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "GLOBAL DIAGNOSTICS\n",
      "\n",
      "REGIMES DIAGNOSTICS\n",
      "Overall SSR         : 2723916133.42\n",
      "Number of clusters  :             4\n",
      "\n",
      "- CHOW TEST -\n",
      "                 VARIABLE        DF        VALUE           PROB\n",
      "            CONSTANT              3         63.025           0.0000\n",
      "              Blk14P              3          3.674           0.2988\n",
      "             Hisp14P              3          3.019           0.3887\n",
      "              HIS_ct              3         34.881           0.0000\n",
      "         Global test             12         89.148           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "- SARERR -\n",
      "TEST                              DF       VALUE           PROB\n",
      "Lagrange Multiplier (lag)         1          3.958           0.0466\n",
      "Robust LM (lag)                   1          3.875           0.0490\n",
      "Lagrange Multiplier (error)       1          0.378           0.5388\n",
      "Robust LM (error)                 1          0.295           0.5872\n",
      "Lagrange Multiplier (SARMA)       2          4.253           0.1193\n",
      "\n",
      "- Spatial Durbin -\n",
      "TEST                              DF       VALUE           PROB\n",
      "LM test for WX                    3         16.835           0.0008\n",
      "Robust LM WX test                 3         13.639           0.0034\n",
      "Lagrange Multiplier (lag)         1          3.958           0.0466\n",
      "Robust LM Lag - SDM               1          0.762           0.3826\n",
      "Joint test for SDM                4         17.597           0.3480\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "ols1 = OLS_Endog_Regimes(y,x,wq,n_clusters=4,quorum=100,\n",
    "                         spat_diag=True,name_ds=ds_name,name_w=w_name)\n",
    "print(ols1.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573f2073",
   "metadata": {},
   "source": [
    "The overall SSR for the four endogenous regimes is 2723916133.42, which compares favorably to the 2857937492.11 obtained for the four exogenous regimes.\n",
    "\n",
    "In contrast to the results for the exogenous regimes reported in a previous notebook, now the Global Chow test very strongly rejects the null hypothesis of homogeneity, even though there is no evidence of heterogeneity in the ethnic variables. The spatial diagnostics show strong evidence for the inclusion of WX, and some weak evidence for a spatial lag, but not spatial Durbin."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5986e18c",
   "metadata": {},
   "source": [
    "#### Cluster characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0874ec",
   "metadata": {},
   "source": [
    "In addition to the usual estimates and measures of fit, the regression object created by the endogenous regimes functionality also contains important characteristics of the clusters themselves.\n",
    "\n",
    "The cluster labels are contained in `clusters`, a numpy array. Note that the cluster labels start at 0, so for four regimes, they go from 0 to 3. We can now use the helper functions `cluster_stats` and `cluster_map` to find the makeup and spatial layout for each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b1bd16d",
   "metadata": {},
   "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>Labels</th>\n",
       "      <th>Cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Labels  Cardinality\n",
       "0       0          254\n",
       "1       1          115\n",
       "2       2          284\n",
       "3       3          138"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stats(ols1.clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b340e48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_map(dfs,ols1.clusters,title=\"Endogenous Regimes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9469342",
   "metadata": {},
   "source": [
    "Another useful piece of information is contained in the `_trace` attribute. It reports on the sequence of steps in the algorithm, i.e., for each *cut* in the minimum spanning tree for the skater algorithm, the resulting cluster labels are given as well as the cut points and the associated value for the objective function. The `_trace` is a list of tuples, consisting of an array with the labels and an internal object with the cut point and `score`.\n",
    "\n",
    "For example, the status of the algorithm for four regimes is contained in `_trace[3]`, shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c5e4d7f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 1, 1, 2, 1, 0, 3, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0,\n",
       "        3, 3, 1, 3, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 1, 0, 2, 2, 2, 3, 2, 2,\n",
       "        2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 0, 1, 2, 2, 2,\n",
       "        2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 0,\n",
       "        1, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 1, 1, 2, 1, 3, 3, 3, 3, 3, 2, 0,\n",
       "        0, 3, 3, 3, 3, 3, 0, 0, 0, 2, 2, 1, 0, 2, 1, 3, 0, 0, 2, 0, 1, 1,\n",
       "        2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 1, 1, 0, 0, 2, 2, 2, 2,\n",
       "        2, 0, 1, 2, 2, 3, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 2, 2,\n",
       "        1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 0, 0, 0, 0, 0, 1, 0, 2,\n",
       "        0, 1, 1, 0, 2, 3, 1, 2, 1, 1, 0, 0, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0,\n",
       "        1, 1, 1, 1, 0, 0, 0, 0, 0, 3, 3, 3, 3, 1, 1, 1, 0, 1, 1, 2, 2, 3,\n",
       "        1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 2, 3, 3, 2, 1,\n",
       "        1, 1, 2, 3, 0, 0, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 1, 0,\n",
       "        0, 0, 1, 1, 0, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 0, 0, 0, 0, 1, 1, 1,\n",
       "        1, 1, 2, 2, 3, 1, 1, 0, 1, 0, 0, 0, 2, 3, 3, 1, 0, 3, 0, 2, 2, 2,\n",
       "        3, 3, 3, 3, 3, 1, 1, 0, 0, 0, 0, 2, 3, 2, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 2, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 3, 0, 0, 3, 3, 3, 3, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 3, 3, 2, 3, 3, 3, 3, 0, 2, 3, 3, 3, 3, 3, 2, 0, 0,\n",
       "        0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 3, 3, 3, 2, 3, 2, 2, 2, 3, 0, 0, 0, 0, 0, 0, 0, 2,\n",
       "        0, 0, 2, 1, 2, 2, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 2, 2, 2, 2, 0,\n",
       "        0, 0, 2, 2, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 2, 2, 0, 0, 0,\n",
       "        1, 1, 1, 1, 0, 0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "        2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 2, 2, 2, 2,\n",
       "        3, 2, 2, 2, 1, 1, 3, 3, 3, 2, 2, 2, 0, 1, 1, 0, 1, 2, 2, 2, 3, 2,\n",
       "        0, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 2,\n",
       "        2, 1, 1, 1, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 2, 2, 1, 1, 2, 2, 2,\n",
       "        2, 2, 2, 1, 1, 1, 2, 2, 0, 0, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 3, 3,\n",
       "        3, 3, 2, 2, 2, 2, 0, 1, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1],\n",
       "       dtype=int32),\n",
       " deletion(in_node=np.int32(11), out_node=np.int32(90), score=np.float64(2723916133.4235063)))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols1._trace[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b877cc",
   "metadata": {},
   "source": [
    "The second item in the tuple contains the value of the objective function. It can be extracted as `_trace[3][1].score`, as shown below. This value is the overall SSR listed in the output. It can be compared for a range of different numbers of regimes, yielding an *elbow plot* to visually identify the optimal number. This is not pursued here. Instead, the *optimal* number of regimes can be determined internally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9caca0fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2723916133.42\n"
     ]
    }
   ],
   "source": [
    "print(np.round(ols1._trace[3][1].score,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "298fb9c0",
   "metadata": {},
   "source": [
    "### Optimal Number of Regimes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1519dc1e",
   "metadata": {},
   "source": [
    "When the `n_clusters` argument is not specified, the optimal number of clusters is determined internally, following Mojena's rule. This essentially assesses the improvement in the objective function and stops when it is deemed insufficient.\n",
    "\n",
    "In order to allow a larger number of clusters, we set the `quorum = 30`. The other arguments are as before.\n",
    "Before listing the coefficient estimates, we examine some properties of the resulting regimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40b40f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "ols2 = OLS_Endog_Regimes(y,x,wq,quorum=30,\n",
    "                         spat_diag=True,name_ds=ds_name,name_w=w_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b25ca98f",
   "metadata": {},
   "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>Labels</th>\n",
       "      <th>Cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Labels  Cardinality\n",
       "0       0          161\n",
       "1       1           99\n",
       "2       2           49\n",
       "3       3           33\n",
       "4       4          119\n",
       "5       5          210\n",
       "6       6           50\n",
       "7       7           32\n",
       "8       8           38"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_stats(ols2.clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b63aa8b",
   "metadata": {},
   "source": [
    "The optimal number of regimes is obtained as 9, ranging in size from 32 observations to 210 observations. The spatial layout is given by `cluster_map` and we extract the value for the object function as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54da01d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_map(dfs,ols2.clusters,title=\"Optimal Endogenous Regimes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cda5aa92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2358809726.99\n"
     ]
    }
   ],
   "source": [
    "print(np.round(ols2._trace[8][1].score,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf3fec7",
   "metadata": {},
   "source": [
    "The coefficients, measures of fit and regression diagnostics follow in the usual way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "924430e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION RESULTS\n",
      "------------------\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 0\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 0_YPLL_rate                Number of Observations:         161\n",
      "Mean dependent var  :   7169.5143                Number of Variables   :           4\n",
      "S.D. dependent var  :   2385.1356                Degrees of Freedom    :         157\n",
      "R-squared           :      0.4553\n",
      "Adjusted R-squared  :      0.4449\n",
      "Sum squared residual: 4.95824e+08                F-statistic           :     43.7386\n",
      "Sigma-square        : 3158116.850                Prob(F-statistic)     :   1.332e-20\n",
      "S.E. of regression  :    1777.109                Log likelihood        :   -1431.146\n",
      "Sigma-square ML     : 3079654.319                Akaike info criterion :    2870.291\n",
      "S.E of regression ML:   1754.8944                Schwarz criterion     :    2882.617\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          0_CONSTANT      1567.55806       742.22095         2.11198         0.03627\n",
      "            0_Blk14P        40.10881         8.26171         4.85478         0.00000\n",
      "           0_Hisp14P       -13.53785        12.54384        -1.07924         0.28213\n",
      "            0_HIS_ct        58.68102        21.10986         2.77979         0.00610\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          14.063\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          2.717           0.2570\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3          7.705           0.0525\n",
      "Koenker-Bassett test              3          5.999           0.1117\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 1\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 1_YPLL_rate                Number of Observations:          99\n",
      "Mean dependent var  :   3682.6091                Number of Variables   :           4\n",
      "S.D. dependent var  :   1347.4298                Degrees of Freedom    :          95\n",
      "R-squared           :      0.2350\n",
      "Adjusted R-squared  :      0.2108\n",
      "Sum squared residual: 1.36114e+08                F-statistic           :      9.7273\n",
      "Sigma-square        : 1432782.849                Prob(F-statistic)     :   1.169e-05\n",
      "S.E. of regression  :    1196.989                Log likelihood        :    -840.102\n",
      "Sigma-square ML     : 1374892.633                Akaike info criterion :    1688.205\n",
      "S.E of regression ML:   1172.5582                Schwarz criterion     :    1698.585\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          1_CONSTANT      5600.07519       623.46679         8.98215         0.00000\n",
      "            1_Blk14P         2.44766        14.64765         0.16710         0.86764\n",
      "           1_Hisp14P       -39.72731        11.73910        -3.38419         0.00104\n",
      "            1_HIS_ct        21.21536        21.37715         0.99243         0.32351\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          20.224\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2         13.339           0.0013\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         48.491           0.0000\n",
      "Koenker-Bassett test              3         25.944           0.0000\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 2\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 2_YPLL_rate                Number of Observations:          49\n",
      "Mean dependent var  :   4634.1137                Number of Variables   :           4\n",
      "S.D. dependent var  :   3411.9263                Degrees of Freedom    :          45\n",
      "R-squared           :      0.2620\n",
      "Adjusted R-squared  :      0.2128\n",
      "Sum squared residual: 4.12403e+08                F-statistic           :      5.3240\n",
      "Sigma-square        : 9164518.761                Prob(F-statistic)     :    0.003167\n",
      "S.E. of regression  :    3027.296                Log likelihood        :    -460.197\n",
      "Sigma-square ML     : 8416394.781                Akaike info criterion :     928.395\n",
      "S.E of regression ML:   2901.1023                Schwarz criterion     :     935.962\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          2_CONSTANT       642.39782      1703.80105         0.37704         0.70792\n",
      "            2_Blk14P        45.60857        16.19433         2.81633         0.00719\n",
      "           2_Hisp14P        47.96520        56.54845         0.84821         0.40081\n",
      "            2_HIS_ct        38.38495        43.82543         0.87586         0.38576\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           9.301\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2        760.816           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         14.205           0.0026\n",
      "Koenker-Bassett test              3          1.425           0.6996\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 3\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 3_YPLL_rate                Number of Observations:          33\n",
      "Mean dependent var  :   4313.1626                Number of Variables   :           4\n",
      "S.D. dependent var  :   5047.5471                Degrees of Freedom    :          29\n",
      "R-squared           :      0.6193\n",
      "Adjusted R-squared  :      0.5799\n",
      "Sum squared residual: 3.10417e+08                F-statistic           :     15.7221\n",
      "Sigma-square        :10704037.181                Prob(F-statistic)     :   2.935e-06\n",
      "S.E. of regression  :    3271.702                Log likelihood        :    -311.764\n",
      "Sigma-square ML     : 9406578.128                Akaike info criterion :     631.528\n",
      "S.E of regression ML:   3067.0145                Schwarz criterion     :     637.514\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          3_CONSTANT     -4430.30712      1655.92475        -2.67543         0.01214\n",
      "            3_Blk14P        37.45367        43.24562         0.86607         0.39356\n",
      "           3_Hisp14P       -41.70947        71.27465        -0.58519         0.56294\n",
      "            3_HIS_ct       273.25534        94.00964         2.90667         0.00693\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          11.420\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2         68.552           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         79.554           0.0000\n",
      "Koenker-Bassett test              3         19.645           0.0002\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 4\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 4_YPLL_rate                Number of Observations:         119\n",
      "Mean dependent var  :   3489.7323                Number of Variables   :           4\n",
      "S.D. dependent var  :    924.0959                Degrees of Freedom    :         115\n",
      "R-squared           :      0.0284\n",
      "Adjusted R-squared  :      0.0031\n",
      "Sum squared residual: 9.79051e+07                F-statistic           :      1.1203\n",
      "Sigma-square        :  851348.508                Prob(F-statistic)     :      0.3439\n",
      "S.E. of regression  :     922.685                Log likelihood        :    -979.267\n",
      "Sigma-square ML     :  822731.752                Akaike info criterion :    1966.533\n",
      "S.E of regression ML:    907.0456                Schwarz criterion     :    1977.650\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          4_CONSTANT      3581.21761       453.98663         7.88838         0.00000\n",
      "            4_Blk14P        23.51982        21.66325         1.08570         0.27988\n",
      "           4_Hisp14P        -6.20659         5.31967        -1.16672         0.24574\n",
      "            4_HIS_ct         3.59212        16.15967         0.22229         0.82448\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          17.354\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          1.478           0.4775\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3          0.115           0.9900\n",
      "Koenker-Bassett test              3          0.119           0.9894\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 5\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 5_YPLL_rate                Number of Observations:         210\n",
      "Mean dependent var  :   2893.0075                Number of Variables   :           4\n",
      "S.D. dependent var  :   1733.3428                Degrees of Freedom    :         206\n",
      "R-squared           :      0.4622\n",
      "Adjusted R-squared  :      0.4543\n",
      "Sum squared residual: 3.37731e+08                F-statistic           :     59.0038\n",
      "Sigma-square        : 1639470.255                Prob(F-statistic)     :   1.422e-27\n",
      "S.E. of regression  :    1280.418                Log likelihood        :   -1798.496\n",
      "Sigma-square ML     : 1608242.250                Akaike info criterion :    3604.991\n",
      "S.E of regression ML:   1268.1649                Schwarz criterion     :    3618.380\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          5_CONSTANT       708.91724       271.98787         2.60643         0.00982\n",
      "            5_Blk14P        70.17152        11.59233         6.05327         0.00000\n",
      "           5_Hisp14P        -2.34048         7.29847        -0.32068         0.74878\n",
      "            5_HIS_ct        61.06936        14.81148         4.12311         0.00005\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          10.408\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2         67.260           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3         63.004           0.0000\n",
      "Koenker-Bassett test              3         29.277           0.0000\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 6\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 6_YPLL_rate                Number of Observations:          50\n",
      "Mean dependent var  :   9530.0923                Number of Variables   :           4\n",
      "S.D. dependent var  :   2997.8307                Degrees of Freedom    :          46\n",
      "R-squared           :      0.2737\n",
      "Adjusted R-squared  :      0.2263\n",
      "Sum squared residual: 3.19849e+08                F-statistic           :      5.7773\n",
      "Sigma-square        : 6953237.671                Prob(F-statistic)     :    0.001938\n",
      "S.E. of regression  :    2636.899                Log likelihood        :    -462.730\n",
      "Sigma-square ML     : 6396978.658                Akaike info criterion :     933.461\n",
      "S.E of regression ML:   2529.2249                Schwarz criterion     :     941.109\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          6_CONSTANT     24160.62071      8836.87976         2.73407         0.00885\n",
      "            6_Blk14P      -147.16395        86.67923        -1.69780         0.09630\n",
      "           6_Hisp14P      -253.49544        94.43810        -2.68425         0.01007\n",
      "            6_HIS_ct         9.65284        63.96308         0.15091         0.88070\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          56.289\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          7.272           0.0264\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3          2.639           0.4506\n",
      "Koenker-Bassett test              3          2.087           0.5546\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 7\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 7_YPLL_rate                Number of Observations:          32\n",
      "Mean dependent var  :   8166.1546                Number of Variables   :           4\n",
      "S.D. dependent var  :   2524.9008                Degrees of Freedom    :          28\n",
      "R-squared           :      0.0264\n",
      "Adjusted R-squared  :     -0.0779\n",
      "Sum squared residual: 1.92403e+08                F-statistic           :      0.2535\n",
      "Sigma-square        : 6871529.582                Prob(F-statistic)     :      0.8582\n",
      "S.E. of regression  :    2621.360                Log likelihood        :    -295.156\n",
      "Sigma-square ML     : 6012588.384                Akaike info criterion :     598.312\n",
      "S.E of regression ML:   2452.0580                Schwarz criterion     :     604.175\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          7_CONSTANT      9080.20357      5085.56584         1.78549         0.08502\n",
      "            7_Blk14P       -31.91519        58.14142        -0.54892         0.58741\n",
      "           7_Hisp14P         0.85429        84.19288         0.01015         0.99198\n",
      "            7_HIS_ct        35.74628        56.67763         0.63069         0.53335\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          27.592\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          0.200           0.9049\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3          0.747           0.8622\n",
      "Koenker-Bassett test              3          0.907           0.8237\n",
      "\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES ESTIMATION - REGIME 8\n",
      "---------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  : 8_YPLL_rate                Number of Observations:          38\n",
      "Mean dependent var  :   8411.6961                Number of Variables   :           4\n",
      "S.D. dependent var  :   1815.5407                Degrees of Freedom    :          34\n",
      "R-squared           :      0.5395\n",
      "Adjusted R-squared  :      0.4989\n",
      "Sum squared residual: 5.61629e+07                F-statistic           :     13.2773\n",
      "Sigma-square        : 1651849.302                Prob(F-statistic)     :    6.73e-06\n",
      "S.E. of regression  :    1285.243                Log likelihood        :    -323.837\n",
      "Sigma-square ML     : 1477970.428                Akaike info criterion :     655.674\n",
      "S.E of regression ML:   1215.7181                Schwarz criterion     :     662.225\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          8_CONSTANT     18999.20328      7267.09588         2.61441         0.01323\n",
      "            8_Blk14P      -144.63735        71.80353        -2.01435         0.05194\n",
      "           8_Hisp14P      -215.14934        72.44500        -2.96983         0.00543\n",
      "            8_HIS_ct        82.39487        34.01491         2.42232         0.02090\n",
      "------------------------------------------------------------------------------------\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER          84.643\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2         24.681           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3          1.145           0.7662\n",
      "Koenker-Bassett test              3          0.433           0.9332\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "GLOBAL DIAGNOSTICS\n",
      "\n",
      "REGIMES DIAGNOSTICS\n",
      "Overall SSR         : 2358809726.99\n",
      "Number of clusters  :             9\n",
      "\n",
      "- CHOW TEST -\n",
      "                 VARIABLE        DF        VALUE           PROB\n",
      "            CONSTANT              8         97.313           0.0000\n",
      "              Blk14P              8         26.791           0.0008\n",
      "             Hisp14P              8         24.032           0.0023\n",
      "              HIS_ct              8         16.589           0.0347\n",
      "         Global test             32        161.121           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "- SARERR -\n",
      "TEST                              DF       VALUE           PROB\n",
      "Lagrange Multiplier (lag)         1          2.184           0.1395\n",
      "Robust LM (lag)                   1          2.510           0.1131\n",
      "Lagrange Multiplier (error)       1          0.065           0.7992\n",
      "Robust LM (error)                 1          0.391           0.5319\n",
      "Lagrange Multiplier (SARMA)       2          2.575           0.2760\n",
      "\n",
      "- Spatial Durbin -\n",
      "TEST                              DF       VALUE           PROB\n",
      "LM test for WX                    3         41.621           0.0000\n",
      "Robust LM WX test                 3         39.522           0.0000\n",
      "Lagrange Multiplier (lag)         1          2.184           0.1395\n",
      "Robust LM Lag - SDM               1          0.085           0.7708\n",
      "Joint test for SDM                4         41.706           0.2365\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "print(ols2.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a2910a",
   "metadata": {},
   "source": [
    "As is to be expected, there is a considerable improvement in the fit relative to four clusters. Also, in this case, coefficient homogeneity is strongly rejected for all but HIS_ct, for which the p-value is <0.04. There is no longer any evidence for a lag alternative, but still strong evidence for the inclusion of WX. This can be implemented in the usual way, by means of `slx_lags=1`, but is not f`urther pursued here.\n",
    "\n",
    "In the endogenous regimes case, the only option is `constant_regi = 'many'` and `cols2regi = 'all'`. However, since the regime solution is available as the `clusters` attribute, it can be used in any exogenous regimes regression with all the available options."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "540176e0",
   "metadata": {},
   "source": [
    "## Spatial Lag with Endogenous Regimes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec233f1",
   "metadata": {},
   "source": [
    "Estimation of the spatial lag model with endogenous regimes is implemented in `GM_Lag_Endog_Regimes`. It operates in the same way as for OLS, with the standard arguments for the `GM_Lag` part and the specific arguments for the endogenous regimes aspect. `GM_Lag_Endog_Regimes` is a special case of `TSLS_Endog_Regimes` for a non-spatial regression with endogenous variables. This is not considered separately since it operates in the same way.\n",
    "\n",
    "Even though there is little/no evidence in the regression diagnostics to motivate a spatial lag model, we briefly illustrate the mechanics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ef22371",
   "metadata": {},
   "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>Labels</th>\n",
       "      <th>Cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Labels  Cardinality\n",
       "0       0          199\n",
       "1       1          115\n",
       "2       2          339\n",
       "3       3          138"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lag1 = GM_Lag_Endog_Regimes(y,x,wq,n_clusters=4,quorum=100,\n",
    "                         spat_diag=True,name_ds=ds_name,name_w=w_name)\n",
    "cluster_stats(lag1.clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54f277bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_map(dfs,lag1.clusters,title=\"GM Lag Endogenous Regimes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34541120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION RESULTS\n",
      "------------------\n",
      "\n",
      "SUMMARY OF OUTPUT: SPATIAL TWO STAGE LEAST SQUARES - REGIMES\n",
      "------------------------------------------------------------\n",
      "Data set            :    Chi-SDOH\n",
      "Weights matrix      :  Chi-SDOH_q\n",
      "Dependent Variable  :   YPLL_rate                Number of Observations:         791\n",
      "Mean dependent var  :   5017.1325                Number of Variables   :          17\n",
      "S.D. dependent var  :   3161.3278                Degrees of Freedom    :         774\n",
      "Pseudo R-squared    :      0.6567\n",
      "Spatial Pseudo R-squared:  0.6562\n",
      "\n",
      "White Standard Errors\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     z-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "          0_CONSTANT      1132.91951       532.67768         2.12684         0.03343\n",
      "            0_Blk14P        25.87906         6.78120         3.81630         0.00014\n",
      "           0_Hisp14P       -33.16300         9.55008        -3.47254         0.00052\n",
      "            0_HIS_ct        88.50324        17.51094         5.05417         0.00000\n",
      "          1_CONSTANT      4903.55916       583.63149         8.40181         0.00000\n",
      "            1_Blk14P        32.20439        24.56445         1.31102         0.18985\n",
      "           1_Hisp14P         1.25221         7.32082         0.17105         0.86419\n",
      "            1_HIS_ct       -38.53055        15.60769        -2.46869         0.01356\n",
      "          2_CONSTANT       165.16916       337.01348         0.49010         0.62407\n",
      "            2_Blk14P        37.46102         6.43470         5.82172         0.00000\n",
      "           2_Hisp14P        -2.92729        10.50110        -0.27876         0.78043\n",
      "            2_HIS_ct        79.15813        23.71714         3.33759         0.00085\n",
      "          3_CONSTANT      3179.58083       459.13358         6.92518         0.00000\n",
      "            3_Blk14P        58.83973        14.50508         4.05649         0.00005\n",
      "           3_Hisp14P        -8.54101         5.71287        -1.49505         0.13490\n",
      "            3_HIS_ct         6.57778        16.36021         0.40206         0.68764\n",
      " _Global_W_YPLL_rate         0.01340         0.00869         1.54181         0.12312\n",
      "------------------------------------------------------------------------------------\n",
      "Instrumented: _Global_W_YPLL_rate\n",
      "Instruments: 0_W_Blk14P, 0_W_HIS_ct, 0_W_Hisp14P, 1_W_Blk14P, 1_W_HIS_ct,\n",
      "             1_W_Hisp14P, 2_W_Blk14P, 2_W_HIS_ct, 2_W_Hisp14P, 3_W_Blk14P,\n",
      "             3_W_HIS_ct, 3_W_Hisp14P\n",
      "Regimes variable: Skater_reg\n",
      "\n",
      "REGIMES DIAGNOSTICS\n",
      "Overall SSR         : 2707576366.70\n",
      "Number of clusters  :             4\n",
      "\n",
      "- CHOW TEST -\n",
      "                 VARIABLE        DF        VALUE           PROB\n",
      "            CONSTANT              3         52.748           0.0000\n",
      "              Blk14P              3          4.746           0.1913\n",
      "             Hisp14P              3          8.776           0.0324\n",
      "              HIS_ct              3         37.435           0.0000\n",
      "         Global test             12        117.504           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "TEST                              DF         VALUE           PROB\n",
      "Anselin-Kelejian Test             1          0.273           0.6012\n",
      "\n",
      "SPATIAL LAG MODEL IMPACTS\n",
      "Impacts computed using the 'simple' method.\n",
      "            Variable         Direct        Indirect          Total\n",
      "            0_Blk14P        25.8791          0.3514         26.2305\n",
      "           0_Hisp14P       -33.1630         -0.4503        -33.6133\n",
      "            0_HIS_ct        88.5032          1.2017         89.7050\n",
      "            1_Blk14P        32.2044          0.4373         32.6417\n",
      "           1_Hisp14P         1.2522          0.0170          1.2692\n",
      "            1_HIS_ct       -38.5306         -0.5232        -39.0537\n",
      "            2_Blk14P        37.4610          0.5087         37.9697\n",
      "           2_Hisp14P        -2.9273         -0.0397         -2.9670\n",
      "            2_HIS_ct        79.1581          1.0748         80.2330\n",
      "            3_Blk14P        58.8397          0.7989         59.6387\n",
      "           3_Hisp14P        -8.5410         -0.1160         -8.6570\n",
      "            3_HIS_ct         6.5778          0.0893          6.6671\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "print(lag1.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e28a05",
   "metadata": {},
   "source": [
    "As was to be expected, the spatial autoregressive coefficient is not significant. The other results are interpreted in the standard way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655cb32b",
   "metadata": {},
   "source": [
    "## Practice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5692c515",
   "metadata": {},
   "source": [
    "Consider the optimal regimes allocation for different spatial weights, i.e., other than queen contiguity. As an exercise, construct an elbow plot from the information in `_trace` and compare your visual inspection to the built-in decision rule. In addition, experiment with the various regime-specific settings, letting some coefficients be fixed and others vary."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}