Regime Models

class GM_Error_Het_Regimes:
    def __init__(self, y, x, regimes, w, constant_regi='many', cols2regi='all',
                 regime_err_sep=False, regime_lag_sep=False, cores=False,
                 max_iter=1, epsilon=0.0000001, step1c=False, 
                 inv_method='power_exp', hard_bound=False, vm=False, 
                 name_y=None, name_x=None, name_regimes=None, name_w=None,
                 name_ds=None, latex=False):
        """
        GMM spatial error model with regimes and heteroskedasticity.

        Parameters:
        - y (array): nx1 dependent variable
        - x (array): nxk independent variables
        - regimes (list/Series): Regime identifier for each observation
        - w (sparse matrix): Spatial weights matrix
        - constant_regi (str): 'one' (same constant) or 'many' (different per regime)
        - cols2regi (str/list): Variables that vary by regime ('all' or list)
        - regime_err_sep (bool): Separate error variance by regime
        - regime_lag_sep (bool): Separate spatial parameter by regime
        - cores (bool): Use multiprocessing for regime estimation

        Attributes:
        - regimes_set (set): Unique regime values
        - kr (int): Number of regimized variables  
        - nr (int): Number of regimes
        - multi (dict): Individual regime results (if regime_err_sep=True)
        - chow (dict): Chow test for regime differences
        """

class GM_Error_Hom_Regimes:
    def __init__(self, y, x, regimes, w, constant_regi='many', cols2regi='all',
                 regime_err_sep=False, regime_lag_sep=False, cores=False,
                 hard_bound=False, vm=False, name_y=None, name_x=None,
                 name_regimes=None, name_w=None, name_ds=None, latex=False):
        """GMM spatial error model with regimes assuming homoskedasticity."""

import numpy as np
import spreg
from libpysal import weights

# Data with regimes
n = 150
x = np.random.randn(n, 2)
y = np.random.randn(n, 1)
regimes = np.random.choice(['North', 'South', 'East'], n)
w = weights.KNN.from_array(np.random.randn(n, 2), k=5)

# Spatial error model with regimes
regime_model = spreg.GM_Error_Het_Regimes(y, x, regimes, w.sparse,
                                          constant_regi='many', cols2regi='all',
                                          name_y='y', name_x=['x1', 'x2'],
                                          name_regimes='region')

print(regime_model.summary)
print(f"Number of regimes: {regime_model.nr}")
print("Chow test for regime differences:", regime_model.chow)