Utilities

def get_lags(w, x, w_lags):
    """
    Generate spatial lags of variables.
    
    Parameters:
    - w: Spatial weights object
    - x: Variable to lag
    - w_lags: Number of lags to generate
    
    Returns:
    - array: Spatially lagged variables
    """

def get_spFilter(w, lamb, sf):
    """
    Apply spatial filtering transformation: (I - λW)x.
    
    Parameters:
    - w: Spatial weights object
    - lamb: Spatial parameter (scalar)
    - sf: Variable to filter
    
    Returns:
    - array: Spatially filtered variable
    """

def get_lags_split(w, x, max_lags, split_at):
    """Generate spatial lags with split functionality."""

def optim_moments(moments_in, vcX=np.array([0]), all_par=False, start=None, hard_bound=False):
    """
    Optimize GMM moments estimation.
    
    Parameters:
    - moments_in: Moment conditions function
    - vcX: Variance-covariance matrix
    - all_par: Boolean, return all parameters if True
    - start: Starting values for optimization
    - hard_bound: Boolean, enforce parameter bounds
    
    Returns:
    - array: Optimized parameters
    """

def get_A1_het(S):
    """Build A1 matrix for heteroskedastic GMM estimation."""

def get_A1_hom(s, scalarKP=False):
    """Build A1 matrix for homoskedastic GMM estimation."""

def get_A2_hom(s):
    """Build A2 matrix for homoskedastic GMM estimation."""

def inverse_prod(w, data, scalar, post_multiply=False, inv_method='power_exp', threshold=0.99999999, max_iter=100):
    """Compute inverse matrix product for spatial filtering."""

def power_expansion(w, data, scalar, post_multiply=False, threshold=0.99999999, max_iter=100):
    """Power expansion approximation for matrix inverse."""

class RegressionPropsY:
    """
    Helper class providing regression properties for dependent variable.
    
    Properties:
    - mean_y: float, mean of dependent variable
    - std_y: float, standard deviation of dependent variable
    - sig2n: float, sigma-squared (n denominator)
    - sig2n_k: float, sigma-squared (n-k denominator)
    """

class RegressionPropsVM:
    """
    Helper class providing variance-covariance matrix properties.
    
    Properties:
    - vm: array, variance-covariance matrix
    - std_err: array, standard errors
    - z_stat: array, z-statistics
    - p_values: array, p-values
    """

import numpy as np
import spreg
from libpysal import weights

# Create spatial weights
n = 100
coords = np.random.randn(n, 2)
w = weights.KNN.from_array(coords, k=5)

# Variable to lag
x = np.random.randn(n, 1)

# Multiple lags (note: lag_spatial is from libpysal, not spreg)
from libpysal.weights.spatial_lag import lag_spatial
wx = lag_spatial(w, x)
print(f"Original variable shape: {x.shape}")
print(f"Spatial lag shape: {wx.shape}")

# Generate lags using spreg utilities
x_multi = np.random.randn(n, 3)
lags = spreg.get_lags(w, x_multi, w_lags=2)
print(f"Multiple spatial lags shape: {lags.shape}")

import numpy as np
import spreg
from libpysal import weights

# Example: Set up endogenous variables for spatial model
n = 100
y = np.random.randn(n, 1)
x = np.random.randn(n, 2)
yend = np.random.randn(n, 1)  # endogenous variable
q = np.random.randn(n, 1)     # instrument
w = weights.lat2W(10, 10)

# Set up for spatial estimation
y_setup, x_setup, yend_setup, q_setup = spreg.set_endog(
    y, x, w, yend, q, w_lags=1, lag_q=True
)

print("Data setup for spatial GMM estimation completed")
print(f"Processed shapes: y={y_setup.shape}, x={x_setup.shape}")