Probit models with spatial dependencies were first studied by McMillen (1992), and he proposed an EM algorithm to produce consistent maximum likelihood estimates for the model. In SAR probit model, the spatial dependence structure adds complexity to the estimation of parameters. The main assumption of the model is that the distribution of errors is known and is often assumed to be normal. Parameter estimation using a full maximum likelihood method is problematic because the likelihood function involves n integrals, where n is the sample size. To avoid the direct calculation of ndimensional integration, several estimators have been proposed that can produce consistent estimates when **data** are spatially autocorrelated and heteroscedastic (e.g., Beron Pinkse and Slade (1998) proposed estimators for the parameter of SAR probit model who becomes infeasible for large samples because they require the inversion of × matrices. LeSage (2000) used Bayesian estimates through the Markov Chain Monte Carlo and Gibbs sampling, which sampled sequentially from the complete conditional distribution for all parameters. Klier and McMillen (2008) have proposed a linearized version of the GMM estimator that avoids the infeasible problem of inverting × matrices when employing large samples and show that standard GMM reduces to a nonlinear two-stage least squares problem.