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Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containingheteroskedasticityusing the maximum likelihood estimation (MLE) method provides biased and inconsistent. The alternative method is the generalized method of moments (GMM). GMM uses a combination of linear and quadratic moment functions simultaneously so that the computation is easier than MLE. The bias is used to evaluate the GMM in estimating parameters of SAR model with heteroskedasticity disturbances in simulation data. The results show that GMM provides the bias of parameter estimates relatively consistent and smaller compared to the MLE method.

Comparison of Maximum Likelihood and Generalized Method of Moments in Spatial Autoregressive Model with Heteroskedasticity