Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances

The likelihood functions for spatial autoregressive models with normal but heteroskedastic disturbances have been derived [Anselin (1988, ch.6)], but there is no implementation of maximum likelihood estimation for these likelihood functions in general cases with heteroskedastic disturbances. Therefore, less efficient IV-based methods must be applied if disturbance terms might be heteroskedastic. In the present paper, we develop a new computer program for maximum likelihood estimation of heteroskedastic models that also utilizes multiple spatial weight matrices and confirm the efficiency of the obtained estimator in cases with heteroskedastic disturbances through Monte Carlo simulations