A comparison of spatial error models through Monte Carlo experiments

A spatial error model is classified as a geostatistical model or a weight matrix model on the basis of the method of specification of spatial autocorrelation in the disturbance. Specification errors cannot be assumed to be absent, and the robustness of alternative specifications is useful for dealing with potential errors. Previous studies compared several models to arrive at two basic conclusions: (i) all of the models maintain reasonable estimation accuracy, and (ii) the two types of models have well-matched predictive abilities. The present study makes a supplementary comparison to investigate whether these conclusions are true for a broader range of models. Also, implications of our results for the model choice are explored.