A Comparison between Maximum Likelihood and Bayesian Approaches for Estimating the Parameters of Three Spatial Econometrics Models

Sometimes, in econometrics problems the observations are not independent, so that their dependence is due to the location of observations in the studied space. To analyze these data are used the spatial regression models. Due to the large number of parameters in these models are used the iteration algorithms to obtain the maximum likelihood estimations, so that it encounters problems such as the complexity of the calculation. In addition in economic studies, the number of observation is large and it seems useful to use the Bayesian approach. The main purpose is using the Bayesian and the Likelihood approaches to estimate the parameters of the three spatial econometric models. Then, comparing the performance of these two approaches, as well as comparing the performance of these three spatial regression models and finally the three models are implemented on two real data. It is observed that the results of the Bayesian approach are more credible than the likelihood approach in these type of econometric models.