Spatial econometric panel data model specification: A Bayesian approach

Taking a Bayesian perspective on model uncertainty for static panel data models proposed in the spatial econometrics literature considerably simplifies the task of selecting an appropriate model. A wide variety of alternative specifications that include various combinations of spatial dependence in lagged values of the dependent variable, spatial lags of the explanatory variables, as well as dependence in the model disturbances have been the focus of a literature on various statistical tests for distinguishing between these numerous specifications. A Bayesian model uncertainty argument is advanced that logically implies we can simplify this task by focusing on only two model specifications. One of these, labeled the spatial Durbin model (SDM) implies global spatial spillovers, while the second, labeled a spatial Durbin error model (SDEM) leads to local spatial spillovers. A Bayesian approach to determining an appropriate local or global specification, SDEM versus SDM is set forth here for static panel variants of these two models. The logic of the Bayesian view of model uncertainty suggests these are the only two specifications that need to be considered. This greatly simplifies the task confronting practitioners when using static panel data models. (C) 2014 Elsevier B.V. All rights reserved.