A space–time filter for panel data models containing random effects

Abstract

A space–time filter structure is introduced that can be used to accommodate dependence across space and time in the error components of panel data models that contain random effects. This specification provides insights regarding several space–time structures that have been used recently in the panel data literature. Markov Chain Monte Carlo methods are set forth for estimating the model which allow simple treatment of initial period observations as endogenous or exogenous. The performance of the approach is demonstrated using both Monte Carlo experiments and an applied illustration.