Dynamic spatial regimes for spatial panel data

Spatial heterogeneity in terms of spatially-varying coefficients is often not properly considered in modeling economic data. This neglect might cause serious problems in the estimation of the parameters of a model specification when group-wise heterogeneity is at work. In this paper we propose a two-step algorithm for the identification of endogenous (data-driven) spatial regimes by using an iterative procedure that is based on weighting functions updated dynamically over time. In the first step, clusters of spatial units (i.e. spatial regimes) are defined using both space and time information. In the second step, a spatial panel data model with random effects is estimated with the spatial regimes identified in the previous step. The additional random effects assumption on the model specification ensures the possibility of controlling also for individual effects as well as group-wise slope coefficients. The proposed method is applied to two real data sets to illustrate our procedure.