Heterogeneous Coefficient Spatial Regression Panel Models

Abstract

Space-time panel data samples covering longer time spans are becoming increasingly prevalent, and some recent spatial econometrics research has proposed exploiting sample data along the time dimension to produce estimates for all spatial units or regions. The appeal of these models that have been labeled heterogeneous coefficient models should be clear, since each observation represents a spatial unit or region. Theoretical models that underpin econometric specifications often specify different utility or production functions for each economic agent, and urban and regional economic theories also focus on individual cities or regions. Typical panel regression models contain information on each observation over a number of different time periods in the case of a balanced panel model. Estimates from the typical model average over all observations and time periods, producing a coarse summary of relationships thought to derive from interaction between individual observations. In contrast, heterogeneous coefficient models produce separate estimates of the parameters of the model relationship for each observation.