ABSTRACT
Spatial econometric models are a suite of likelihood-based models that adapt the standard normal linear regression model in order to address two of the fundamental challenges associated with spatial data, namely spatial dependence and spatial heterogeneity, and their implications for model specification, parameter estimation and hypothesis testing. Spatial econometric models pay particular attention to assessing spatial spillover and associated spatial feedback effects. The chapter describes the spatial lag model (SLM), the spatially-lagged covariates model (SLX), the spatial error model (SEM) and the spatial Durbin model (SDM). An application demonstrates the form(s) of spatial spillover each model captures and discusses issues associated with the interpretation of the covariate effects on outcomes distinguishing between direct and indirect (or exogenous) effects of a covariate on the outcome. Computational issues that arise from fitting some of the spatial econometric models to observed data are described. Finally, we compare this group of spatial econometric models with the hierarchical models discussed in Chapters 7, 8 and 9.
