Parametric Models in Spatial Econometrics: A Survey - Mendes, D. A.; Mendes, V. (Complexity & Geographical Economics: Topics and Tools, 2014)
The main purpose of this chapter is to review the parametric spatial econometric models that can be applied to regional economics. Spatial econometric methods are based on regression analysis applied to cases where spatial interactions and spatial structures are fundamental characteristics of the process under discussion. The review presented here outlines the basic terminology, the spatial data dependence, the specification of spatial effects, and some basic spatial regression models, i.e., the spatial autoregressive (SAR) model (or spatial lag model), the spatial error model (SEM), the spatial Durbin model (SDM) and the general spatial models—SAC and SARMA. The maximum likelihood estimation for SAR and SEM models it is also presented with some detail.In the context of the European Union, we should emphasize several empirical works in the particular areas of urban economics, economic growth and productivity, and studies dealing with agglomeration and externalities (spillovers). We provide a brief survey of some of the results obtained in these particular areas.