Abstract: This paper reviews a number of theoretical aspects
for implementing an explicit spatial perspective in econometrics
for modelling non-continuous data, in general, and count data, in
particular. It provides an overview of the several spatial econometric
approaches that are available to model data that are collected with
reference to location in space, from the classical spatial econometrics
approaches to the recent developments on spatial econometrics to
model count data, in a Bayesian hierarchical setting. Considerable
attention is paid to the inferential framework, necessary for
structural consistent spatial econometric count models, incorporating
spatial lag autocorrelation, to the corresponding estimation and
testing procedures for different assumptions, to the constrains and
implications embedded in the various specifications in the literature. This review combines insights from the classical spatial
econometrics literature as well as from hierarchical modeling and
analysis of spatial data, in order to look for new possible directions
on the processing of count data, in a spatial hierarchical Bayesian
econometric context.