Bayesian model choice in spatial econometrics

LW Hepple

Research output: Chapter in Book/Report/Conference proceedingChapter in a report

14 Citations (Scopus)


Within spatial econometrics a whole family of different spatial specifications has been developed, with associated estimators and tests. This lead to issues of model comparison and model choice, measuring the relative merits of alternative specifications and then using appropriate criteria to choose the “best” model or relative model probabilities. Bayesian theory provides a comprehensive and coherent framework for such model choice, including both nested and non-nested models within the choice set. The paper reviews the potential application of this Bayesian theory to spatial econometric models, examining the conditions and assumptions under which application is possible. Problems of prior distributions are outlined, and Bayes factors and marginal likelihoods are derived for a particular subset of spatial econometric specifications. These are then applied to two well-known spatial data-sets to illustrate the methods. Future possibilities, and comparisons with other approaches to both Bayesian and non-Bayesian model choice are discussed.
Translated title of the contributionBayesian model choice in spatial econometrics
Original languageEnglish
Title of host publicationSpatial and Spatiotemporal Econometrics
EditorsJames P Lesage, R Kelley Pace
Number of pages26
ISBN (Electronic)9781849503013
ISBN (Print)9780762311484
Publication statusPublished - 2004

Publication series

NameAdvances in Econometrics
ISSN (Print)0731-9053

Bibliographical note

Publisher: Elsevier


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