TY  - CHAP
AB  - 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.
VL  - 18
SN  - 978-0-76231-148-4, 978-1-84950-301-3/0731-9053
DO  - 10.1016/S0731-9053(04)18003-1
UR  - https://doi.org/10.1016/S0731-9053(04)18003-1
AU  - Hepple Leslie W.
ED  - James P. Lesage
ED  - R. Kelley Pace
PY  - 2004
Y1  - 2004/01/01
TI  - BAYESIAN MODEL CHOICE IN SPATIAL ECONOMETRICS
T2  - Spatial and Spatiotemporal Econometrics
T3  - Advances in Econometrics
PB  - Emerald Group Publishing Limited
SP  - 101
EP  - 126
Y2  - 2024/09/22
ER  -