Observed-data DIC for spatial panel data models

Ye Yang (), Osman Doğan () and Süleyman Taşpınar ()
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Ye Yang: Capital University of Economics and Business
Osman Doğan: Istanbul Technical University
Süleyman Taşpınar: Queens College CUNY

Empirical Economics, 2023, vol. 64, issue 3, No 9, 1314 pages

Abstract: Abstract In spatial panel data modeling, researchers often need to choose a spatial weights matrix from a pool of candidates, and decide between static and dynamic specifications. We propose observed-data deviance information criteria to resolve these specification problems in a Bayesian setting. The presence of high dimensional latent variables (i.e., the individual and time fixed effects) in spatial panel data models invalidates the use of a deviance information criterion (DIC) formulated with the conditional and the complete-data likelihood functions of spatial panel data models. We first show how to analytically integrate out these latent variables from the complete-data likelihood functions to obtain integrated likelihood functions. We then use the integrated likelihood functions to formulate observed-data DIC measures for both static and dynamic spatial panel data models. Our simulation analysis indicates that the observed-data DIC measures perform satisfactorily to resolve specification problems in spatial panel data modeling. We also illustrate the usefulness of the proposed observed-data DIC measures using an application from the literature on spatial modeling of the house price changes in the US.

Keywords: Spatial panel data models; Bayesian inference; MCMC; Deviance information criterion; DIC; Bayesian model comparison; Model selection (search for similar items in EconPapers)
JEL-codes: C13 C21 C31 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00181-022-02286-6

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