Deviance Information Criterion for Model Selection:Theoretical Justification and Applications

Yong Li, Mallick Sushanta K (), Nianling Wang, Jun Yu and Tao Zeng ()
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Yong Li: Renmin University of China
Mallick Sushanta K: Queen Mary University of London
Nianling Wang: Capital University of Economics and Business
Tao Zeng: Zhejiang University

No 202415, Working Papers from University of Macau, Faculty of Business Administration

Abstract: This paper gives a rigorous justification to the Deviance information criterion (DIC), which has been extensively used for model selection based on MCMC output. It is shown that, when a plug-in predictive distribution is used and under a set of regularity conditions, DIC is an asymptotically unbiased estimator of the expected Kullback-Leibler divergence between the data generating process and the plug-in predictive distribution. High-order expansions to DIC and the effective number of parameters are developed, facilitating investigating the effect of the prior. DIC is used to compare alternative discrete-choice models, stochastic frontier models, and copula models in three empirical applications.

Keywords: AIC; DIC; Expected loss function; Kullback-Leibler divergence; Model comparison; Plug-in predictive distribution (search for similar items in EconPapers)
JEL-codes: C11 C22 C25 C32 C52 (search for similar items in EconPapers)
Pages: 92 pages
Date: 2024-08
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Published in UM-FBA Working Paper Series

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