Robust Deviance Information Criterion for Latent Variable Models

Yong Li (), Zeng Tao Author Email:taozeng@smu.edu.sg and Jun Yu
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Zeng Tao Author Email:taozeng@smu.edu.sg: Singapore Management University

Working Papers from Singapore Management University, Sim Kee Boon Institute for Financial Economics

Abstract: It is shown in this paper that the data augmentation technique undermines the theoretical underpinnings of the deviance information criterion (DIC), a widely used information criterion for Bayesian model comparison, although it facilitates parameter estimation for latent variable models via Markov chain Monte Carlo (MCMC) simulation. Data augmentation makes the likelihood function non-regular and hence invalidates the standard asymptotic arguments. A new information criterion, robust DIC (RDIC), is proposed for Bayesian comparison of latent variable models. RDIC is shown to be a good approximation to DIC without data augmentation. While the later quantity is dicult to compute, the expectation { maximization (EM) algorithm facilitates the computation of RDIC when the MCMC output is available. Moreover, RDIC is robust to nonlinear transformations of latent variables and distributional representations of model speci cation. The proposed approach is illustrated using several popular models in economics and nance.

Keywords: AIC; DIC; EM Algorithm; Latent variable models; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C12 G12 (search for similar items in EconPapers)
Pages: 43 Pages
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Published in SMU-SKBI CoFie Working Paper

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Working Paper: Robust Deviance Information Criterion for Latent Variable Models (2012)
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