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Robust Deviance Information Criterion for Latent Variable Models

  • Yong Li

    (Hanqing Advanced Institute of Economics and Finance, Renmin University of China)

  • Tao Zeng

    (School of Economics and Sim Kee Boon Institute for Financial Economics, Singapore Management University)

  • Jun Yu


    (Sim Kee Boon Institute for Financial Economics, School of Economics and Lee Kong Chian School of Business)

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 difficult 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 specification. The proposed approach is illustrated using several popular models in economics and finance.

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Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 30-2012.

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Length: 44 pages
Date of creation: Aug 2012
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:30-2012
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