Fast Computation of the Deviance Information Criterion for Latent Variable Models

Joshua Chan and Angelia Grant

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: The deviance information criterion (DIC) has been widely used for Bayesian model comparison. However, recent studies have cautioned against the use of the DIC for comparing latent variable models. In particular, the DIC calculated using the conditional likelihood (obtained by conditioning on the latent variables) is found to be inappropriate, whereas the DIC computed using the integrated likelihood (obtained by integrating out the latent variables) seems to perform well. In view of this, we propose fast algorithms for computing the DIC based on the integrated likelihood for a variety of highdimensional latent variable models. Through three empirical applications we show that the DICs based on the integrated likelihoods have much smaller numerical standard errors compared to the DICs based on the conditional likelihoods.

Keywords: Bayesian model comparison; state space; factor model; vector autoregression; semiparametric (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C52 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2014-01
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-sog
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Journal Article: Fast computation of the deviance information criterion for latent variable models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2014-09

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