Publication Type
Working Paper
Version
publishedVersion
Publication Date
8-2012
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 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.
Keywords
AIC, DIC, EM Algorithm, Latent variable models, Markov Chain Monte Carlo
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
43
Publisher
SMU Economics and Statistics Working Paper Series, No. 30-2012
City or Country
Singapore
Citation
LI, Yong; ZENG, Tao; and YU, Jun.
Robust Deviance Information Criterion for Latent Variable Models. (2012). 1-43.
Available at: https://ink.library.smu.edu.sg/soe_research/1403
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.