Publication Type

Working Paper

Publication Date

2-2017

Abstract

Deviance information criterion (DIC) has beenextensively used for making Bayesian model selection. It is a Bayesian versionof AIC and chooses a model that gives the smallest expected Kullback-Leiblerdivergence between the data generating process (DGP) and a predictivedistribution asymptotically. We show that when the plug-in predictivedistribution is used, DIC can have a rigorous decision-theoretic justificationunder regularity conditions. An alternative expression for DIC, based on theBayesian predictive distribution, is proposed. The new DIC has a smallerpenalty term than the original DIC and is very easy to compute from the MCMCoutput. It is invariant to reparameterization and yields a smaller frequentistrisk than the original DIC asymptotically.

Keywords

AIC, DIC, Bayesian Predictive Distribution, Plug-in Predictive Distribution, Loss Function, Bayesian Model Comparison, Frequentist Risk

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

40

Publisher

Singapore Management University

City or Country

Singapore

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Included in

Econometrics Commons

Share

COinS