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Deviance Information Criterion for Bayesian Model Selection: Justification and Variation

Author

Listed:
  • Li, Yong

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

  • Yu, Jun

    (School of Economics, Singapore Management University)

  • Zeng, Tao

    (Department of Finance, Wuhan University)

Abstract

Deviance information criterion (DIC) has been extensively used for making Bayesian model selection. It is a Bayesian version of AIC and chooses a model that gives the smallest expected Kullback-Leibler divergence between the data generating process (DGP) and a predictive distribution asymptotically. We show that when the plug-in predictive distribution is used, DIC can have a rigorous decision-theoretic justification under regularity conditions. An alternative expression for DIC, based on the Bayesian predictive distribution, is proposed. The new DIC has a smaller penalty term than the original DIC and is very easy to compute from the MCMC output. It is invariant to reparameterization and yields a smaller frequentist risk than the original DIC asymptotically.

Suggested Citation

  • Li, Yong & Yu, Jun & Zeng, Tao, 2017. "Deviance Information Criterion for Bayesian Model Selection: Justification and Variation," Economics and Statistics Working Papers 5-2017, Singapore Management University, School of Economics.
    • Handle: RePEc:ris:smuesw:2017_005
    Note: Paper available on: http://ink.library.smu.edu.sg/soe_research/1927
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    Citations

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    Cited by:

    1. Liu, Xiaobin & Li, Yong & Yu, Jun & Zeng, Tao, 2022. "Posterior-based Wald-type statistics for hypothesis testing," Journal of Econometrics, Elsevier, vol. 230(1), pages 83-113.
    2. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.

    More about this item

    Keywords

    AIC; DIC; Bayesian Predictive Distribution; Plug-in Predictive Distribution; Loss Function; Bayesian Model Comparison; Frequentist Risk;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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