IDEAS home Printed from https://ideas.repec.org/p/boa/wpaper/202415.html
   My bibliography  Save this paper

Deviance Information Criterion for Model Selection:Theoretical Justification and Applications

Author

Listed:
  • Yong Li

    (Renmin University of China)

  • Sushanta K. Mallick

    (Queen Mary University of London)

  • Nianling Wang

    (Capital University of Economics and Business)

  • Jun Yu

    (University of Macau)

  • Tao Zeng

    (Zhejiang University)

Abstract

This paper gives a rigorous justification to the Deviance information criterion (DIC), which has been extensively used for model selection based on MCMC output. It is shown that, when a plug-in predictive distribution is used and under a set of regularity conditions, DIC is an asymptotically unbiased estimator of the expected Kullback-Leibler divergence between the data generating process and the plug-in predictive distribution. High-order expansions to DIC and the effective number of parameters are developed, facilitating investigating the effect of the prior. DIC is used to compare alternative discrete-choice models, stochastic frontier models, and copula models in three empirical applications.

Suggested Citation

  • Yong Li & Sushanta K. Mallick & Nianling Wang & Jun Yu & Tao Zeng, 2024. "Deviance Information Criterion for Model Selection:Theoretical Justification and Applications," Working Papers 202415, University of Macau, Faculty of Business Administration.
    • Handle: RePEc:boa:wpaper:202415
    as

    Download full text from publisher

    File URL: https://fba.um.edu.mo/wp-content/uploads/RePEc/doc/202415.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    AIC; DIC; Expected loss function; Kullback-Leibler divergence; Model comparison; Plug-in predictive distribution;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boa:wpaper:202415. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Carla Leong (email available below). General contact details of provider: https://edirc.repec.org/data/fbmacmo.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.