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
    ---><---

    References listed on IDEAS

    as
    1. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    2. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    3. Yoichi Miyata, 2004. "Fully Exponential Laplace Approximations Using Asymptotic Modes," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1037-1049, December.
    4. Vignat, C., 2012. "A generalized Isserlis theorem for location mixtures of Gaussian random vectors," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 67-71.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gholamreza Hajargasht & William E. Griffiths, 2018. "Estimation and testing of stochastic frontier models using variational Bayes," Journal of Productivity Analysis, Springer, vol. 50(1), pages 1-24, October.
    2. Gholamreza Hajargasht, 2015. "Stochastic frontiers with a Rayleigh distribution," Journal of Productivity Analysis, Springer, vol. 44(2), pages 199-208, October.
    3. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
    4. Michael S. Rimler & Seongho Song & David T. Yi, 2010. "Estimating Production Efficiency in Men’s NCAA College Basketball: A Bayesian Approach," Journal of Sports Economics, , vol. 11(3), pages 287-315, June.
    5. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    6. repec:cte:wsrepe:ws121007 is not listed on IDEAS
    7. Laura Di Giorgio & Abraham D Flaxman & Mark W Moses & Nancy Fullman & Michael Hanlon & Ruben O Conner & Alexandra Wollum & Christopher J L Murray, 2016. "Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    8. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    9. Juan Martín & Concepción Román & Augusto Voltes-Dorta, 2009. "A stochastic frontier analysis to estimate the relative efficiency of Spanish airports," Journal of Productivity Analysis, Springer, vol. 31(3), pages 163-176, June.
    10. David H. Bernstein & Christopher F. Parmeter, 2017. "Returns to Scale in Electricity Generation: Revisited and Replicated," Working Papers 2017-08, University of Miami, Department of Economics.
    11. Stephen M. Miller & Terrence M. Clauretie & Thomas M. Springer, 2006. "Economies Of Scale And Cost Efficiencies: A Panel‐Data Stochastic‐Frontier Analysis Of Real Estate Investment Trusts," Manchester School, University of Manchester, vol. 74(4), pages 483-499, July.
    12. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    13. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    14. Karlaftis, Matthew G. & Tsamboulas, Dimitrios, 2012. "Efficiency measurement in public transport: Are findings specification sensitive?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 392-402.
    15. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    16. Deng, Yaguo, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Sandrine Kablan & Ouidad Yousfi, 2015. "Performance of Islamic Banks across the World: An Empirical Analysis over the Period 2001-2008," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(1), pages 27-46.
    18. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Department of Economics - Working Papers Series 1092, The University of Melbourne.
    19. J. Cummins & Hongmin Zi, 1998. "Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry," Journal of Productivity Analysis, Springer, vol. 10(2), pages 131-152, October.
    20. Lavado, Rouselle F. & Barrios, Erniel B., 2010. "Spatial Stochastic Frontier Models," Discussion Papers DP 2010-08, Philippine Institute for Development Studies.
    21. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.