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Intrinsic Priors for Objective Bayesian Model Selection

In: Bayesian Model Comparison

Author

Listed:
  • Elías Moreno
  • Luís Raúl Pericchi

Abstract

We put forward the idea that for model selection the intrinsic priors are becoming a center of a cluster of a dominant group of methodologies for objective Bayesian Model Selection. The intrinsic method and its applications have been developed in the last two decades, and has stimulated closely related methods. The intrinsic methodology can be thought of as the long searched approach for objective Bayesian model selection and hypothesis testing. In this paper we review the foundations of the intrinsic priors, their general properties, and some of their applications.

Suggested Citation

  • Elías Moreno & Luís Raúl Pericchi, 2014. "Intrinsic Priors for Objective Bayesian Model Selection," Advances in Econometrics, in: Bayesian Model Comparison, volume 34, pages 279-300, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320140000034012
    DOI: 10.1108/S0731-905320140000034012
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    More about this item

    Keywords

    g-prior; intrinsic priors; model selection; sampling properties of Bayes factors; C11; C12; C18;
    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
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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