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Bayesian Model Averaging Using Power-Expected-Posterior Priors

Author

Listed:
  • Dimitris Fouskakis

    (Statistics Lab, Department of Mathematics, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece)

  • Ioannis Ntzoufras

    (Computational and Bayesian Statistics Lab, Department of Statistics, Athens University of Economics and Business, 10434 Athens, Greece)

Abstract

This paper focuses on the Bayesian model average (BMA) using the power–expected– posterior prior in objective Bayesian variable selection under normal linear models. We derive a BMA point estimate of a predicted value, and present computation and evaluation strategies of the prediction accuracy. We compare the performance of our method with that of similar approaches in a simulated and a real data example from economics.

Suggested Citation

  • Dimitris Fouskakis & Ioannis Ntzoufras, 2020. "Bayesian Model Averaging Using Power-Expected-Posterior Priors," Econometrics, MDPI, vol. 8(2), pages 1-15, May.
    • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:17-:d:356718
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    References listed on IDEAS

    as
    1. Andrew J. Womack & Luis León-Novelo & George Casella, 2014. "Inference From Intrinsic Bayes' Procedures Under Model Selection and Uncertainty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1040-1053, September.
    2. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    3. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
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