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On the Choice of Prior in Bayesian Model Averaging

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Author Info

  • Einmahl, J.H.J.
  • Magnus, J.R.
  • Kumar, K.

    (Tilburg University, Center for Economic Research)

Abstract

Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable’ prior as one which leads to a continuous and suitable analog to the pretest estimator. The normal prior, used in standard Bayesian model averaging, is shown to be unsuitable. The Laplace (or lasso) prior is almost suitable. A suitable prior (the Subbotin prior) is proposed and its properties are investigated.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2011-003.

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Date of creation: 2011
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Handle: RePEc:dgr:kubcen:2011003

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Web page: http://center.uvt.nl

Related research

Keywords: Model averaging; Bayesian analysis; Subbotin prior;

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Cited by:
  1. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.

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