On the Choice of Prior in Bayesian Model Averaging

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

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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.
Original languageEnglish
Place of PublicationTILBURG
Number of pages29
Publication statusPublished - 2011

Publication series

NameCentER Discussion Paper


  • Model averaging
  • Bayesian analysis
  • Subbotin prior


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