On the Choice of Prior in Bayesian Model Averaging
CentER Working Paper No. 2011-003
29 Pages Posted: 31 May 2011 Last revised: 26 Apr 2017
Date Written: January 21, 2011
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.
Keywords: Model averaging, Bayesian analysis, Subbotin prior
JEL Classification: C11, C51, C52
Suggested Citation: Suggested Citation