Benchmark Priors for Bayesian Model Averaging

Benchmark Priors for Bayesian Model Averaging (2001)

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by Carmen Fernández , Eduardo Ley , Mark F. J. Steel

Venue:	FORTHCOMING IN THE JOURNAL OF ECONOMETRICS
Citations:	61 - 3 self

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Datum	Value	Source
TITLE	Benchmark Priors for Bayesian Model Averaging	user correction
AUTHOR NAME	Carmen Fernández	user correction
AUTHOR AFFIL	School of Mathematics and Statistics, University of St. Andrews, St. Andrews KY16 9SS, U.K.	user correction
AUTHOR NAME	Eduardo Ley	user correction
AUTHOR AFFIL	International Monetary Fund	user correction
AUTHOR ADDR	700 19th St NW, Washington DC 20431, U.S.A.	user correction
AUTHOR NAME	Mark F. J. Steel	user correction
AUTHOR AFFIL	Institute of Mathematics and Statistics, University of Kent at Canterbury, Canterbury CT2 7NF, U.K.	user correction
ABSTRACT	In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, “diffuse” priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an “automatic” or “benchmark” prior structure that can be used in such cases. We focus on the Normal linear regression model with uncertainty in the choice of regressors. We propose a partly noninformative prior structure related to a Natural Conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g0j. The consequences of different choices for g0j are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. More importantly, we examine the finite sample implications of several choices of g0j in a simulation study. The use of the MC3 algorithm of Madigan and York (1995), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a “benchmark” prior specification in a linear regression context with model uncertainty.	user correction
YEAR	2001	user correction
VENUE	FORTHCOMING IN THE JOURNAL OF ECONOMETRICS	user correction
CITATIONS	62 found	ParsCit 1.0