Fernandez, Carmen and Ley, Eduardo and Steel, Mark F.J. (2001) Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100 (2). pp. 381-427. ISSN 0304-4076. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
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 non-informative 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 g(0j). The consequences of different choices for g(0j) 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 g(0j) in a simulation study. The use of the MC3 algorithm of Madigan and York (Int. Stat. Rev. 63 (1995) 215), 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. (C) 2001 Elsevier Science S.A. All rights reserved. JEL classification: C11; C15.
|Subjects:||Q Science > QA Mathematics (inc Computing science)|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Judith Broom|
|Date Deposited:||11 Oct 2008 21:37|
|Last Modified:||28 Apr 2014 15:07|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/7930 (The current URI for this page, for reference purposes)|