Bayesian Averaging, Prediction and Nonnested Model Selection

Han Hong and Bruce Preston

No 14284, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper studies the asymptotic relationship between Bayesian model averaging and post-selection frequentist predictors in both nested and nonnested models. We derive conditions under which their difference is of a smaller order of magnitude than the inverse of the square root of the sample size in large samples. This result depends crucially on the relation between posterior odds and frequentist model selection criteria. Weak conditions are given under which consistent model selection is feasible, regardless of whether models are nested or nonnested and regardless of whether models are correctly specified or not, in the sense that they select the best model with the least number of parameters with probability converging to 1. Under these conditions, Bayesian posterior odds and BICs are consistent for selecting among nested models, but are not consistent for selecting among nonnested models.

JEL-codes: C14 C52 (search for similar items in EconPapers)
Date: 2008-08
New Economics Papers: this item is included in nep-ecm
Note: TWP
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Citations: View citations in EconPapers (6)

Published as Hong, Han & Preston, Bruce, 2012. "Bayesian averaging, prediction and nonnested model selection," Journal of Econometrics, Elsevier, vol. 167(2), pages 358-369.

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Journal Article: Bayesian averaging, prediction and nonnested model selection (2012)
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