Bayesian Averaging, Prediction and Nonnested Model Selection
Han Hong, Bruce Preston
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
Year of publication: |
August 2008
|
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Authors: | Hong, Han |
Other Persons: | Preston, Bruce (contributor) |
Institutions: | National Bureau of Economic Research (contributor) |
Publisher: |
Cambridge, Mass : National Bureau of Economic Research |
Subject: | Bayes-Statistik | Bayesian inference | Prognoseverfahren | Forecasting model | Modellierung | Scientific modelling | Schätztheorie | Estimation theory |
Saved in:
freely available
Extent: | 1 Online-Ressource |
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Series: | NBER working paper series ; no. w14284 |
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Mode of access: World Wide Web System requirements: Adobe [Acrobat] Reader required for PDF files Hardcopy version available to institutional subscribers. |
Other identifiers: | 10.3386/w14284 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012464362
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