Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging
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Summary:
Default prior choices fixing Zellner's g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts posterior model distributions to data quality. Analytically, existing work on the hyper-g-prior is complemented by posterior expressions essential to fully Bayesian analysis and to sound numerical implementation. A simulation experiment illustrates the implications for posterior inference. Furthermore, an application to determinants of economic growth identifies several covariates whose robustness differs considerably from previous results.
Series:
Working Paper No. 2009/202
Subject:
Bayesian models Data processing Human capital Inflation Public investment and public-private partnerships (PPP)
English
Publication Date:
September 1, 2009
ISBN/ISSN:
9781451873498/1018-5941
Stock No:
WPIEA2009202
Pages:
39
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