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Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging

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Author Info

  • Martin Feldkircher
  • Stefan Zeugner

Abstract

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.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 09/202.

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Length: 39
Date of creation: 01 Sep 2009
Date of revision:
Handle: RePEc:imf:imfwpa:09/202

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Related research

Keywords: Data analysis; Economic growth; Economic models;

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