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

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  • 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.

Suggested Citation

  • Martin Feldkircher & Stefan Zeugner, 2009. "Benchmark Priors Revisited; On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging," IMF Working Papers 09/202, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:09/202
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