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

September 1, 2009

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Martin Feldkircher, and Stefan Zeugner. Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging, (USA: International Monetary Fund, 2009) accessed September 19, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

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.

Subject: Bayesian models, Data processing, Human capital, Inflation, Public investment and public-private partnerships (PPP)

Keywords: Expected value, WP

Publication Details

Pages:

39
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2009/202
Stock No:

WPIEA2009202
ISBN:

9781451873498
ISSN:

1018-5941

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