Benchmark Priors Revisited On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging
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...
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| Format: | eBook |
| Language: | English |
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Washington, D.C.
International Monetary Fund
2009
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| Series: | IMF Working Papers
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| Collection: | International Monetary Fund - Collection details see MPG.ReNa |
| 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 |
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| Physical Description: | 39 pages |
| ISBN: | 9781451873498 |