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Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?

  • De Mol, Christine
  • Giannone, Domenico
  • Reichlin, Lucrezia

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study conditions for consistency of the forecast based on Bayesian regression as the cross-section and the sample size become large. This analysis serves as a guide to establish a criterion for setting the amount of shrinkage in a large cross-section.

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File URL: http://www.sciencedirect.com/science/article/B6VC0-4T9VPBS-6/2/724d2c79a50fb64b9ad88148f2f95df6
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 146 (2008)
Issue (Month): 2 (October)
Pages: 318-328

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Handle: RePEc:eee:econom:v:146:y:2008:i:2:p:318-328
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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