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

Christine De Mol, Domenico Giannone and Lucrezia Reichlin

Journal of Econometrics, 2008, vol. 146, issue 2, 318-328

Abstract: 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.

Keywords: Bayesian; shrinkage; Bayesian; VAR; Ridge; regression; Lasso; regression; Principal; components; Large; cross-sections (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (370)

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Related works:
Working Paper: Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components? (2006)
Working Paper: Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:146:y:2008:i:2:p:318-328

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