Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?

Lucrezia Reichlin, Domenico Giannone and Christine De Mol

No 5829, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section.

Keywords: Bayesian var; Ridge regressions; Lasso regression; Principal components; Large cross-sections (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
Date: 2006-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (58)

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Related works:
Journal Article: Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components? (2008)
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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