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Forecasting with a large number of predictors: Is Bayesian regression a valid alternative to principal components?


Journal of Econometrics



Authors / Editors

Reichlin L;De Mol C;Giannone D

Publication Year



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.


Bayesian shrinkage; Bayesian VAR; Ridge regression; Lasso regression; Principal components; Large cross-sections

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