Forecasting with a large number of predictors: Is Bayesian regression a valid alternative to principal components?
Journal
Journal of Econometrics
Subject
Economics
Publishing details
Authors / Editors
Reichlin L; De Mol C; Giannone D
Biographies
Publication Year
2008
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
Available on ECCH
No
