a b s t r a c t
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.