Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?
大量预测变量下的预测：贝叶斯收缩能否替代主成分？
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…