De Mol, Christine Giannone, Domenico Reichlin, Lucrezia
Year of Publication:
ECB Working Paper 700
This paper considers Bayesian regression with normal and doubleexponential 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.
Bayesian VAR large cross-sections Lasso regression principal components ridge regression