DP5829 Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?
|Author(s):||Christine De Mol, Domenico Giannone, Lucrezia Reichlin|
|Publication Date:||September 2006|
|Keyword(s):||Bayesian VAR, large cross-sections, Lasso regression, principal components, ridge regressions|
|JEL(s):||C11, C13, C33, C53|
|Programme Areas:||International Macroeconomics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=5829|
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 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.