Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153134
Authors: 
De Mol, Christine
Giannone, Domenico
Reichlin, Lucrezia
Year of Publication: 
2006
Series/Report no.: 
ECB Working Paper 700
Abstract: 
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.
Subjects: 
Bayesian VAR
large cross-sections
Lasso regression
principal components
ridge regression
JEL: 
C11
C13
C33
C53
Document Type: 
Working Paper

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