Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?

Domenico Giannone, Lucrezia Reichlin and Christine De Mol

No 700, Working Paper Series from European Central Bank

Abstract: 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. JEL Classification: C11, C13, C33, C53

Keywords: Bayesian VAR; large cross-sections; lasso regression; principal components; ridge regression (search for similar items in EconPapers)
Date: 2006-12
Note: 93468
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (48)

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Related works:
Working Paper: Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components? (2006)
Working Paper: Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:2006700

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