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

by Christine De Mol, Domenico Giannone and Lucrezia Reichlin

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

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Year of publication:	2006
Other Persons:	De Mol, Christine (contributor) ; Giannone, Domenico (contributor) ; Reichlin, Lucrezia (contributor)
Publisher:	Frankfurt am Main : European Central Bank
Subject:	Hauptkomponentenregression \| Prognoseverfahren \| Forecasting model \| Zeitreihenanalyse \| Time series analysis \| Regressionsanalyse \| Regression analysis \| Bayes-Statistik \| Bayesian inference \| VAR-Modell \| VAR model \| Theorie \| Theory