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

[journal article]

Mol, Christine de; Giannone, Domenico; Reichlin, Lucrezia

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Please use the following Persistent Identifier (PID) to cite this document:http://nbn-resolving.de/urn:nbn:de:0168-ssoar-198289

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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 conditions for consistency of the forecast based on Bayesian regression as the cross-section and the sample size become large. This analysis serves as a guide to establish a criterion for setting the amount of shrinkage in a large cross-section.
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods; Political Economy
Free Keywords Bayesian shrinkage; Bayesian VAR; Ridge regression; Lasso regression; Principal components; Large cross-sections
Document language English
Publication Year 2008
Page/Pages p. 318-328
Journal Journal of Econometrics, 146 (2008) 2
DOI http://dx.doi.org/10.1016/j.jeconom.2008.08.011
Status Postprint; reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)