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


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


Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):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.
Klassifikation Erhebungstechniken und Analysetechniken der Sozialwissenschaften; Volkswirtschaftslehre
Freie Schlagwörter Bayesian shrinkage; Bayesian VAR; Ridge regression; Lasso regression; Principal components; Large cross-sections
Sprache Dokument Englisch
Publikationsjahr 2008
Seitenangabe S. 318-328
Zeitschriftentitel Journal of Econometrics, 146 (2008) 2
DOI http://dx.doi.org/10.1016/j.jeconom.2008.08.011
Status Postprint; begutachtet (peer reviewed)
Lizenz PEER Licence Agreement (applicable only to documents from PEER project)