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

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dc.creator De Mol, Christine
dc.creator Giannone, Domenico
dc.creator Reichlin, Lucrezia
dc.date 2006
dc.date.accessioned 2013-10-16T07:06:10Z
dc.date.available 2013-10-16T07:06:10Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/19661
dc.identifier ppn:519034066
dc.identifier RePEc:zbw:bubdp1:5040
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/19661
dc.description 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.
dc.language eng
dc.relation Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2006,32
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject C33
dc.subject C13
dc.subject C53
dc.subject C11
dc.subject ddc:330
dc.subject Bayesian VAR
dc.subject ridge regression
dc.subject Lasso regression
dc.subject principal components
dc.subject large cross-sections
dc.subject Prognoseverfahren
dc.subject Zeitreihenanalyse
dc.subject Regression
dc.subject Bayes-Statistik
dc.subject VAR-Modell
dc.subject Theorie
dc.subject Hauptkomponentenregression
dc.title Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?
dc.type doc-type:workingPaper


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