Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/19661
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dc.creatorDe Mol, Christine-
dc.creatorGiannone, Domenico-
dc.creatorReichlin, Lucrezia-
dc.date2006-
dc.date.accessioned2013-10-16T07:06:10Z-
dc.date.available2013-10-16T07:06:10Z-
dc.date.issued2013-10-16-
dc.identifierhttp://hdl.handle.net/10419/19661-
dc.identifierppn:519034066-
dc.identifierRePEc:zbw:bubdp1:5040-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/10419/19661-
dc.descriptionThis 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.languageeng-
dc.relationDiscussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2006,32-
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.subjectC33-
dc.subjectC13-
dc.subjectC53-
dc.subjectC11-
dc.subjectddc:330-
dc.subjectBayesian VAR-
dc.subjectridge regression-
dc.subjectLasso regression-
dc.subjectprincipal components-
dc.subjectlarge cross-sections-
dc.subjectPrognoseverfahren-
dc.subjectZeitreihenanalyse-
dc.subjectRegression-
dc.subjectBayes-Statistik-
dc.subjectVAR-Modell-
dc.subjectTheorie-
dc.subjectHauptkomponentenregression-
dc.titleForecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?-
dc.typedoc-type:workingPaper-
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