A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification

dc.contributor.authorLübke, Karstende
dc.contributor.authorWeihs, Clausde
dc.date.accessioned2004-12-06T18:39:19Z
dc.date.available2004-12-06T18:39:19Z
dc.date.issued2004de
dc.description.abstractIn this paper it is shown that the number of latent factors in a multiple multivariate regression model need not be larger than the number of the response variables in order to achieve an optimal prediction. The practical importance of this lemma is outlined and an application of such a projection on latent factors in a classification example is given.en
dc.format.extent123090 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2003/4900
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15078
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectlatent factor modelsen
dc.subjectprojection matrixen
dc.subjectregressionen
dc.subjectclassificationen
dc.subject.ddc310de
dc.titleA Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classificationen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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