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

dc.contributor.author	Lübke, Karsten	de
dc.contributor.author	Weihs, Claus	de
dc.date.accessioned	2004-12-06T18:39:19Z
dc.date.available	2004-12-06T18:39:19Z
dc.date.issued	2004	de
dc.description.abstract	In 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.extent	123090 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/4900
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15078
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	latent factor models	en
dc.subject	projection matrix	en
dc.subject	regression	en
dc.subject	classification	en
dc.subject.ddc	310	de
dc.title	A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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