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

Claus Weihs and Karsten Luebke

No 2004,29, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

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.

Keywords: Latent Factor Models; Projection Matrix; Regression; Classification (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (2)

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