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

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.

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Year of publication:	2004
Authors:	Weihs, Claus ; Luebke, Karsten
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	Faktorenanalyse \| Multiple Regression \| Diskriminanzanalyse \| Konjunktur \| Theorie \| Latent Factor Models \| Projection Matrix \| Regression \| Classification

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Series:	Technical Report ; 2004,29
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	391704648 [GVK] hdl:10419/22541 [Handle] RePEc:zbw:sfb475:200429 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010296612