Latent variable analysis and partial correlation graphs for multivariate time series

We investigate the possibility of exploiting partial correlation graphs for identifying interpretable latent variables underlying a multivariate time series. It is shown how the collapsibility and separation properties of partial correlation graphs can be used to understand the relation between a factor model and the structure among the observable variables.

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Year of publication:	2003
Authors:	Fried, Roland ; Didelez, Vanessa
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	Multivariate Analyse \| Zeitreihenanalyse \| Theorie \| Korrelation \| Time series analysis \| Dimension reduction \| Factor analysis \| Partial correlations

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Series:	Technical Report ; 2003,06
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	817943315 [GVK] hdl:10419/49370 [Handle] RePEc:zbw:sfb475:200306 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010306285