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
Institutions:	Institut für Wirtschafts- und Sozialstatistik, Universität Dortmund
Subject:	Time series analysis \| Dimension reduction \| Factor analysis \| Partial correlations

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Extent:	application/pdf
Series:	Technical Reports.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number 2003,06
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009295191