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Dynamic Factor Analysis of Nonstationary Multivariate Time Series

Published online by Cambridge University Press:  01 January 2025

Peter C. M. Molenaar,
Jan G. De Gooijer and
Bernhard Schmitz
Peter C. M. Molenaar*
Affiliation:
Department of Psychology, University of Amsterdam
Jan G. De Gooijer
Affiliation:
Department of Economic Statistics, University of Amsterdam
Bernhard Schmitz
Affiliation:
Max Planck Institute for Human Development and Education, Berlin
*
Requests for reprints should be sent to Peter C. M. Molenaar, Department of Psychology, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, THE NETHERLANDS.
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Abstract

A dynamic factor model is proposed for the analysis of multivariate nonstationary time series in the time domain. The nonstationarity in the series is represented by a linear time dependent mean function. This mild form of nonstationarity is often relevant in analyzing socio-economic time series met in practice. Through the use of an extended version of Molenaar's stationary dynamic factor analysis method, the effect of nonstationarity on the latent factor series is incorporated in the dynamic nonstationary factor model (DNFM). It is shown that the estimation of the unknown parameters in this model can be easily carried out by reformulating the DNFM as a covariance structure model and adopting the ML algorithm proposed by Jöreskog. Furthermore, an empirical example is given to demonstrate the usefulness of the proposed DNFM and the analysis.

Keywords

AICdynamic factor analysisKalman filterMarkovian state-space modelnonstationaritySBIC

Information

Type
Original Paper
Information
Psychometrika , Volume 57 , Issue 3 , September 1992, pp. 333 - 349
Copyright
Copyright © 1992 The Psychometric Society

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