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Dynamic factor analysis of nonstationary multivariate time series

Molenaar, P. C. and De Gooijer, J. G. and Schmitz, B. (1992):
Dynamic factor analysis of nonstationary multivariate time series.
In: Psychometrika, pp. 333-349, 57, [Article]

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.

Item Type: Article
Erschienen: 1992
Creators: Molenaar, P. C. and De Gooijer, J. G. and Schmitz, B.
Title: Dynamic factor analysis of nonstationary multivariate time series
Language: English
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.

Journal or Publication Title: Psychometrika
Volume: 57
Divisions: 03 Department of Human Sciences > Institute for Psychology
03 Department of Human Sciences
Date Deposited: 21 Feb 2012 12:17
Additional Information:

Impact Factor: 1.78

Alternative keywords:
Alternative keywordsLanguage
AIC - dynamic factor analysis - Kalman filter - Markovian state-space model - nonstationarity - SBICEnglish
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