Molenaar, P. C. ; Gooijer, J. G. de ; Schmitz, B. (1992)
Dynamic factor analysis of nonstationary multivariate time series.
In: Psychometrika, 57
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel | ||||
|---|---|---|---|---|---|
| Erschienen: | 1992 | ||||
| Autor(en): | Molenaar, P. C. ; Gooijer, J. G. de ; Schmitz, B. | ||||
| Art des Eintrags: | Bibliographie | ||||
| Titel: | Dynamic factor analysis of nonstationary multivariate time series | ||||
| Sprache: | Englisch | ||||
| Publikationsjahr: | 1992 | ||||
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Psychometrika | ||||
| Jahrgang/Volume einer Zeitschrift: | 57 | ||||
| Kurzbeschreibung (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. |
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| Zusätzliche Informationen: | Impact Factor: 1.78 |
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| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
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| Hinterlegungsdatum: | 21 Feb 2012 12:17 | ||||
| Letzte Änderung: | 23 Aug 2021 10:19 | ||||
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