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Psychometrika

, Volume 57, Issue 3, pp 333–349 | Cite as

Dynamic factor analysis of nonstationary multivariate time series

  • Peter C. M. Molenaar
  • Jan G. De Gooijer
  • Bernhard Schmitz
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.

Key words

AIC dynamic factor analysis Kalman filter Markovian state-space model nonstationarity SBIC 

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Copyright information

© The Psychometric Society 1992

Authors and Affiliations

  • Peter C. M. Molenaar
    • 1
  • Jan G. De Gooijer
    • 2
  • Bernhard Schmitz
    • 3
  1. 1.Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Department of Economic StatisticsUniversity of AmsterdamThe Netherlands
  3. 3.Max Planck Institute for Human Development and EducationBerlin

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