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A dynamic factor model for the analysis of multivariate time series

Abstract

As a method to ascertain the structure of intra-individual variation,P-technique has met difficulties in the handling of a lagged covariance structure. A new statistical technique, coined dynamic factor analysis, is proposed, which accounts for the entire lagged covariance function of an arbitrary second order stationary time series. Moreover, dynamic factor analysis is shown to be applicable to a relatively short stretch of observations and therefore is considered worthwhile for psychological research. At several places the argumentation is clarified through the use of examples.

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Correspondence to Peter C. M. Molenaar.

Additional information

I would like to thank WM. van der Molen, G. J. Mellenbergh and L. H. M. Oppenheimer, who provided valuable ideas that led to this formulation.

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Molenaar, P.C.M. A dynamic factor model for the analysis of multivariate time series. Psychometrika 50, 181–202 (1985). https://doi.org/10.1007/BF02294246

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Keywords

  • Time Series
  • Covariance
  • Public Policy
  • Statistical Technique
  • Statistical Theory