Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2025-01-05T14:20:19.865Z Has data issue: false hasContentIssue false
  • English
  • Français

Comment on Fitting MA Time Series by Structural Equation Models

Published online by Cambridge University Press:  01 January 2025

Peter C. M. Molenaar
Peter C. M. Molenaar*
Affiliation:
University of Amsterdam
*
Requests for reprints should be sent to Peter C.M. Molenaar, Department of Psychology, University of Amsterdam, Roetersstraat 15, room 1010, 1018 WB Amsterdam, THE NETHERLANDS. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In a recent paper by van Buuren (1997) it is concluded that parameter estimates in pure moving-average (MA) models, obtained by software for fitting structural equation models (SEMs), are biased and inefficient. In this comment it is shown that this negative finding may be due to a particular feature of van Buuren's simulation experiment. A modified procedure for fitting MA models by means of SEM software is proposed, and some of its implications are discussed.

Keywords

lagged variablesmoving-averagesstationarityinvertibilitypolynomial factorizationdynamic factor analysis
Type
Notes And Comments
Information
Psychometrika , Volume 64 , Issue 1 , March 1999 , pp. 91 - 94
Copyright
Copyright © 1999 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Box, G.E.P., & Jenkins, G.M. (1976). Time series analysis: Forecasting and control, San Francisco: Holden-Day.Google Scholar
Dolan, C.V., & Molenaar, P.C.M. (1993). NONLIS: A Fortran program for multi-group covariance structure analysis with nonstandard constraints. In Maarse, F.J., & Kattenpoel Oude Heerink, K. (Eds.), Computers in psychology: Tools for experimental and applied psychology, Vol. 4, Amsterdam: Swets & Zeitlinger.Google Scholar
Molenaar, P.C.M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50, 181–202.CrossRefGoogle Scholar
Molenaar, P.C.M. (1994). Dynamic latent variable models in developmental psychology. In von Eye, A., & Clogg, C.C. (Eds.), Analysis of latent variables in developmental research (pp. 155–180). Newbury Park, CA: Sage.Google Scholar
Molenaar, P.C.M., de Gooijer, J.G., & Schmitz, B. (1992). Dynamic factor analysis of nonstationary multivariate time series. Psychometrika, 57, 333–349.CrossRefGoogle Scholar
Robinson, E.A., & Silvia, M.T. (1978). Digital signal processing and time series analysis, San Francisco: Holden-Day.Google Scholar
van Buuren, S. (1997). Fitting ARMA time series by structural equation models. Psychometrika, 62, 215–236.CrossRefGoogle Scholar