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A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

Published online by Cambridge University Press:  01 January 2025

Guangjian Zhang ,
Sy-Miin Chow  and
Anthony D. Ong
Guangjian Zhang*
Affiliation:
University of Notre Dame
Sy-Miin Chow
Affiliation:
University of North Carolina at Chapel Hill
Anthony D. Ong
Affiliation:
Cornell University
*
Requests for reprints should be sent to Guangjian Zhang, Psychology Department, Haggar Hall, University of Notre Dame, Notre Dame, IN 46556, USA. E-mail: gzhang3@nd.edu
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Abstract

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a sandwich-type standard error estimator of independent data to multivariate time series data. One required element of this estimator is the asymptotic covariance matrix of concurrent and lagged correlations among manifest variables, whose closed-form expression has not been presented in the literature. The performance of the adapted sandwich-type standard error estimator is evaluated using a simulation study and further illustrated using an empirical example.

Keywords

SEMfactor analysistime series analysisstandard error estimationsandwich type standard error estimatordynamic factor analysis
Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 1 , January 2011 , pp. 77 - 96
Copyright
Copyright © 2010 The Psychometric Society

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