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

Guangjian Zhang, Sy-Miin Chow, Anthony D. Ong

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)77-96
Number of pages20
JournalPsychometrika
Volume76
Issue number1
DOIs
StatePublished - Jan 1 2011

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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