Title

Multiple Indicator Stationary Time Series Models

Abstract

This article is intended to complement previous research (Sivo, 1997; Sivo & Willson, 1998, in press) by discussing the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. Three practical considerations motivated this article. Unlike Marsh (1993), Sivo andWillson (2000) did not offer multiple indicator (latent order) equivalents to their autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models. Moreover, such models have yet to be discussed, despite Marsh's (1993) advocacy for multiple indicator models in general. Further motivating multiple indicator extensions of the AR, MA, and ARMA equivalent models is the fact that longitudinal studies often collect data on more than 1 related variable per occasion. Such multiple indicator models capitalize on 1 of the chief analytical advantages of structural equation modeling in that measurement error may be estimated directly. © 2001, Lawrence Erlbaum Associates, Inc.

Publication Date

12-1-2001

Publication Title

Structural Equation Modeling

Volume

8

Issue

4

Number of Pages

599-612

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1207/S15328007SEM0804_05

Socpus ID

18444363865 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/18444363865

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