Multiple Indicator Stationary Time Series Models

    Authors

    S. A. Sivo

    Comments

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    Abbreviated Journal Title

    Struct. Equ. Modeling

    Keywords

    PANEL; Mathematics, Interdisciplinary Applications; Social Sciences, ; Mathematical Methods

    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 and Willson (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.

    Journal Title

    Structural Equation Modeling-a Multidisciplinary Journal

    Volume

    8

    Issue/Number

    4

    Publication Date

    1-1-2001

    Document Type

    Article

    Language

    English

    First Page

    599

    Last Page

    612

    WOS Identifier

    WOS:000208064800005

    ISSN

    1070-5511

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