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A Limited Information Estimator for Dynamic Factor Models.


ABSTRACT: Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.

SUBMITTER: Fisher ZF 

PROVIDER: S-EPMC7473595 | biostudies-literature | 2019 Mar-Apr

REPOSITORIES: biostudies-literature

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A Limited Information Estimator for Dynamic Factor Models.

Fisher Zachary F ZF   Bollen Kenneth A KA   Gates Kathleen M KM  

Multivariate behavioral research 20190304 2


Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the imp  ...[more]

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