A Limited Information Estimator for Dynamic Factor Models

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 me...

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Bibliographic Details
Published in:Multivariate behavioral research 2019-03, Vol.54 (2), p.246-263
Main Authors: Fisher, Zachary F., Bollen, Kenneth A., Gates, Kathleen M.
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Data analysis
Data Interpretation, Statistical
Diagnostic systems
Dynamic factor analysis
Economic models
Humans
Latent Class Analysis
Mathematical models
Models, Statistical
Multivariate analysis
robustness
Structural equation modeling
structural misspecification
Time series
time series analysis
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Summary: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.
ISSN:0027-3171
1532-7906
DOI:10.1080/00273171.2018.1519406