Fitting ARMA time series by structural equation models

DOI10.1007/BF02295276zbMath1003.62547OpenAlexW2100473319WikidataQ59419676 ScholiaQ59419676MaRDI QIDQ1362271

Stef van Buuren

Publication date: 6 May 1999

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02295276

zbMATH Keywords

autocorrelationcovariance structuresBox-Jenkins modelintervention analysislagged variablesPROC CALIS

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to psychology (62P15)

Related Items (6)

A sandwich-type standard error estimator of SEM models with multivariate time series ⋮ Comment on: Fitting ARMA time series by structural equation models ⋮ Dynamic analysis of multivariate panel data with nonlinear transformations ⋮ A Bayesian analysis of finite mixtures in the LISREL model ⋮ Autoregressive generalized linear mixed effect models with crossed random effects: an application to intensive binary time series eye-tracking data ⋮ Errors-in-variables system identification using structural equation modeling

Uses Software

• SAS
• EQS
• BMDP
• SAS/STAT

Cites Work

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• Relationships between linear systems theory and covariance structure modeling
• A dynamic factor model for the analysis of multivariate time series
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• Circumplex models for correlation matrices
• Linear structural equations with latent variables
• Algorithm AS 197: A Fast Algorithm for the Exact Likelihood of Autoregressive-Moving Average Models
• Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Iterative maximum‐likelihood estimation of parameters of the Toeplitz correlation structure

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