Dynamic factor analysis of nonstationary multivariate time series

DOI10.1007/BF02295422zbMath0825.92165OpenAlexW2135337222MaRDI QIDQ1205779

Bernhard Schmitz, Peter C. M. Molenaar, Jan G. De Gooijer

Publication date: 1 April 1993

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02295422

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Spatial models in sociology (91D25)

Related Items (11)

Fitting ARMA time series by structural equation models ⋮ Continuous time modeling of panel data: SEM versus filter techniques ⋮ Dynamic factor analysis for short panels: estimating performance trajectories for water utilities ⋮ Comment on: Fitting ARMA time series by structural equation models ⋮ A novel algorithm for dynamic factor analysis ⋮ Dynamic analysis of multivariate panel data with nonlinear transformations ⋮ A wavelet-based approach for imputation in nonstationary multivariate time series ⋮ Rotation in the dynamic factor modeling of multivariate stationary time series ⋮ Fitting dynamic factor models to non-stationary time series ⋮ The exact covariance matrix of dynamic models with latent variables ⋮ 50 Years of international journal of systems science: a review of the past and trends for the future

Uses Software

• BMDP

Cites Work

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• Identification in restricted factor models and the evaluation of rank conditions
• Relationships between linear systems theory and covariance structure modeling
• A dynamic factor model for the analysis of multivariate time series
• Dynamic structural systems under indirect observation: Identifiability and estimation aspects from a system theoretic perspective
• Factor analysis and AIC
• Estimating the dimension of a model
• Pseudo Maximum Likelihood Methods: Theory
• Identifying a Simplifying Structure in Time Series
• Parametrizations of linear dynamical systems: Canonical forms and identifiability
• A canonical analysis of multiple time series
• Some results on multivariate autoregressive index models

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