Abstract
A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of aq-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average. This is accomplished by minimizing a so-called state-space criterion that penalizes deviations of the rotated solution from a generalized state-space model with only instantaneous factor loadings. Alternative criteria are discussed in the closing section. The results of an empirical application are presented in some detail.
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This research was supported by the Institute for Developmental and Health Research Methodology, University of Virginia.
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Molenaar, P.C.M., Nesselroade, J.R. Rotation in the dynamic factor modeling of multivariate stationary time series. Psychometrika 66, 99–107 (2001). https://doi.org/10.1007/BF02295735
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DOI: https://doi.org/10.1007/BF02295735