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Bayesian Estimation of Categorical Dynamic Factor Models - 2005-05-10 - Zhang, Z. & Nesselroade, J. R.
Dynamic factor models have been used to analyze continuous time series data. Motivated
by a categorical time series data set, we extend two main kinds of dynamic factor models
?C the direct autoregressive factor score (DAFS) model and the white noise factor score
(WNFS) model ?C to categorical DAFS and WNFS models in the framework of the
underlying variable method. To estimate the categorical dynamic factor models, a
Bayesian method via Gibbs sampling is used. The applications of the proposed models
are demonstrated with an empirical data set. The validities of results from the empirical
study are then evaluated by means of simulation studies. Differences between continuous
and categorical dynamic factor models are evaluated and discussed.
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