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| Bayesian Estimation of Categorical Dynamic Factor Models
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Abstract
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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| How to use DFA
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| DFA is a Windows program written in IDHRM, University of Virginia. It was first programmed by Peter C. M. Molenaar and John R. Nesselroade. It can calculate lagged topelitz matrix and test the poolability of covariance/correlation matrix (Nesselroade, J.R. & Molenaar, P.C.M. Pooling lagged covariance structures based on short, multivariate time-series for dynamic factor analysis Newbury Park, CA: Sage Publications, 1999). It can also generate Lisrel codes for dynamic factor model (DAFS). |
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