A dynamic factor model for the analysis of multivariate time series

Describes the new statistical technique of dynamic factor analysis (DFA), which accounts for the entire lagged covariance function of an arbitrary 2nd-order stationary time series. DFA is shown to be applicable to a relatively short stretch of observations and is therefore considered worthwhile for psychological research in areas such as individual psychotherapy and individual differences in EEG topography. Applications using real data are presented