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 a q-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 leadings. Alternative criteria are discussed in the closing section. The results of an empirical application are presented in some detail.
All Science Journal Classification (ASJC) codes
- General Psychology
- Applied Mathematics