Generalized linear dynamic factor models are analysed. These models have been developed recently and they are used for analysis and forecasting of high dimensional time series in order to overcome the ¿curse of dimensionality¿. We develop a structure theory, with emphasis on the zeroless case, which is generic in the setting considered. Accordingly the latent variables are modeled as a singular autoregressive process and (generalized) Yule Walker equations are used for estimation.
