Generalized linear dynamic factor models: An approach via singular autoregressions

We consider generalized linear dynamic factor models. These models have been developed recently and they are used for high dimensional time series in order to overcome the "curse of dimensionality". We present a structure theory with emphasis on the zeroless case, which is generic in the setting considered. Accordingly the latent variables are modeled as a possibly singular autoregressive process and (generalized) Yule-Walker equations are used for parameter estimation. The Yule-Walker equations do not necessarily have a unique solution in the singular case, and the resulting complexities are examined with a view to find a stable and coprime system.