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.