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M. Deistler, Brian Anderson, A. Filler, W. Chen:
"Generalized Linear Dynamic Factor Models - A Structure theory";
Vortrag: Computational and Financial Econometrics (CFE'10), London, UK (eingeladen); 10.12.2010 - 12.12.2010.

We consider generalized linear dynamic factor models. These models have been developed recently and they are used for forecasting and analysis
of high dimensional time series in order to overcome the curse of dimensionality plaguing traditional multivariate time series analysis. We consider
a stationary framework; the observations are represented as the sum of two uncorrelated component processes: The so called latent process, which
is obtained from a dynamic linear transformation of a low dimensional factor process and which shows strong dependence of its components, and
the noise process, which shows weak dependence of the components. The latent process is assumed to have a singular rational spectral density.
For the analysis, the cross sectional dimension n, i.e. the number of single time series is going to infinity; the decomposition of the observations
into these two components is unique only for n tending to infinity. We present a structure theory giving a state space or ARMA realization for the
latent process, commencing from the second moments of the observations. The emphasis is 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.

http://publik.tuwien.ac.at/files/PubDat_193846.pdf