Generalized Linear Dynamic Factor Models - The Single and the Mixed Frequency Case
Em.O.Univ.Prof. Manfred Deistler (Vienna University of Technology)
SYSTEMS AND CONTROL SERIESDATE: 2012-11-09
TIME: 11:00:00 - 12:00:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
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ABSTRACT:
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 acurse of dimensionalitya. We present a structure theory with em- phasis on the zeroless case, which is generic in the setting considered. Modelling of the latent variables is decomposed into two steps, first the transformation of the latent variables to static factors by a linear static transformation. Then, in the second step, modelling of the static factors as a possibly singular autoregres- sive process. The (generalized) Yulea"Walker equations are used for parameter estimation. The Yulea"Walker equations do not necessarily have a unique solu- tion in the singular case, and the resulting complexities are examined with a view to find a stable and coprime system. Finally, some preliminary results for the mixed frequency case are presented.
BIO:
Manfred Deistler is a Professor of Econometrics and System Theory at Vienna University of Technology. He received his Dr. techn. (approximately corresponding to a PhD) from Vienna University of Technology in 1970. Manfred Deistler has served on the editorial board of a number of journals, at present he is an Associate Editor of Journal of Econometrics and of Journal of Time Series Analysis and he is a member of the Advisory Board of Econometric Theory. He is a Fellow of the Econometric Society, a Fellow of IEEE (The Institute of Electrical and Electronic Engineers) and a Fellow of the Journal of Econometrics. Manfred Deistler Is research interests are in econometrics, system identification and time series analysis. As far as theory and methods are concerned the focus of his work is on structure theory and estimation for multivariate ARMAX- and state space systems and for linear dynamic factor- and errors- in- variables models. His current research interests are modeling of high dimensional time series and parameterization of multivariate state space systems. As far as applications are concerned, his current interests are: Forecasting of financial assets, analysis of electroencephalograms, analysis and forecasting of sales data and, to a lesser degree, data-driven modeling of combustion engines.
