Yale University
Department of Statistics
Seminar

Monday, September 22, 2003

Estimating linear dynamical models using subspace methods

Dietmar Bauer
Institut for Econometrics, Operations Research and System Theory
TU Wien, Austria

Abstract: This talk presents a survey on a special class of subspace methods.
These methods are alternative to prediction error methods for the estimation
of linear dynamical models of the ARMA type (in the state space framework)
fitted to time series data. The main advantages of subspace methods lie in
their conceptual simplicity and the lower computational load as compared to
prediction error methods. The basic underlying idea is the exploitation of the
properties of the state.

The talk includes a detailed description of the methods followed by a
discussion of the results on the asymptotic properties of the algorithm. The
main emphasis here will be on explicit expressions for the asymptotic variance.

Seminar to be held in Room 107, 24 Hillhouse at 4:15 pm