A1107
Title: Some contributions to harmonizable time series analysis
Authors: Anna Dudek - AGH University of Krakow (Poland)
Dominique Dehay - University of Rennes (France)
Jean-Marc Freyermuth - Aix-Marseille University (France) [presenting]
Abstract: Harmonizable time series are natural extensions of stationary time series with a spectral decomposition whose components are correlated. Thus, the covariance function of a harmonizable time series is bivariate and admits a two-dimensional Fourier decomposition (Loeve spectrum). They form a broad class of nonstationary processes that has been a subject of investigation for a long time. The purpose is to introduce a parametric form for these harmonizable processes, namely Harmonizable Vector AutoRegressive and Moving Average models (HVARMA). A method is then provided to generate finite time sample realizations of HVARMA with known Loeve spectrum, and finally, a first approach to the parameter estimation problem is discussed.
