Abstract :
This paper deals with the estimation of linear dynamic models of the autoregressive
moving average type for the conditional mean for stationary time series with
conditionally heteroskedastic innovation process+ Estimation is performed using
a particular class of subspace methods that are known to have computational advantages
as compared to estimation based on criterion minimization+ These advantages
are especially strong for high-dimensional time series+ Conditions to ensure
consistency and asymptotic normality of the subspace estimators are derived in
this paper+ Moreover asymptotic equivalence to quasi maximum likelihood estimators
based on the Gaussian likelihood in terms of the asymptotic distribution is
proved under mild assumptions on the innovations+ Furthermore order estimation
techniques are proposed and analyzed+
