Title:
Prediction of singular VARs and application to generalized dynamic
factor models
Abstract :
Vector autoregressive processes (VARs) with innovations having a
singular covariance matrix (in short singular VARs) appear naturally in
the context of dynamic factor models. The Yule-Walker estimator of such
a VAR is problematic, because the solution of the corresponding equation
system tends to be numerically rather unstable. For example, if we
overestimate the order of the VAR, then the singularity of the
innovations renders the Yule-Walker equation system singular as well.
Moreover, even with correctly selected order, the Yule-Walker system
tends be close to singular in finite sample. In this paper we are going
to show that this has a severe impact on predictions. While the
asymptotic rate of the mean square prediction error (MSPE) can be just
like in the regular (non-singular) case, the finite sample behaviour is
suffering. This effect will turn out to be particularly dramatic in
context of dynamic factor models, where we do not directly observe the
so-called common components which we aim to predict. Then, when the data
are sampled with some additional error, the MSPE often gets severely
inflated. We will explain the reason for this phenomenon and show how to
overcome the problem. Our numerical results underline that it is very
important to adapt prediction algorithms accordingly.
Speaker
Gilles Nisol, post-doc researcher at ULB (Universite' Libre de Bruxelles)
Location
Idsia's Meeting Room