Vector autoregressive models with measurement errors
Alexandre Galvão Patriota
Abstract:
This presentation introduces a new approach for the estimation of vector
autoregressive (VAR) models in the case of multivariate time series
with measurement errors. Consistent parameter estimators and their
asymptotic distributions are presented. The estimation procedure does
not require any iterative process. The theoretical results and
measurement error effects on
parameter estimates were evaluated by using computational simulations.
The simulation results show that the proposed approach produces test
size close to the adopted nominal level (even for small samples) and
has a good performance around
the null hypothesis when the time series are subject to small
and moderate measurement errors.
The applicability and usefulness of the proposed approach are illustrated using a
functional magnetic resonance imaging dataset.
This is a joint work with Betsabé Blas and João Sato.