OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
If staff have any questions about the Oxford Events launch, please contact halo@digital.ox.ac.uk
Please sign up for meetings at the link below:
docs.google.com/spreadsheets/d/1f8qVDhJVjjzt1slDbwiEwwKS77Bb4Yk_kg4vCM4ObfI/edit?usp=sharing
Abstract
We introduce a frequency domain criterion to identify the parameters of, possibly noncausal and/or noninvertible, vector autoregressive moving average (VARMA) models. We use information from higher order moments to achieve identification on the location of the roots of the VAR and VMA matrix polynomials for non-Gaussian vector time series possibly non-fundamental. We develop general representations of the higher order spectral density arrays of vector linear processes and describe sufficient conditions for the parameter identification that rely on both sufficiently rich (linear) dynamics and higher order dependence structure of the vector of linear innovations. These results generalize previous univariate analysis to develop more efficient estimates and relate to the predictability of Wold innovations of nonfundamental processes.
