Oxford Events, the new replacement for OxTalks, will launch on 16th March. The two-week OxTalks freeze period starts on Monday 2nd March. During this time, there will be no facility to publish or edit events. The existing OxTalks site will remain available to view during this period. Once Oxford Events launches, you will need a Halo login to submit events. Full details are available on the Staff Gateway.
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.
