Event Details

We will have a presentation by Konstantinos Fokianos, which will be followed by comments from John Boylan.

Abstract:

We are studying linear and log-linear models for multivariate count time series data with Poisson marginals. For studying the properties of such processes we develop a novel conceptual framework which is based on copulas. Earlier contributions impose the copula on the joint distribution of the vector of counts by employing a continuous extension methodology. Instead we introduce a copula function on a vector of associated continuous random variables. This construction avoids conceptual difficulties related to the joint distribution of counts yet it keeps the properties of the Poisson process marginally. Furthermore, this construction can be employed for modeling multivariate count time series with other marginal count distributions. We study stability properties of these processes and suitable estimating equations are suggested for estimating unknown model parameters. The work concludes with some simulations and a real data example.