Seminar: Refik Soyer

Multivariate non Gaussian time series of correlated observations are considered. In so doing, we focus on multivariate time series of counts and durations. Dependence among series arises as a result of sharing a common dynamic environment. We discuss characteristics of the resulting multivariate time series models and introduce dynamic versions of some known multivariate distributions. We develop Bayesian inference for the models using Markov chain Monte Carlo and particle filtering methods and discuss computational issues. The proposed models are illustrated using actual bivariate time series data.