We consider modeling of multivariate nonGaussian time series of correlated observations. In so doing, we focus on multivariate time series of durations and counts. Dependence among series arises as a result of sharing a common dynamic environment. We discuss characteristics of the resulting multivariate time series models and develop Bayesian inference for them using particle filtering and Markov chain Monte Carlo methods.