  We review autoregressive models for the analysis of multivariate count time
series. In doing so, we discuss the choice of a suitable distribution for a
vectors of count random variables. This review focus on three main approaches
taken for multivariate count time series analysis: (a) integer autoregressive
processes, (b) parameter-driven models and (c) observation-driven models. The
aim of this work is to highlight some recent methodological developments and
propose some potentially useful research topics.
