Multivariate Count Time Series Modelling

Abstract

Autoregressive models are reviewed for the analysis of multivariate count time series. A particular topic of interest which is discussed in detail is that of the choice of a suitable distribution for a vectors of count random variables. The focus is 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 is to highlight some recent methodological developments and propose some potentially useful research topics.