The compound Poisson INAR (1) model for time series of overdispersed counts is
considered. For such CPINAR (1) processes, explicit results are derived for joint moments …

The INARCH (1) model for overdispersed time series of counts has a simple structure, a
parsimonious parametrization, and a great potential for applications in practice. We analyze …

Time series of counts occur in many fields of practice, with the Poisson distribution as a
popular choice for the marginal process distribution. A great variety of serial dependence …

The marginal distribution of count data processes rarely follows a simple Poisson model in
practice. Instead, one commonly observes deviations such as overdispersion or zero …

Real count data time series often show the phenomenon of the underdispersion and
overdispersion. In this paper, we develop two extensions of the first-order integer-valued …

The time series of counts observed in practice often exhibit overdispersion. The INGARCH
(p, q) models are able to describe integer-valued processes with overdispersion. Known …

The Poisson distribution is a simple and popular model for count-data random variables, but
it suffers from the equidispersion requirement, which is often not met in practice. While …

This paper is concerned with a novel version of the INAR (1) model, a non‐linear auto‐
regressive Markov chain on ℕ, with innovations following a finite mixture distribution of m≧1 …

An extension of the INAR (1) process which is useful for modelling discrete-time dependent
counting processes is considered. The model investigated here has a form similar to that of …

Frequently count time series exhibit overdispersion, but the opposite phenomenon of
underdispersion is well documented in some situations, thus may be encountered in real …