A growing interest in non-Gaussian time series, particularly in series comprised of
nonnegative integers (counts), is taking place in today's statistics literature. Count series …

The R package tscount provides likelihood-based estimation methods for analysis and
modeling of count time series following generalized linear models. This is a flexible class of …

Abstract During the last 20–30 years, there was a remarkable growth in interest on
approaches for stationary count time series. We consider popular classes of models for such …

Multivariate count autoregression Page 1 Bernoulli 26(1), 2020, 471–499 https://doi.org/10.3150/19-BEJ1132
Multivariate count autoregression KONSTANTINOS FOKIANOS1, BÅRD STØVE2,*, DAG …

Numerous models have been proposed for count time series, including the integer-valued
autoregressive moving average (ARMA) and integer-valued generalized autoregressive …

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 …

The paper introduces a general class of heavy-tailed auto-regressions for modelling integer-
valued time series with outliers. The specification proposed is based on a heavy-tailed …

We consider a positive-valued time series whose conditional distribution has a time-varying
mean, which may depend on exogenous variables. The main applications concern count or …

Modeling multivariate time series of counts using the integer-valued generalized
autoregressive conditional heteroscedastic (INGARCH) scheme is proposed. The key idea …

Univariate integer-valued time series models, including integer-valued autoregressive
(INAR) models and integer-valued generalized autoregressive conditional heteroscedastic …