Summary: During the last three decades, integer-valued autoregressive process of order \(p\) [or INAR(\(p\))] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer-valued time-series data. However, this literature is surprisingly mute on the usefulness of the standard AR(\(p\)) process, which is otherwise meant for continuous-valued time-series data. In this paper, we attempt to explore the usefulness of the standard AR(\(p\)) model for obtaining coherent forecasting from integer-valued time series. First, some advantages of this standard Box-Jenkins’s type AR(\(p\)) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from \(\mathrm{INAR}(p)\) process, Box-Jenkins’s model performs as good as the INAR(\(p\)) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(\(p\)) model for integer-valued time-series data.
{© 2015 The Authors. Statistica Neerlandica © 2015 VVS.}