Coherent forecasting for count time series using Box-Jenkins's AR(p) model
Article Type
Research Article
Publication Title
Statistica Neerlandica
Abstract
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 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.
First Page
123
Last Page
145
DOI
10.1111/stan.12083
Publication Date
5-1-2016
Recommended Citation
Maiti, Raju; Biswas, Atanu; and Das, Samarjit, "Coherent forecasting for count time series using Box-Jenkins's AR(p) model" (2016). Journal Articles. 4146.
https://digitalcommons.isical.ac.in/journal-articles/4146