Free access to paper on coherent forecasting using Box–Jenkins's AR(p) model

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  • Author: Statistics Views, Raju Maiti, Atanu Biswas, Samarjit Das
  • Date: 22 August 2016
  • Copyright: © VVS

Each week, we select a recently published article and provide free access. This week's is from Statistica Neerlandica and is available from Early View.

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Coherent forecasting for count time series using Box–Jenkins's AR(p) model

Raju Maiti, Atanu Biswas, Samarjit Das

Statistica Neerlandica, Early View

DOI: 10.1111/stan.12083

thumbnail image: Free access to paper on coherent forecasting using Box–Jenkins's AR(p) model

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, the authors 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. They then carry out some simulation experiments, which show the adequacy of the proposed method over the available alternatives.

The 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.

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