Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2019

Forecasting with two generalized integer-valued autoregressive processes of order one in the mutual random environment

  • Datos identificativos

    Identificador: RP:4696
    Handle: https://hdl.handle.net/20.500.11797/RP4696
    Autores:
    Nastić, Aleksandar S.Laketa, Petra N.Popović, Predrag M.
    Resumen:
    In this article, we consider two univariate random environment integer-valued autoregressive processes driven by the same hidden process. A model of this kind is capable of describing two correlated non-stationary counting time series using its marginal variable parameter values. The properties of the model are presented. Some parameter estimators are described and implemented on the simulated time series. The introduction of this bivariate integer-valued autoregressive model with a random environment is justified at the end of the paper, where its real-life data-fitting performance was checked and compared to some other appropriate models. The forecasting properties of the model are tested on a few data sets, and forecasting errors are discussed through the residual analysis of the components that comprise the model.

  • Otros:

    Autor según el artículo: Nastić, Aleksandar S. Laketa, Petra N. Popović, Predrag M.
    Palabras clave: INAR
    Resumen: In this article, we consider two univariate random environment integer-valued autoregressive processes driven by the same hidden process. A model of this kind is capable of describing two correlated non-stationary counting time series using its marginal variable parameter values. The properties of the model are presented. Some parameter estimators are described and implemented on the simulated time series. The introduction of this bivariate integer-valued autoregressive model with a random environment is justified at the end of the paper, where its real-life data-fitting performance was checked and compared to some other appropriate models. The forecasting properties of the model are tested on a few data sets, and forecasting errors are discussed through the residual analysis of the components that comprise the model.
    Año de publicación de la revista: 2019
    Tipo de publicación: ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article