An INAR(1) model based on the Pegram and thinning operators with serially dependent innovation

The present work proposes a new INAR(1) model based on the Pegram and thinning operators, where the innovations are supposed to be serially dependent to the current population. Several properties of the model are discussed. Maximum likelihood and modified Yule-Walker methods besides, a new sieve boo...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 49; no. 10; pp. 2617 - 2638
Main Authors Shirozhan, Masoumeh, Mohammadpour, Mehrnaz
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.10.2020
Taylor & Francis Ltd
Subjects
INAR model
Innovations
Operators
Overdispersion
Parameter estimation
Pegram operator
Thinning
Thinning operator
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Summary:The present work proposes a new INAR(1) model based on the Pegram and thinning operators, where the innovations are supposed to be serially dependent to the current population. Several properties of the model are discussed. Maximum likelihood and modified Yule-Walker methods besides, a new sieve bootstrap approach are considered for the parameter estimation of the model and their performances and forecasting methods are checked by a simulation. This survey was carried out to study the efficiency of the new model by applying it on a real data.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2018.1521975