INARMA Modeling of Count Time Series

Christian H. Weiß, Martin H.-J. M. Feld, Naushad Mamode Khan and Yuvraj Sunecher
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Christian H. Weiß: Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany
Martin H.-J. M. Feld: Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany
Naushad Mamode Khan: Department of Economics and Statistics, University of Mauritius, Reduit 80837, Mauritius
Yuvraj Sunecher: School of Business, Management and Finance, University of Technology, La Tour Koenig 11134, Mauritius

Stats, 2019, vol. 2, issue 2, 1-37

Abstract: While most of the literature about INARMA models (integer-valued autoregressive moving-average) concentrates on the purely autoregressive INAR models, we consider INARMA models that also include a moving-average part. We study moment properties and show how to efficiently implement maximum likelihood estimation. We analyze the estimation performance and consider the topic of model selection. We also analyze the consequences of choosing an inadequate model for the given count process. Two real-data examples are presented for illustration.

Keywords: INARMA models; maximum likelihood estimation; model selection; model adequacy (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:2:y:2019:i:2:p:22-320:d:236899

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