INARMA Modeling of Count Time Series

INARMA Modeling of Count Time Series

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 likelihoo...

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Veröffentlicht in:Stats (Basel, Switzerland) Switzerland), 2019-06, Vol.2 (2), p.284-320
Hauptverfasser: Weiß, Christian H., Feld, Martin H.-J. M., Mamode Khan, Naushad, Sunecher, Yuvraj
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container_issue 2
container_start_page 284
container_title Stats (Basel, Switzerland)
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creator Weiß, Christian H.
Feld, Martin H.-J. M.
Mamode Khan, Naushad
Sunecher, Yuvraj
description 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.
doi_str_mv 10.3390/stats2020022
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title INARMA Modeling of Count Time Series
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