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Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] METRON [ISSN:] 2281-695X [Volume:] 79 [Issue:] 2 [Publisher:] Springer [Place:] Milan [Year:] 2021 [Pages:] 137-158
Verlag: 
Springer, Milan
Zusammenfassung: 
We discuss robust estimation of INARCH models for count time series, where each observation conditionally on its past follows a negative binomial distribution with a constant scale parameter, and the conditional mean depends linearly on previous observations. We develop several robust estimators, some of them being computationally fast modifications of methods of moments, and some rather efficient modifications of conditional maximum likelihood. These estimators are compared to related recent proposals using simulations. The usefulness of the proposed methods is illustrated by a real data example.
Schlagwörter: 
Count time series
Negative binomial distribution
Overdispersion
Generalized linear models
Rank autocorrelation
Tukey M-estimator
Additive outliers
JEL: 
F35
G10
Persistent Identifier der Erstveröffentlichung: 
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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