Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287192 
Year of Publication: 
2021
Citation: 
[Journal:] METRON [ISSN:] 2281-695X [Volume:] 79 [Issue:] 2 [Publisher:] Springer [Place:] Milan [Year:] 2021 [Pages:] 137-158
Publisher: 
Springer, Milan
Abstract: 
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.
Subjects: 
Count time series
Negative binomial distribution
Overdispersion
Generalized linear models
Rank autocorrelation
Tukey M-estimator
Additive outliers
JEL: 
F35
G10
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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