On robust estimation of negative binomial INARCH models

dc.contributor.authorElsaied, Hanan
dc.contributor.authorFried, Roland
dc.date.accessioned2022-03-03T13:36:26Z
dc.date.available2022-03-03T13:36:26Z
dc.date.issued2021-04-24
dc.description.abstractWe 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.en
dc.identifier.urihttp://hdl.handle.net/2003/40748
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22606
dc.language.isoende
dc.relation.ispartofseriesMetron;Vol. 79. 2021, H. 2, S. 137-158
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.de
dc.subjectCount time seriesen
dc.subjectNegative binomial distributionen
dc.subjectOverdispersionen
dc.subjectGeneralized linear modelsen
dc.subjectRank autocorrelationen
dc.subjectTukey M-estimatoren
dc.subjectAdditive outliersen
dc.subject.ddc310
dc.subject.ddc570
dc.titleOn robust estimation of negative binomial INARCH modelsde
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationMetron. Vol. 79. 2021, H. 2, S. 137-158en
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s12054-021-00414-7de

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