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dc.contributor.authorMaruotti, Antonello
dc.contributor.authorPetrella, Lea
dc.contributor.authorSposito, Luca
dc.date.accessioned2022-05-04T12:59:54Z
dc.date.available2022-05-04T12:59:54Z
dc.date.created2021-09-06T15:35:15Z
dc.date.issued2021
dc.identifier.issn0167-9473
dc.identifier.urihttps://hdl.handle.net/11250/2994220
dc.description.abstractA hidden semi-Markov-switching quantile regression model is introduced as an extension of the hidden Markov-switching one. The proposed model allows for arbitrary sojourn-time distributions in the states of the Markov-switching chain. Parameters estimation is carried out via maximum likelihood estimation method using the Asymmetric Laplace distribution. As a by product of the model specification, the formulae and methods for forecasting, the state prediction, decoding and model checking that exist for ordinary hidden Markov-switching models can be applied to the proposed model. A simulation study to investigate the behaviour of the proposed model is performed covering several experimental settings. The empirical analysis studies the relationship between the stock index from the emerging market of China and those from the advanced markets, and investigates the determinants of high levels of pollution in an Italian small city.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleHidden semi-Markov-switching quantile regression for time seriesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.source.articlenumber107208en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1016/j.csda.2021.107208
dc.identifier.cristin1931716
dc.source.journalComputational Statistics & Data Analysisen_US
dc.identifier.citationComputational Statistics & Data Analysis. 2021, 159, 107208.en_US
dc.source.volume159en_US


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