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dc.contributor.authorParavee Maneejuken_US
dc.contributor.authorWoraphon Yamakaen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T04:38:49Z-
dc.date.available2018-09-05T04:38:49Z-
dc.date.issued2018-07-26en_US
dc.identifier.issn17426596en_US
dc.identifier.issn17426588en_US
dc.identifier.other2-s2.0-85051406678en_US
dc.identifier.other10.1088/1742-6596/1053/1/012130en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85051406678&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/59127-
dc.description.abstract© Published under licence by IOP Publishing Ltd. The Markov-switching (MS) model is one of the most popular nonlinear time series models in the literature. However, the estimation methods which are normally used to estimate the MS models rely on the assumption of a parametric distribution, which sometimes is considered as a strong assumption. This study, therefore, tries to relax the assumption and develop a more flexible estimator for the MS models that is a maximum empirical likelihood estimation. According to this approach, the parametric likelihood will be replaced by the empirical likelihood function with relatively minor modifications to existing recursive filters. A performance of the suggested estimation method is then evaluated through a Monte Carlo experiment and a real application, the U.S. business cycle. Overall results of both empirical studies indicate that the empirical likelihood could outweigh the classical likelihood estimators.en_US
dc.subjectPhysics and Astronomyen_US
dc.titleEmpirical likelihood estimation of the Markov-switching modelen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleJournal of Physics: Conference Seriesen_US
article.volume1053en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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