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Why are Bayesian trend-cycle decompositions of US real GDP so different?

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dc.contributor.authorKim, Jaeho-
dc.contributor.authorChon, Sora-
dc.date.accessioned2023-08-16T07:45:55Z-
dc.date.available2023-08-16T07:45:55Z-
dc.date.issued2020-03-
dc.identifier.issn0377-7332-
dc.identifier.issn1435-8921-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114236-
dc.description.abstractThis paper provides an underlying reason for why recent Bayesian trend-cycle decompositions of US real GDP differ despite using identical unobserved components models. We stress that a pitfall in estimating unobserved components models accounts for the divergence in the empirical conclusions. Our results also show that the decline in the long-run growth rate of real GDP has been slow and gradual rather than abrupt during the post-World War II period. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.-
dc.format.extent16-
dc.language영어-
dc.language.isoENG-
dc.publisherSpringer Verlag-
dc.titleWhy are Bayesian trend-cycle decompositions of US real GDP so different?-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.doi10.1007/s00181-018-1554-0-
dc.identifier.scopusid2-s2.0-85053471648-
dc.identifier.wosid000514996800017-
dc.identifier.bibliographicCitationEmpirical Economics, v.58, no.3, pp 1339 - 1354-
dc.citation.titleEmpirical Economics-
dc.citation.volume58-
dc.citation.number3-
dc.citation.startPage1339-
dc.citation.endPage1354-
dc.type.docType정기학술지(Article(Perspective Article포함))-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBusiness & Economics-
dc.relation.journalResearchAreaMathematical Methods In Social Sciences-
dc.relation.journalWebOfScienceCategoryEconomics-
dc.relation.journalWebOfScienceCategorySocial Sciences, Mathematical Methods-
dc.subject.keywordPlusOIL-PRICE SHOCK-
dc.subject.keywordPlusTIME-SERIES-
dc.subject.keywordPlusGREAT CRASH-
dc.subject.keywordPlusBREAK-
dc.subject.keywordPlusMODEL-
dc.subject.keywordAuthorGibbs sampling-
dc.subject.keywordAuthorStructural break-
dc.subject.keywordAuthorTrend-cycle decomposition-
dc.subject.keywordAuthorUnobserved components model-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00181-018-1554-0-
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