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Reconciling the Return Predictability Evidence under Structural Breaks

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dc.contributor.author박철범-
dc.date.accessioned2021-09-04T07:49:03Z-
dc.date.available2021-09-04T07:49:03Z-
dc.date.created2021-06-17-
dc.date.issued2016-
dc.identifier.issn1225-5017-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/91074-
dc.description.abstractThis study shows that the poor out-of-sample performance of the realtime adjusted dividend-price ratio reported in Lettau and Nieuwerburgh (2008) is mainly a result of the gap period between the occurrence of a break and its detection, which implies that the poor out-of-sample performance of the adjusted dividend-price ratio is due to the requirement in Bai and Perron’s (1998) procedure that breaks must be away from the boundaries of the sample. A substantial improvement in the out-of-sample performance of the adjusted dividend-price ratio during the gap period is shown with the use of Andrews’s (2003) procedure in the real-time adjustment of the dividend-price ratio. The newly suggested procedure for the adjusted dividend-price ratio in this study has better out-ofsample performance than the simple sample mean, although it is not significant.-
dc.languageEnglish-
dc.language.isoen-
dc.publisher서울대학교행정대학원-
dc.titleReconciling the Return Predictability Evidence under Structural Breaks-
dc.title.alternativeReconciling the Return Predictability Evidence under Structural Breaks-
dc.typeArticle-
dc.contributor.affiliatedAuthor박철범-
dc.identifier.bibliographicCitationThe Korean Journal of Policy Studies, v.31, no.2, pp.71 - 81-
dc.relation.isPartOfThe Korean Journal of Policy Studies-
dc.citation.titleThe Korean Journal of Policy Studies-
dc.citation.volume31-
dc.citation.number2-
dc.citation.startPage71-
dc.citation.endPage81-
dc.type.rimsART-
dc.identifier.kciidART002143242-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorstock-return predictability-
dc.subject.keywordAuthorstructural break-
dc.subject.keywordAuthorout-of-sample forecast-
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