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dc.contributor.author신동완*
dc.date.accessioned2016-08-27T04:08:32Z-
dc.date.available2016-08-27T04:08:32Z-
dc.date.issued2016*
dc.identifier.issn0361-0918*
dc.identifier.issn1532-4141*
dc.identifier.otherOAK-16005*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/217822-
dc.description.abstractSeemingly unrelated regression (SUR) method is applied to the instrumental variable (IV) estimation of the canonical contagion models. A finite sample Monte Carlo experiment shows that the resulting estimator, IV-SUR estimator, is substantially better than the existing IV estimator in terms of both bias and mean squares error under diverse circumstance of instrument, conditional heteroscedasticity, and cross-section correlation.*
dc.languageEnglish*
dc.publisherTAYLOR &amp*
dc.publisherFRANCIS INC*
dc.subjectContagion*
dc.subjectInstrumental variable estimator*
dc.subjectSeemingly unrelated regression*
dc.titleSUR Approach for IV Estimation of Canonical Contagion Models*
dc.typeArticle*
dc.relation.issue1*
dc.relation.volume45*
dc.relation.indexSCIE*
dc.relation.indexSCOPUS*
dc.relation.startpage378*
dc.relation.lastpage387*
dc.relation.journaltitleCOMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION*
dc.identifier.doi10.1080/03610918.2013.864764*
dc.identifier.wosidWOS:000365094700022*
dc.identifier.scopusid2-s2.0-84947741330*
dc.author.googleShin, Dong Wan*
dc.author.googleKim, Hyo Jin*
dc.author.googleSeo, Jinwook*
dc.contributor.scopusid신동완(7403352539)*
dc.date.modifydate20240116115756*
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자연과학대학 > 통계학전공 > Journal papers
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