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Testing the nested fixed-point algorithm in BLP random coefficients demand estimation

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dc.contributor.authorLee, J.-
dc.contributor.authorSeo, K.-
dc.date.accessioned2021-09-03T14:08:40Z-
dc.date.available2021-09-03T14:08:40Z-
dc.date.created2021-06-17-
dc.date.issued2017-
dc.identifier.issn1229-2893-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/86138-
dc.description.abstractThis paper examines the numerical properties of the nested fixed point algorithm (NFP) using Monte Carlo experiments in the estimation of Berry, Levinsohn, and Pakes’s (1995) random coefficient logit demand model. We find that in speed, convergence and accuracy, nested fixed-point (NFP) approach using Newton’s method performs well like a mathematical programming with equilibrium constraints (MPEC) approach adopted by Dubé, Fox, and Su (2012). © 2017, Korean Econometric Society. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherKorean Econometric Society-
dc.titleTesting the nested fixed-point algorithm in BLP random coefficients demand estimation-
dc.typeArticle-
dc.contributor.affiliatedAuthorLee, J.-
dc.identifier.scopusid2-s2.0-85039857942-
dc.identifier.bibliographicCitationJournal of Economic Theory and Econometrics, v.28, no.4, pp.1 - 21-
dc.relation.isPartOfJournal of Economic Theory and Econometrics-
dc.citation.titleJournal of Economic Theory and Econometrics-
dc.citation.volume28-
dc.citation.number4-
dc.citation.startPage1-
dc.citation.endPage21-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.identifier.kciidART002296223-
dc.description.journalClass1-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorNested fixedpoint algorithm-
dc.subject.keywordAuthorNewton’s method-
dc.subject.keywordAuthorNumerical methods-
dc.subject.keywordAuthorRandom coefficients logit demand-
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