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A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs

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dc.contributor.authorLee, Seo Jeong-
dc.date.accessioned2024-05-02T06:02:20Z-
dc.date.available2024-05-02T06:02:20Z-
dc.date.created2024-05-02-
dc.date.created2024-05-02-
dc.date.created2024-05-02-
dc.date.created2024-05-02-
dc.date.created2024-05-02-
dc.date.created2024-05-02-
dc.date.issued2018-07-
dc.identifier.citationJournal of Business and Economic Statistics, Vol.36 No.3, pp.400-410-
dc.identifier.issn0735-0015-
dc.identifier.urihttps://hdl.handle.net/10371/200554-
dc.description.abstractUnder treatment effect heterogeneity, an instrument identifies the instrument-specific local average treatment effect (LATE). With multiple instruments, two-stage least squares (2SLS) estimand is a weighted average of different LATEs. What is often overlooked in the literature is that the postulated moment condition evaluated at the 2SLS estimand does not hold unless those LATEs are the same. If so, the conventional heteroscedasticity-robust variance estimator would be inconsistent, and 2SLS standard errors based on such estimators would be incorrect. I derive the correct asymptotic distribution, and propose a consistent asymptotic variance estimator by using the result of Hall and Inoue (2003, Journal of Econometrics) on misspecified moment condition models. This can be used to correctly calculate the standard errors regardless of whether there is more than one LATE or not.-
dc.language영어-
dc.publisherAmerican Statistical Association-
dc.titleA Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs-
dc.typeArticle-
dc.identifier.doi10.1080/07350015.2016.1186555-
dc.citation.journaltitleJournal of Business and Economic Statistics-
dc.identifier.wosid000451553000003-
dc.identifier.scopusid2-s2.0-85018171171-
dc.citation.endpage410-
dc.citation.number3-
dc.citation.startpage400-
dc.citation.volume36-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorLee, Seo Jeong-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusGENERALIZED-METHOD-
dc.subject.keywordPlusMOMENTS-
dc.subject.keywordPlusBENEFITS-
dc.subject.keywordPlusIDENTIFICATION-
dc.subject.keywordPlusBOOTSTRAP-
dc.subject.keywordPlusEDUCATION-
dc.subject.keywordPlusVALIDITY-
dc.subject.keywordPlusMODELS-
dc.subject.keywordAuthorLocal average treatment effect-
dc.subject.keywordAuthorModel misspecification-
dc.subject.keywordAuthorTreatment heterogeneity-
dc.subject.keywordAuthorTwo-stage least squares-
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  • College of Social Sciences
  • Department of Economics
Research Area GMM추정, causal inference with instrumental variables and GMM,, cluster sampling, robust inference, 군집표집, 로버스트 추정

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