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csa2sls: A complete subset approach for many instruments using Stata

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dc.contributor.authorLee, Seo Jeong-
dc.contributor.authorLee, Siha-
dc.contributor.authorOwusu, Julius-
dc.contributor.authorShin, Youngki-
dc.date.accessioned2024-05-02T06:44:49Z-
dc.date.available2024-05-02T06:44:49Z-
dc.date.created2024-01-11-
dc.date.created2024-01-11-
dc.date.created2024-01-11-
dc.date.created2024-01-11-
dc.date.created2024-01-11-
dc.date.issued2023-12-
dc.identifier.citationStata Journal, Vol.23 No.4, pp.932-941-
dc.identifier.issn1536-867X-
dc.identifier.urihttps://hdl.handle.net/10371/200716-
dc.description.abstractWe developed a command, csa2sls, that implements the complete subset averaging two-stage least-squares (CSA2SLS) estimator in Lee and Shin (2021, Econometrics Journal 24: 290–314). The CSA2SLS estimator is an alternative to the two-stage least-squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of csa2sls in Stata with an empirical application.-
dc.language영어-
dc.publisherStata Press-
dc.titlecsa2sls: A complete subset approach for many instruments using Stata-
dc.typeArticle-
dc.identifier.doi10.1177/1536867X231212432-
dc.citation.journaltitleStata Journal-
dc.identifier.wosid001133095300007-
dc.identifier.scopusid2-s2.0-85180643355-
dc.citation.endpage941-
dc.citation.number4-
dc.citation.startpage932-
dc.citation.volume23-
dc.description.isOpenAccessY-
dc.contributor.affiliatedAuthorLee, Seo Jeong-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordAuthorcomplete subset averaging-
dc.subject.keywordAuthorcsa2sls-
dc.subject.keywordAuthormany instruments-
dc.subject.keywordAuthorst0732-
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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