Instrument residual estimator for any response variable with endogenous binary treatment*
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Myoung-jae | - |
dc.date.accessioned | 2022-02-27T20:40:37Z | - |
dc.date.available | 2022-02-27T20:40:37Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 1369-7412 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137173 | - |
dc.description.abstract | Given an endogenous/confounded binary treatment D, a response Y with its potential versions (Y-0, Y-1) and covariates X, finding the treatment effect is difficult if Y is not continuous, even when a binary instrumental variable (IV) Z is available. We show that, for any form of Y (continuous, binary, mixed, horizontal ellipsis ), there exists a decomposition Y = mu(0)(X) + mu(1)(X)D + error with E(error|Z,X) = 0, where mu 1(X)equivalent to E(Y1-Y0|complier,X) and 'compliers' are those who get treated if and only if Z = 1. First, using the decomposition, instrumental variable estimator (IVE) is applicable with polynomial approximations for mu(0)(X) and mu(1)(X) to obtain a linear model for Y. Second, better yet, an 'instrumental residual estimator (IRE)' with Z-E(Z|X) as an IV for D can be applied, and IRE is consistent for the 'E(Z|X)-overlap' weighted average of mu(1)(X), which becomes E(Y1-Y0|complier) for randomized Z. Third, going further, a 'weighted IRE' can be done which is consistent for E{mu(1)(X)}. Empirical analyses as well as a simulation study are provided to illustrate our approaches. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.subject | SAMPLE SELECTION MODELS | - |
dc.subject | ROBUST ESTIMATION | - |
dc.subject | IDENTIFICATION | - |
dc.subject | HETEROSCEDASTICITY | - |
dc.subject | REGRESSION | - |
dc.title | Instrument residual estimator for any response variable with endogenous binary treatment* | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Myoung-jae | - |
dc.identifier.doi | 10.1111/rssb.12442 | - |
dc.identifier.scopusid | 2-s2.0-85110973736 | - |
dc.identifier.wosid | 000675490200001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, v.83, no.3, pp.612 - 635 | - |
dc.relation.isPartOf | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY | - |
dc.citation.title | JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY | - |
dc.citation.volume | 83 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 612 | - |
dc.citation.endPage | 635 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | HETEROSCEDASTICITY | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | ROBUST ESTIMATION | - |
dc.subject.keywordPlus | SAMPLE SELECTION MODELS | - |
dc.subject.keywordAuthor | effect on complier | - |
dc.subject.keywordAuthor | endogenous treatment | - |
dc.subject.keywordAuthor | heterogeneous effect | - |
dc.subject.keywordAuthor | instrumental variable estimator | - |
dc.subject.keywordAuthor | overlap weight | - |
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