Weighted bootstrap consistency for matching estimators: The role of bias-correction

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dc.contributor.authorWalsh, Christopher
dc.contributor.authorJentsch, Carsten
dc.contributor.authorHossain, Shaikh Tanvir
dc.date.accessioned2021-03-16T14:54:50Z
dc.date.available2021-03-16T14:54:50Z
dc.date.issued2021
dc.description.abstractWe show that the purpose of consistent bias-correction for matching estimators of treatment effects is two-fold. Firstly, it is known to improve point estimation to get rid of asymptotically non-negligible bias terms. Secondly, point estimates, it will also distort inference leading e.g. to invalid confidence intervals. In simulations, we show that the choice of the bias-correction estimator that practitioners still have to make, can severely affect the weighted bootstrap’s performance when estimating the asymptotic variance in finite samples. In particular, simple rules such as estimating the bias based on linear regressions in the treatment arms can lead to very poor weighted bootstrap based variance estimates.en
dc.identifier.urihttp://hdl.handle.net/2003/40083
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-21960
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;8/2021
dc.subjectATEde
dc.subjectbias-correctionen
dc.subjectwild bootstrapen
dc.subjectweighted bootstrapen
dc.subjectbootstrap consistencyen
dc.subjectmatching estimatoren
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleWeighted bootstrap consistency for matching estimators: The role of bias-correctionen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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