Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/52091 
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dc.contributor.authorde Luna, Xavieren
dc.contributor.authorJohansson, Peren
dc.contributor.authorSjöstedt-de Luna, Saraen
dc.date.accessioned2011-10-17-
dc.date.accessioned2011-11-23T11:43:48Z-
dc.date.available2011-11-23T11:43:48Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/52091-
dc.description.abstractAbadie and Imbens (2008, Econometrica) showed that classical bootstrap schemes fail to provide correct inference for K-nearest neighbour (KNN) matching estimators of average causal effects. This is an interesting result showing that bootstrap should not be applied without theoretical justification. In this paper, we present two resampling schemes, which we show provide valid inference for KNN matching estimators. We resample estimated individual causal effects (EICE), i.e. the difference in outcome between matched pairs, instead of the original data. Moreover, by taking differences in EICEs ordered with respect to the matching covariate, we obtain a bootstrap scheme valid also with heterogeneous causal effects where mild assumptions on the heterogeneity are imposed. We provide proofs of the validity of the proposed resampling based inferences. A simulation study illustrates finite sample properties.en
dc.language.isoengen
dc.publisher|aInstitute for the Study of Labor (IZA) |cBonnen
dc.relation.ispartofseries|aIZA Discussion Papers |x5361en
dc.subject.jelC14en
dc.subject.jelC21en
dc.subject.ddc330en
dc.subject.keywordblock bootstrapen
dc.subject.keywordsubsamplingen
dc.subject.keywordaverage causal/treatment effecten
dc.titleBootstrap inference for K-nearest neighbour matching estimators-
dc.typeWorking Paperen
dc.identifier.ppn670045586en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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