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dc.contributor.advisorLederer, Johannes C
dc.contributor.authorGold, David Ariel
dc.date.accessioned2018-04-24T22:05:54Z
dc.date.submitted2017
dc.identifier.otherGold_washington_0250O_18216.pdf
dc.identifier.urihttp://hdl.handle.net/1773/41697
dc.descriptionThesis (Master's)--University of Washington, 2017
dc.description.abstractThis thesis concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear model for the endogenous regressors and a second-stage linear model for the response variable, we develop a novel adaptation of the parametric one-step update to a generic second-stage estimator. We provide high-level conditions under which the scaled update is asymptotically normal. We introduce a two-stage Lasso procedure and show that, under a Gaussian noise regime, the second-stage Lasso estimator satisfies the aforementioned conditions. Using these results, we construct asympotitically valid confidence intervals for the components of the second-stage regression vector. We complement our asymptotic theory with empirical studies, which demonstrate the relevance of our method in finite samples.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsnone
dc.subjectHigh-dimensional statistics
dc.subjectInstrumental variables regression
dc.subjectOne-step update
dc.subjectStatistics
dc.subject.otherStatistics
dc.titleInference for High-Dimensional Instrumental Variables Regression
dc.typeThesis
dc.embargo.termsRestrict to UW for 1 year -- then make Open Access
dc.embargo.lift2019-04-24T22:05:54Z


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