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dc.contributor.authorHall, Peteren_US
dc.contributor.authorHorowitz, Joelen_US
dc.description.abstractStandard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias, which generally is not estimated consistently when using the bootstrap and conventionally smoothed function estimators. To overcome this problem it is common practice to either undersmooth, so as to reduce the impact of bias, or oversmooth, and thereby introduce an explicit or implicit bias estimator. However, these approaches, and others based on nonstandard smoothing methods, complicate the process of inference, for example by requiring the choice of new, unconventional smoothing parameters and, in the case of undersmoothing, producing relatively wide bands. In this paper we suggest a new approach, which exploits to our advantage one of the difficulties that, in the past, has prevented an attractive solution to this problem - the fact that the standard bootstrap bias estimator suffers from relatively high-frequency stochastic error. The high frequency, together with a technique based on quantiles, can be exploited to dampen down the stochastic error term, leading to relatively narrow, simple-to-construct confidnce bands.en_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP14/12en_US
dc.subject.keywordconfidence intervalen_US
dc.subject.keywordconservative coverageen_US
dc.subject.keywordcoverage erroren_US
dc.subject.keywordkernel methodsen_US
dc.subject.keywordstatistical smoothingen_US
dc.titleA simple bootstrap method for constructing nonparametric confidence bands for functionsen_US
dc.typeWorking Paperen_US

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