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dc.contributor.authorLinton, O.
dc.contributor.authorXiao, Z.
dc.date.accessioned2019-04-11T14:39:22Z
dc.date.available2019-04-11T14:39:22Z
dc.date.issued2019-01-15
dc.identifier.otherCWPE1907
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/291513
dc.description.abstractWe study the efficient estimation of nonparametric regression in the presence of heteroskedasticity. We focus our analysis on local polynomial estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. We show that, although traditionally it is adviced that one should not weight for heteroskedasticity in nonparametric regressions, in many popular nonparametric regression models our method has lower asymptotic variance than the usual unweighted procedures. We conduct a Monte Carlo investigation that confirms the efficiency gain over conventional nonparametric regression estimators infinite samples.
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectEfficiency
dc.subjectHeteroskedasticity
dc.subjectLocal Polynomial Estimation
dc.subjectNonparametric Regression.
dc.titleEfficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.38673


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