Efficient Estimation of Nonparametric Regression in The Presence of Dynamic Heteroskedasticity

Oliver Linton and Zhijie Xiao

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: We 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.

Keywords: Efficiency; Heteroskedasticity; Local Polynomial Estimation; Nonparametric Regression. (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2019-01-15
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: obl20
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Citations: View citations in EconPapers (4)

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Journal Article: Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:1907

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