文章基本信息

标题：Nonparametric Transformation to White Noise
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作者：Oliver Linton, Enno Mammen
期刊名称：International Studies Papers
出版年度：2006
卷号：2006
期号：aug
出版社：Suntory Toyota International Centres for Economics and Related Disciplines
摘要：We consider a semiparametric distributed lag model in which the “news impact curve” m is nonparametric but the response is dynamic through some linear filters. A special case of this is a nonparametric regression with serially correlated errors. We propose an estimator of the news impact curve based on a dynamic transformation that produces white noise errors. This yields an estimating equation for m that is a type two linear integral equation. We investigate both the stationary case and the case where the error has a unit root. In the stationary case we establish the pointwise asymptotic normality. In the special case of a nonparametric regression subject to time series errors our estimator achieves efficiency improvements over the usual estimators, see Xiao, Linton, Carroll, and Mammen (2003). In the unit root case our procedure is consistent and asymptotically normal unlike the standard regression smoother. We also present the distribution theory for the parameter estimates, which is non-standard in the unit root case. We also investigate its finite sample performance through simulation experiments.
关键词：Efficiency; Inverse Problem; Kernel Estimation; Nonparametric regression; Time Series; Unit Roots