Nonparametric transformation to white noise

Oliver Linton and Enno Mammen

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

Keywords: Efficiency; Inverse Problem; Kernel Estimation; Nonparametric regression; Time Series; Unit Roots. (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2006-08
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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http://eprints.lse.ac.uk/4426/ Open access version. (application/pdf)

Related works:
Journal Article: Nonparametric transformation to white noise (2008)
Working Paper: Nonparametric Transformation to White Noise (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:4426

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