More efficient kernel estimation in nonparametric regression with autocorrelated errors

Raymond J Carroll, Oliver Linton, Enno Mammen and Zhijie Xiao

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

Abstract: We propose a modification of kernel time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that has to be estimated from the data. We establish the asymptotic distribution of our estimator under weak dependence conditions. It is shown that the proposed estimation procedure is more efficient than the conventional kernel method. We also provide simulation evidence to suggest that gains can be achieved in moderate sized samples.

Keywords: Backfitting; efficiency; kernel estimation; time series (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2002-06
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://eprints.lse.ac.uk/2017/ Open access version. (application/pdf)

Related works:
Working Paper: More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors (2002)
Working Paper: More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2017

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