More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors

50 Pages Posted: 12 Aug 2002

See all articles by Zhijie Xiao

Zhijie Xiao

University of Illinois at Urbana-Champaign - Department of Economics

Oliver B. Linton

University of Cambridge

Raymond Carroll

Texas A&M University - Department of Statistics

Enno Mammen

University of Mannheim - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: June 2002

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: Time Series Regression, Nonparametric Regression, Kernel, Efficiency

JEL Classification: C22

Suggested Citation

Xiao, Zhijie and Linton, Oliver B. and Carroll, Raymond and Mammen, Enno, More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors (June 2002). Available at SSRN: https://ssrn.com/abstract=322163

Zhijie Xiao (Contact Author)

University of Illinois at Urbana-Champaign - Department of Economics ( email )

410 David Kinley Hall
1407 W. Gregory
Urbana, IL 61801
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Oliver B. Linton

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

Raymond Carroll

Texas A&M University - Department of Statistics ( email )

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College Station, TX 77843
United States
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Enno Mammen

University of Mannheim - Department of Economics ( email )

Mannheim, 68131
Germany