More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors
50 Pages Posted: 12 Aug 2002
There are 2 versions of this paper
More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors
More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors
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: Suggested Citation
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