CFDP 1375
More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors
Author(s):Publication Date: June 2002
Pages: 49
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 Codes: C22
Note:
Published in Journal of the American Statistical Association (2003), 98(3): 980-992 [DOI]