CFDP 1375

More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors


Publication Date: June 2002

Pages: 49


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.


Time series regression, Nonparametric regression, Kernel, Efficiency

JEL Classification Codes:  C22


Published in Journal of the American Statistical Association (2003), 98(3): 980-992 [DOI]