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.
Xiao, Zhijie; Linton, Oliver B.; Carroll, Raymond J.; and Mammen, E., "More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors" (2002). Cowles Foundation Discussion Papers. 1639.