More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors
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.
|Date of creation:||Jun 2002|
|Date of revision:|
|Publication status:||Published in Journal of Econometrics (February 2010), 154(2): 186-202|
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|Order Information:|| Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA|
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