More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors
AbstractWe 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.
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Bibliographic InfoPaper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1375.
Length: 49 pages
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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Other versions of this item:
- Raymond J Carroll & Oliver Linton & Enno Mammen & Zhijie Xiao, 2002. "More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors," STICERD - Econometrics Paper Series /2002/435, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- B12 - Schools of Economic Thought and Methodology - - History of Economic Thought through 1925 - - - Classical (includes Adam Smith)
- B31 - Schools of Economic Thought and Methodology - - History of Economic Thought: Individuals - - - Individuals
- D03 - Microeconomics - - General - - - Behavioral Economics; Underlying Principles
This paper has been announced in the following NEP Reports:
- NEP-ECM-2002-10-18 (Econometrics)
- NEP-ETS-2002-10-18 (Econometric Time Series)
- NEP-RMG-2002-10-18 (Risk Management)
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