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 Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2002/435.
Date of creation: Jun 2002
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Backfitting; efficiency; kernel estimation; time series.;
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," LSE Research Online Documents on Economics 2017, London School of Economics and Political Science, LSE Library.
- Zhijie Xiao & Oliver Linton & Raymond J. Carroll & E. Mammen, 2002. "More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors," Cowles Foundation Discussion Papers 1375, Cowles Foundation for Research in Economics, Yale University.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-11-03 (All new papers)
- NEP-ECM-2003-11-03 (Econometrics)
- NEP-ETS-2003-11-03 (Econometric Time Series)
- NEP-MFD-2003-11-03 (Microfinance)
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