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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Date of creation: Jun 2002
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Backfitting; efficiency; kernel estimation; time series.;
Other versions of this item:
- 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.
- 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 Microeconomics; Underlying Principles
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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