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More efficient kernel estimation in nonparametric regression with autocorrelated errors

Author

Listed:
  • Carroll, Raymond J
  • Linton, Oliver
  • Mammen, Enno
  • Xiao, Zhijie

Abstract

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.

Suggested Citation

  • Carroll, Raymond J & Linton, Oliver & Mammen, Enno & Xiao, Zhijie, 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.
    • Handle: RePEc:ehl:lserod:2017
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    Cited by:

    1. Dabo-Niang, Sophie & Francq, Christian & Zakoian, Jean-Michel, 2009. "Combining parametric and nonparametric approaches for more efficient time series prediction," MPRA Paper 16893, University Library of Munich, Germany.
    2. Dabo-Niang, Sophie & Francq, Christian & Zakoïan, Jean-Michel, 2010. "Combining Nonparametric and Optimal Linear Time Series Predictions," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1554-1565.

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    JEL classification:

    • 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

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