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Applied nonparametric methods


  • HÄRDLE, Wolfgang

    (CORE, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium and CentER, Tilburg)


In this note we review different approaches to non parametric regression. Kernel estimators are motivated from local averaging, solving ill-posed problems and weighting of binned data. Kernel estimators are compared to k-NN estimators and splines. The choice of smoothing is discussed and finally the method is applied for nonparametric prediction of time series.

Suggested Citation

  • HÄRDLE, Wolfgang, 1992. "Applied nonparametric methods," CORE Discussion Papers 1992003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:1992003

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    References listed on IDEAS

    1. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics,in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339 Elsevier.
    2. HARDLE, Wolfgang & SCOTT, David, 1990. "Smoothing by weighted averaging of rounded points," CORE Discussion Papers 1990040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other


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