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Local Polynomials vs Neural Networks: some empirical evidences


  • Giordano Francesco

    () (Dep. Economic Science and Statistics University of Salerno)

  • Parrella Maria Lucia

    (University of Salerno)


In the context of Local Polynomial estimators the global bandwidth parameter takes one of most important roles. There are several methods to get a consistent estimator for it. In particular, starting from the Mean Square Error of Local Polynomial estimators, the “plug-in†method is often used. So, we propose to estimate this global bandwidth parameter via a Neural Network approach for models of conditional mean functions in a proper nonlinear time series environment. Further the problem is to evaluate some functionals which depend on unknown quantities such as: the derivatives of the unknown conditional mean function, the conditional variance and the density function of the data generating process.

Suggested Citation

  • Giordano Francesco & Parrella Maria Lucia, 2006. "Local Polynomials vs Neural Networks: some empirical evidences," Computing in Economics and Finance 2006 396, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:396

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    More about this item


    kernel estimators; neural networks; nonlinear time series;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General


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