Local Polynomials vs Neural Networks: some empirical evidences

Giordano Francesco () and Parrella Maria Lucia
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Giordano Francesco: Dep. Economic Science and Statistics University of Salerno
Parrella Maria Lucia: University of Salerno

No 396, Computing in Economics and Finance 2006 from Society for Computational Economics

Abstract: 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.

Keywords: kernel estimators; neural networks; nonlinear time series (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2006-07-04
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecfa:396

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