Local Polynomials vs Neural Networks: some empirical evidences
AbstractIn 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 â€œplug-inâ€ 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 396.
Date of creation: 04 Jul 2006
Date of revision:
kernel estimators; neural networks; nonlinear time series;
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- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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