Boosting Estimation of RBF Neural Networks for Dependent Data
This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specification testing illustrates the usefulness of the results.
|Date of creation:||Mar 2007|
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- Andrew Blake, 2001.
"A Timeless Perspective on Optimality in Forward-Looking Rational Expectations Models,"
NIESR Discussion Papers
188, National Institute of Economic and Social Research.
- Blake, Andrew P., 2002. "A 'Timeless Perspective' on Optimality in Forward-Looking Rational Expectations Models," Royal Economic Society Annual Conference 2002 30, Royal Economic Society.
- Guay, Alain & Guerre, Emmanuel, 2006. "A Data-Driven Nonparametric Specification Test For Dynamic Regression Models," Econometric Theory, Cambridge University Press, vol. 22(04), pages 543-586, August.
- Emmanuel Guerre & Pascal Lavergne, 2004. "Data-Driven Rate-Optimal Specification Testing In Regression Models," Econometrics 0411008, EconWPA. Full references (including those not matched with items on IDEAS)
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