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No. 588: Boosting Estimation of RBF Neural Networks for Dependent Data

George Kapetanios , Queen Mary, University of London
Andrew P. Blake , Bank of England

March 1, 2007

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Abstract

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.

J.E.L classification codes: C12, C13, C22

Keywords:Neural Networks, Boosting

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