Boosting Estimation of RBF Neural Networks for Dependent Data

George Kapetanios and Andrew Blake

No 588, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: Neural Networks; Boosting (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2007-03-01
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:588

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