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Boosting Estimation of RBF Neural Networks for Dependent Data

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Author Info
George Kapetanios () (Queen Mary, University of London)
Andrew P. Blake () (Bank of England)

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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.

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File URL: http://www.econ.qmul.ac.uk/papers/doc/wp588.pdf
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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 588.

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Date of creation: Mar 2007
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Handle: RePEc:qmw:qmwecw:wp588

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Related research
Keywords: Neural Networks; Boosting;

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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