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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)

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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Bibliographic Info

Paper provided by Queen Mary, University of London, School of Economics and Finance 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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Keywords: Neural Networks; Boosting;

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  1. 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.
  2. 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.
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Cited by:
  1. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary, University of London, School of Economics and Finance.
  2. George Kapetanios, 2007. "A Test for Serial Dependence Using Neural Networks," Working Papers 609, Queen Mary, University of London, School of Economics and Finance.

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