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Boosting Estimation of RBF Neural Networks for Dependent Data

  • George Kapetanios

    ()

    (Queen Mary, University of London)

  • Andrew P. Blake

    ()

    (Bank of England)

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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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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Web page: http://www.econ.qmul.ac.uk

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  1. n/a, 2001. "A Timeless Perspective on Optimality in Forward-Looking Rational Expectations Models," NIESR Discussion Papers 154, National Institute of Economic and Social Research.
  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.
  3. Emmanuel Guerre & Pascal Lavergne, 2004. "Data-Driven Rate-Optimal Specification Testing In Regression Models," Econometrics 0411008, EconWPA.
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