Boosting Estimation of RBF Neural Networks for Dependent Data
AbstractThis 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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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 588.
Date of creation: Mar 2007
Date of revision:
Neural Networks; Boosting;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-03-10 (All new papers)
- NEP-CMP-2007-03-10 (Computational Economics)
- NEP-ECM-2007-03-10 (Econometrics)
- NEP-ETS-2007-03-10 (Econometric Time Series)
- NEP-SOC-2007-03-10 (Social Norms & Social Capital)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Andrew P Blake & George Kapetanios, 1999.
"A Radial Basis Function Artificial Neural Network Test for ARCH,"
NIESR Discussion Papers
154, National Institute of Economic and Social Research.
- Blake, Andrew P. & Kapetanios, George, 2000. "A radial basis function artificial neural network test for ARCH," Economics Letters, Elsevier, vol. 69(1), pages 15-23, October.
- 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.
- 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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