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Robust Tests for Heteroscedasticity in a general Framework

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  • Marie Lebreton
  • Anne Peguin-Feissolle

Abstract

In this paper, we suggest two heteroscedasticity tests that require little knowledge of the functional relationship determining the variance. The first one is based on a Taylor series expansion of the unknown scedastic function and the second one is based on artificial neural networks. These tests are easy to apply and perform well in our small sample simulations, but they possess asymptotically incorrect sizes except in the case of normal errors. Therefore, we propose a simple modification in order to correct this non-robustness property. We investigate the size and the power of these tests by Monte Carlo experiments by comparing them to well-known heteroscedasticity tests.

Suggested Citation

  • Marie Lebreton & Anne Peguin-Feissolle, 2007. "Robust Tests for Heteroscedasticity in a general Framework," Annals of Economics and Statistics, GENES, issue 85, pages 159-187.
    • Handle: RePEc:adr:anecst:y:2007:i:85:p:159-187
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    File URL: http://www.jstor.org/stable/20079184
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    Cited by:

    1. Chuffart Thomas & Flachaire Emmanuel & Péguin-Feissolle Anne, 2018. "Testing for misspecification in the short-run component of GARCH-type models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-17, December.
    2. Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101.
    3. Vanessa Berenguer Rico & Ines Wilms, 2018. "White heteroscedasticty testing after outlier removal," Economics Series Working Papers 853, University of Oxford, Department of Economics.

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