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Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean

  • Blake, Andrew P.
  • Kapetanios, George

Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlinearity on the properties of ARCH tests. This paper provides some such evidence and also new ARCH testing procedures that are robust to the presence of neglected nonlinearity. Monte Carlo evidence shows that the problem is serious and that the new methods alleviate this problem to a very large extent.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 137 (2007)
Issue (Month): 2 (April)
Pages: 472-488

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Handle: RePEc:eee:econom:v:137:y:2007:i:2:p:472-488
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  1. Andrew Blake, 2001. "A Timeless Perspective on Optimality in Forward-Looking Rational Expectations Models," NIESR Discussion Papers 188, National Institute of Economic and Social Research.
  2. Bera, Anil K & Higgins, Matthew L & Lee, Sangkyu, 1992. "Interaction between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 133-42, April.
  3. Anne Peguin-Feissolle, 1999. "A comparison of the power of some tests for conditional heteroscedasticity," Post-Print halshs-00390157, HAL.
  4. Bera, Anil K & Higgins, Matthew L, 1997. "ARCH and Bilinearity as Competing Models for Nonlinear Dependence," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 43-50, January.
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