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More Efficient Tests Robust to Heteroskedasticity of Unknown Form


  • Emmanuel Flachaire


In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient, and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits asymptotically correct inference. This estimator is widely used in practice. Cragg (1983) proposed a more efficient estimator, but concluded that tests basd on it are unreliable. Thus, this last estimator has not been used in practice. This article is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggest that reliable and more efficient tests can be obtained with the Cragg estimators in small samples.

Suggested Citation

  • Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
  • Handle: RePEc:taf:emetrv:v:24:y:2005:i:2:p:219-241 DOI: 10.1081/ETC-200067942

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    References listed on IDEAS

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    Cited by:

    1. Olivier Armantier, 2006. "Estimates of Own Lethal Risks and Anchoring Effects," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 37-56, January.
    2. Torben Klarl, 2014. "Is Spatial Bootstrapping A Panacea For Valid Inference?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 304-312, March.


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