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Bootstrapping heteroskedasticity consistent covariance matrix estimator

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Summary

Recent results of Cribari-Neto and Zarkos (1999) show that bootstrap methods can be successfully used to estimate a heteroskedasticity robust covariance matrix estimator. In this paper, we show that the wild bootstrap estimator can be calculated directly, without simulations, as it is just a more traditional estimator. Their experimental results seem to conflict with those of MacKinnon and White (1985); we reconcile these two results.

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Notes

  1. 1x = −2.2824, −0.435864, 2.27108, −1.05705, −1.10142, 0.648927, 0.143281, −0.25922, 1.87924, −1.32969, 0.013618, −0.303695, 1.24507, 0.670023, 0.658823, 0.521237, −0.0656568, −0.370603, −0.0734635, −0.169986

References

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Authors and Affiliations

  1. Maison des Sciences Économiques, Université Paris I Panthéon-Sorbonne Eurequa, 106-112 bd de l’Hopital, 75013, Paris, France

    Emmanuel Flachaire

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  1. Emmanuel Flachaire
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Flachaire, E. Bootstrapping heteroskedasticity consistent covariance matrix estimator. Computational Statistics 17, 501–506 (2002). https://doi.org/10.1007/s001800200122

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  • DOI: https://doi.org/10.1007/s001800200122

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