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VALID HETEROSKEDASTICITY ROBUST TESTING

Published online by Cambridge University Press:  11 September 2023

Benedikt M. Pötscher*
Affiliation:
University of Vienna
David Preinerstorfer
Affiliation:
University of St. Gallen
*
Address correspondence to Benedikt Pötscher, Department of Statistics, University of Vienna, A-1090 Oskar-Morgenstern Platz 1, Vienna, Austria; e-mail: benedikt.poetscher@univie.ac.at.
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Abstract

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Tests based on heteroskedasticity robust standard errors are an important technique in econometric practice. Choosing the right critical value, however, is not simple at all: conventional critical values based on asymptotics often lead to severe size distortions, and so do existing adjustments including the bootstrap. To avoid these issues, we suggest to use smallest size-controlling critical values, the generic existence of which we prove in this article for the commonly used test statistics. Furthermore, sufficient and often also necessary conditions for their existence are given that are easy to check. Granted their existence, these critical values are the canonical choice: larger critical values result in unnecessary power loss, whereas smaller critical values lead to overrejections under the null hypothesis, make spurious discoveries more likely, and thus are invalid. We suggest algorithms to numerically determine the proposed critical values and provide implementations in accompanying software. Finally, we numerically study the behavior of the proposed testing procedures, including their power properties.

Type
ARTICLES
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Footnotes

Financial support of the second author by the Program of Concerted Research Actions (ARC) of the Université libre de Bruxelles in an early stage of this project is gratefully acknowledged. We thank two referees, a Co-Editor, and the Editor for helpful comments.

References

Bakirov, N. & Székely, G. (2005) Student’s t-test for Gaussian scale mixtures. Zapiski Nauchnyh Seminarov POMI 328, 519.Google Scholar
Bakirov, N.K. (1998) Nonhomogeneous samples in the Behrens–Fisher problem. Journal of Mathematical Sciences (New York) 89, 14601467.CrossRefGoogle Scholar
Bell, R.M. & McCaffrey, D. (2002) Bias reduction in standard errors for linear regression with multi-stage samples. Survey Methodology 28, 169181.Google Scholar
Cattaneo, M.D., Jansson, M., & Newey, W.K. (2018) Inference in linear regression models with many covariates and heteroscedasticity. Journal of the American Statistical Association 113, 13501361.CrossRefGoogle Scholar
Chesher, A. & Jewitt, I. (1987) The bias of a heteroskedasticity consistent covariance matrix estimator. Econometrica 55, 12171222.CrossRefGoogle Scholar
Chesher, A.D. (1989) Hájek inequalities, measures of leverage, and the size of heteroskedasticity robust Wald tests. Econometrica 57, 971977.CrossRefGoogle Scholar
Chesher, A.D. & Austin, G. (1991) The finite-sample distributions of heteroskedasticity robust Wald statistics. Journal of Econometrics 47, 153173.CrossRefGoogle Scholar
Chu, J., Lee, T.-H., Ullah, A., & Xu, H. (2021) Exact distribution of the F-statistic under heteroskedasticity of unknown form for improved inference. Journal of Statistical Computation and Simulation 91, 17821801.CrossRefGoogle Scholar
Cragg, J.G. (1983) More efficient estimation in the presence of heteroscedasticity of unknown form. Econometrica 51, 751763.CrossRefGoogle Scholar
Cragg, J.G. (1992) Quasi-Aitken estimation for heteroscedasticity of unknown form. Journal of Econometrics 54, 179201.CrossRefGoogle Scholar
Cribari-Neto, F. (2004) Asymptotic inference under heteroskedasticity of unknown form. Computational Statistics & Data Analysis 45, 215233.CrossRefGoogle Scholar
Davidson, R. & Flachaire, E. (2008) The wild bootstrap, tamed at last. Journal of Econometrics 146, 162169.CrossRefGoogle Scholar
Davidson, R. & MacKinnon, J.G. (1985) Heteroskedasticity-robust tests in regressions directions. Ministère de l’Économie et des Finances. Institut National de la Statistique et des Études Économiques. Annales 59/60, 183218.Google Scholar
Davies, R.B. (1980) Algorithm AS 155: The distribution of a linear combination of ${\chi}^2$ random variables. Journal of the Royal Statistical Society. Series C (Applied Statistics) 29, 323333.Google Scholar
DiCiccio, C.J., Romano, J.P., & Wolf, M. (2019) Improving weighted least squares inference. Econometrics and Statistics 10, 96119.CrossRefGoogle Scholar
Duchesne, P. & de Micheaux, P.L. (2010) Computing the distribution of quadratic forms: Further comparisons between the Liu–Tang–Zhang approximation and exact methods. Computational Statistics and Data Analysis 54, 858862.CrossRefGoogle Scholar
Eicker, F. (1963) Asymptotic normality and consistency of the least squares estimators for families of linear regressions. Annals of Mathematical Statistics 34, 447456.CrossRefGoogle Scholar
Eicker, F. (1967). Limit theorems for regressions with unequal and dependent errors. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Berkeley, CA, 1965/66), Volume 1: Statistics , pp. 5982. University of California Press.Google Scholar
Flachaire, E. (2005) More efficient tests robust to heteroskedasticity of unknown form. Econometric Reviews 24, 219241.CrossRefGoogle Scholar
Godfrey, L.G. (2006) Tests for regression models with heteroskedasticity of unknown form. Computational Statistics & Data Analysis 50, 27152733.CrossRefGoogle Scholar
Hansen, B. (2021). The exact distribution of the White t-ratio. Working paper, University of Wisconsin–Madison.Google Scholar
Hinkley, D.V. (1977) Jackknifing in unbalanced situations. Technometrics 19, 285292.CrossRefGoogle Scholar
Ibragimov, R. & Müller, U.K. (2010) t-statistic based correlation and heterogeneity robust inference. Journal of Business and Economic Statistics 28, 453468.CrossRefGoogle Scholar
Ibragimov, R. & Müller, U.K. (2016) Inference with few heterogeneous clusters. The Review of Economics and Statistics 98, 8396.CrossRefGoogle Scholar
Imbens, G.W. & Kolesár, M. (2016) Robust standard errors in small samples: Some practical advice. The Review of Economics and Statistics 98, 701712.CrossRefGoogle Scholar
Kolesár, M. (2019). dfadjust: Degrees of Freedom Adjustment for Robust Standard Errors. R package version 1.0.1. https://CRAN.R-project.org/package=dfadjust.Google Scholar
Lin, E.S. & Chou, T.-S. (2018) Finite-sample refinement of GMM approach to nonlinear models under heteroskedasticity of unknown form. Econometric Reviews 37, 128.CrossRefGoogle Scholar
Long, J.S. & Ervin, L.H. (2000) Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician 54, 217224.Google Scholar
MacKinnon, J.G. (2013) Thirty years of heteroskedasticity-robust inference. In Chen, X. & Swanson, N. R. E. (eds.), Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis , pp. 437462. Springer.CrossRefGoogle Scholar
MacKinnon, J.G. & White, H. (1985) Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics 29, 305325.CrossRefGoogle Scholar
Mickey, M.R. & Brown, M.B. (1966) Bounds on the distribution functions of the Behrens–Fisher statistic. Annals of Mathematical Statistics 37, 639642.CrossRefGoogle Scholar
Phillips, P.C. (1993) Operational algebra and regression t-tests. In Phillips, P.C. (ed.), Models, Methods and Applications of Econometrics: Essays in Honor of A.R. Bergstrom , pp. 140152. Basil Blackwell.Google Scholar
Pötscher, B.M. & Preinerstorfer, D. (2018) Controlling the size of autocorrelation robust tests. Journal of Econometrics 207, 406431.CrossRefGoogle Scholar
Pötscher, B.M. & Preinerstorfer, D. (2019) Further results on size and power of heteroskedasticity and autocorrelation robust tests, with an application to trend testing. Electronic Journal of Statistics 13, 38933942.CrossRefGoogle Scholar
Pötscher, B.M. & Preinerstorfer, D. (2023) How reliable are bootstrap-based heteroskedasticity robust tests? Econometric Theory 39(4), 789847. doi:10.1017/S0266466622000184.CrossRefGoogle Scholar
Preinerstorfer, D. (2021). hrt: Heteroskedasticity Robust Testing. R package version 1.0.0.Google Scholar
Preinerstorfer, D. & Pötscher, B.M. (2016) On size and power of heteroskedasticity and autocorrelation robust tests. Econometric Theory 32, 261358.CrossRefGoogle Scholar
Robinson, G. (1979) Conditional properties of statistical procedures. Annals of Statistics 7, 742755.Google Scholar
Romano, J.P. & Wolf, M. (2017) Resurrecting weighted least squares. Journal of Econometrics 197, 119.CrossRefGoogle Scholar
Rothenberg, T.J. (1988) Approximate power functions for some robust tests of regression coefficients. Econometrica 56, 9971019.CrossRefGoogle Scholar
Satterthwaite, F.E. (1946) An approximate distribution of estimates of variance components. Biometrics Bulletin 2, 110114.CrossRefGoogle ScholarPubMed
White, H. (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817838.CrossRefGoogle Scholar
Wooldridge, J.M. (2010) Econometric Analysis of Cross Section and Panel Data , 2nd Edition. MIT Press.Google Scholar
Wooldridge, J.M. (2012) Introductory Econometrics, 5th Edition. South-Western.Google Scholar
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