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A Necessary and Sufficient Condition for Size Controllability of Heteroskedasticity Robust Test Statistics

Benedikt M. Pötscher, David Preinerstorfer

arXiv 23 Dec 2024 · Mathematics — Statistics Theory

arXiv:2412.17470 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We revisit size controllability results in P\"otscher and Preinerstorfer (2025) concerning heteroskedasticity robust test statistics in regression models. For the special, but important, case of testing a single restriction (e.g., a zero restriction on a single coefficient), we povide a necessary and sufficient condition for size controllability, whereas the condition in P\"otscher and Preinerstorfer (2025) is, in general, only sufficient (even in the case of testing a single restriction).

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Pötscher, B. M. and Preinerstorfer, D (2025) Valid heteroskedasticity robust testing self0.83978659%
2Pötscher, B. M. and Preinerstorfer, D (2018) Controlling the size of autocorrelation robust tests self0.63827426%
3Eicker, F (1963) Asymptotic normality and consistency of the least squares estimators for families of linear regressions0.51121100%
4Eicker, F (1967) Limit theorems for regressions with unequal and dependent errors0.51121100%
5Pötscher, B. M. and Preinerstorfer, D (2023) How reliable are bootstrap-based heteroskedasticity robust tests? self0.51121100%
6White, H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity0.51121100%
7Bell, R. M. and McCaffrey, D (2002) Bias reduction in standard errors for linear regression with multi-stage samples0.40511100%
8Chesher, A. and Jewitt, I (1987) The bias of a heteroskedasticity consistent covariance matrix estimator0.40511100%
9Chu, J., Lee, T.-H., Ullah, A. and Xu, H (2021) Exact distribution of the F-statistic under heteroskedasticity of unknown form for improved inference0.40511100%
10Cribari-Neto, F (2004) Asymptotic inference under heteroskedasticity of unknown form0.40511100%

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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

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1Inference with few treated units0.40511