Heteroskedasticity variance estimators exhibit a predictable direction of bias
Heteroskedasticity and clustered variance estimators frequently exhibit predictable directions of bias, such as underestimating standard errors in specific small-sample or non-standard settings.
The claim addresses whether heteroskedasticity/covariance variance estimators display predictable directions of bias. Paper [6] explicitly details how conventional variance estimators result in standard errors biased low under specific conditions (small clusters). Paper [10] examines standard error bias in heteroskedastic models across different estimators and sample sizes. The retrieved literature thus supports the idea that biases in these estimators follow predictable patterns depending on sample size and structural conditions.
Austin PC. A Comparison of Variance Estimators for Logistic Regression Models Estimated Using Generalized Estimating Equations (GEE) in the Context of Observational Health Services Research.. 2024. https://doi.org/10.1002/sim.10260
Paper [6] notes that conventional variance estimators, such as the Liang-Zeger estimator, systematically underestimate standard errors (exhibiting a predictable downward bias) when cluster counts are small.
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Rajh-Weber H, Huber SE, Arendasy M. A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions.. 2025. https://doi.org/10.3758/s13428-025-02801-4
Paper [10] evaluates standard error bias under heteroskedasticity and finds that classical estimators and specific HC alternatives display predictable patterns of bias depending on the scenario and sample size.
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