A beta-based heteroskedasticity-consistent covariance matrix estimator

This paper introduces an adaptive leverage correction for heteroskedasticity-consistent covariance matrix estimation in ordinary least squares regression. Unlike existing estimators, which rely on predetermined adjustment functions, the proposed approach uses a fitted Beta distribution calibrated to the design matrix's empirical leverage structure. Its parameters are estimated from observed leverage values, allowing the adjustment to reflect the overall configuration rather than individual values alone. The estimator accommodates heterogeneous leverage patterns while avoiding excessive growth of adjustment factors. Monte Carlo simulations show accurate finite-sample inference and confidence interval coverage, alongside desirable asymptotic properties. Empirical applications illustrate its advantages with influential observations and overshooting by existing estimators. The open-source R package hcinfer implements the estimator and related procedures.