Robust Two-Sample Mean Inference under Serial Dependence

Ulrich Hounyo and Min Seong Kim

Papers from arXiv.org

Abstract: We propose robust two-sample tests for comparing means in time series. The framework accommodates a wide range of applications, including structural breaks, treatment-control comparisons, and group-averaged panel data. We first consider series HAR two-sample t-tests, where standardization employs orthonormal basis projections, ensuring valid inference under heterogeneity and nonparametric dependence structures. We propose a Welch-type t-approximation with adjusted degrees of freedom to account for long-run variance heterogeneity across the series. We further develop a series-based HAR wild bootstrap test, extending traditional wild bootstrap methods to the time-series setting. Our bootstrap avoids resampling blocks of observations and delivers superior finite-sample performance.

Date: 2025-12, Revised 2025-12
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