Robust Inference for Convex Pairwise Difference Estimators (opens in new tab)

arXiv:2510.05991v2 Announce Type: replace-cross Abstract: This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-parameter function over observation pairs with similar covariates, where the similarity is governed by a localization (bandwidth) parameter. While classical results establish asymptotic normality under restrictive bandwidth conditions, we show tha...