Nonparametric regression and regression-discontinuity designs suffer from smoothing bias that distorts conventional confidence intervals. Solutions based on robust bias correction (RBC) are now central to the economist's toolbox. In this paper, we establish a novel connection between RBC methods and bootstrap prepivoting. Revisiting RBC through the lens of bootstrapping allows us to develop a novel bias correction procedure which delivers improved nonparametric inference. The resulting confidence intervals are 17% shorter than the usual intervals employed in curve estimation and regression discontinuity designs, without compromising asymptotic coverage. This holds regardless of evaluation point location, bandwidth choice, or regressor and error distribution.

翻译：非参数回归与断点回归设计存在平滑偏差问题，这会扭曲传统的置信区间。基于稳健偏差校正的解决方案已成为经济学家工具箱的核心方法。本文建立了稳健偏差校正方法与自助法预枢轴化之间的新联系。通过自助法的视角重新审视稳健偏差校正，我们开发出一种新型偏差校正程序，能够实现改进的非参数推断。所得置信区间比曲线估计和断点回归设计中通常采用的区间缩短17%，且不损害渐近覆盖概率。这一结论不受评估点位置、带宽选择、回归量或误差分布的影响。