Improved inference for nonparametric regression and regression-discontinuity designs

TL;DR

This paper introduces a new bias correction method for nonparametric regression and regression-discontinuity designs, leveraging bootstrap prepivoting to produce shorter confidence intervals with reliable coverage.

Contribution

It establishes a novel connection between robust bias correction and bootstrap prepivoting, leading to improved inference procedures that are robust across various settings.

Findings

Confidence intervals are 17% shorter than traditional methods.

The new method maintains asymptotic coverage regardless of settings.

Improved inference is achieved without sacrificing accuracy.

Abstract

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.