NEWS.md

In RDHonest: Honest Inference in Regression Discontinuity Designs

RDHonest 1.0.1

Minor improvements and fixes

• Use covariate-adjusted outcome to compute nearest-neighbor variance estimator
• Drop collinear covariates automatically instead of throwing an error

RDHonest 1.0.0

New Features

• The function RDHonest computes estimates and confidence intervals for the regression discontinuity (RD) parameter in sharp and fuzzy designs. It supports covariates, clustering, and weighting. Confidence intervals are honest (or bias-aware), with critical values computed using the CVb function. Worst-case bias of the estimator is computed under either the Taylor or Hölder smoothness class.
• RDHonestBME computes confidence intervals in sharp RD designs with discrete covariates under the assumption assumption that the conditional mean lies in the "bounded misspecification error" class of functions, as considered in Kolesár and Rothe (2018).
• Support for plotting the data is provided by the function RDScatter
• The function RDSmoothnessBound computes a lower bound on the smoothness constant M, used as a parameter by RDHonest to calculate the worst-case bias of the estimator
• The function RDTEfficiencyBound calculates efficiency of minimax one-sided CIs at constant functions, or efficiency of two-sided fixed-length CIs at constant functions under second-order Taylor smoothness class.

RDHonest documentation built on April 4, 2025, 2:55 a.m.