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).RDScatterRDSmoothnessBound computes a lower bound
on the smoothness constant M, used as a parameter by
RDHonest to calculate the worst-case bias of the
estimatorRDTEfficiencyBound 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.mirror server hosted at Truenetwork, Russian Federation.