RDHonest 1.0.3
Minor Improvements and Fixes
- Rewrite tests so they do not fail due to numerical tolerance issues
when MKL implementation of BLAS is used.
RDHonest 1.0.2
Minor Improvements and
Fixes
- Adjust numerical tolerance on some tests so that they don’t fail
when BLIS implementation of BLAS is used.
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.