Randomization Tests // A collection of randomization tests, data
sets and examples. The current version focuses on five testing
problems and their implementation in empirical work. First, it
facilitates the empirical researcher to test for particular
hypotheses, such as comparisons of means, medians, and
variances from k populations using robust permutation tests,
which asymptotic validity holds under very weak assumptions,
while retaining the exact rejection probability in finite
samples when the underlying distributions are identical.
Second, the description and implementation of a permutation
test for testing the continuity assumption of the baseline
covariates in the sharp regression discontinuity design (RDD)
as in Canay and Kamat (2018) https://goo.gl/UZFqt7. More
specifically, it allows the user to select a set of covariates
and test the aforementioned hypothesis using a permutation test
based on the Cramer-von Misses test statistic. Graphical
inspection of the empirical CDF and histograms for the
variables of interest is also supported in the package. Third,
it provides the practitioner with an effortless implementation
of a permutation test based on the martingale decomposition of
the empirical process for testing for heterogeneous treatment
effects in the presence of an estimated nuisance parameter as
in Chung and Olivares (2021) doi:10.1016/j.jeconom.2020.09.015.
Fourth, this version considers the two-sample goodness-of-fit
testing problem under covariate adaptive randomization and
implements a permutation test based on a prepivoted Kolmogorov-
Smirnov test statistic. Lastly, it implements an asymptotically
valid permutation test based on the quantile process for the
hypothesis of constant quantile treatment effects in the
presence of an estimated nuisance parameter.