  In this paper we describe the testing procedure for assessing the statistical
significance of treatment effect under the experimental conditions of baseline
imbalance across covariates and attrition from the survey, using the
permutation tests proposed by Freedman and Lane (1983) and Romano and Wolf
(2016). We discuss the testing procedure for these hypotheses based on a linear
regression model and introduce the new Stata command [R] permtest for the
implementation of the permutation test in Stata. Moreover, we investigate the
finite-sample performance as well as the statistical validity of the test with
a Monte Carlo simulation study in which we examine the empirical size and power
properties under the conditions of baseline imbalance and attrition for a fixed
number of permutation steps.
