quantile regression model coefficient CIs using the empirical variance distribution approximation

This paper investigates the application of the empirical variance distribution (evd) function to estimate the confidence interval bounds of the quantile regression model coefficient estimates for homoscedastic unweighted data.<br>It can be seen that for this modest sample size, the bootstrap estimator and the two evd based confidence interval estimators exhibit nominal 95% coverage for homoscedastic iid cases in slope estimates and &gt;93% in the intercept estimates. For extreme quantiles in smaller samples or when homoscedastic iid error is not present, the coverage performance is lower.