A wide variety of important distributional hypotheses can be assessed using the empirical quantile regression processes. In this paper, a very simple and practical resampling test is offered as an alternative to inference based on Khmaladzation, as developed in Koenker and Xiao (2002). This alternative has better or competitive power, accurate size, and does not require estimation of non-parametric sparsity and score functions. It applies not only to iid but also time series data. Computational experiments and an empirical example that re-examines the effect of re-employment bonus on the unemployment duration strongly support this approach. Keywords: bootstrap, subsampling, quantile regression, quantile regression process, Kolmogorov-Smirnov test, unemployment duration. JEL Classification: C13, C14, C30, C51, D4, J24, J31.