# Quantile Regression with Regression Discontinuity

Sort of a methodological question: If one has an exogenous binary treatment and a continuous outcome variable Y and wants to estimate quantile treatment effects by exploiting a (sharp) discontinuity in the treatment, what is the advantage of using non-parametric quantile RDD (specifically this: http://www.sciencedirect.com/science/article/pii/S0304407612000607 by Frandsen, Frolich and Melly (2010) but in general any non-parametric method) vs. the normal quantile regression (the one we would find in qreg in stata, for instance).

It seems to me that since the treatment is exogenous and the discontinuity is "sharp", the normal qreg command would still give nice estimates of the quantile effects? Am I missing something here? The one advantage I see is that the non-parametric method estimates the entire distribution around the discontinuity which is more desirable but any other advantages?