Nonparametric Inference on Quantile Marginal Effects

We propose a nonparametric method to construct confidence intervals for quantile marginal effects (i.e., derivatives of the conditional quantile function). Under certain conditions, a quantile marginal effect equals a causal (structural) effect in a general nonseparable model, or equals an average thereof within a particular subpopulation. The high- order accuracy of our method is derived. Simulations and an empirical example demonstrate the new method's favorable performance and practical use. Code for the new method is provided.