  This paper develops a semi-parametric procedure for estimation of
unconditional quantile partial effects using quantile regression coefficients.
The estimator is based on an identification result showing that, for continuous
covariates, unconditional quantile effects are a weighted average of
conditional ones at particular quantile levels that depend on the covariates.
We propose a two-step estimator for the unconditional effects where in the
first step one estimates a structural quantile regression model, and in the
second step a nonparametric regression is applied to the first step
coefficients. We establish the asymptotic properties of the estimator, say
consistency and asymptotic normality. Monte Carlo simulations show numerical
evidence that the estimator has very good finite sample performance and is
robust to the selection of bandwidth and kernel. To illustrate the proposed
method, we study the canonical application of the Engel's curve, i.e. food
expenditures as a share of income.
