This chapter focuses on the question of correcting quantile regression estimates for
nonrandom sample selection. It provides the empirical illustration in Huber and Melly, and …

Most sample selection models assume that the errors are independent of the regressors.
Under this assumption, all quantile and mean functions are parallel, which implies that …

This study uses quantile regression techniques to analyze changes in the returns to
education for women. The data used is the March Current Population Survey for the years …

Quantile regression (QR) fits a linear model for conditional quantiles just as ordinary least
squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it …

Since Quetelet's work in the nineteenth century, social science has iconified the average
man, that hypothetical man without qualities who is comfortable with his head in the oven …

The effects of policy variables on distributional outcomes beyond simple averages are of
interest in many research areas and in particular in labor economics. For instance, the …

Quantile regression is emerging as a popular statistical approach, which complements the
estimation of conditional mean models. While the latter only focuses on one aspect of the …

Since Quetelet's work in the nineteenth century, social science has iconified the average
man, that hypothetical man without qualities who is comfortable with his head in the oven …

We propose a method to correct for sample selection in quantile regression models.
Selection is modeled via the cumulative distribution function, or copula, of the percentile …

I consider identification of nonparametric quantile regressions with endogenous regressors
and an excluded instrumental variable. This model has an outcome equation that is both …