Quantile Regression under Limited Dependent Variable in Stata

Abstract

This article develops a Stata command, ldvqreg, to estimate quantile regression models for the cases of censored (with lower and/or upper censoring) and binary dependent variables. The estimator is implemented using a smoothed version of the quantile regression objective function. Simulation exercises show that it correctly estimates the parameters and it should be implemented instead of the available quantile regression methods when censoring is present. Different empirical applications illustrate these methods.