Quantile Regression Estimation of Panel Duration Models with Censored Data

This paper studies the estimation of quantile regression panel duration models. We allow for the possibility of endogenous covariates and correlated individual effects in the quantile regression models. We propose a quantile regression approach for panel duration models under conditionally independent censoring. The procedure involves minimizing ℓ1 convex objective functions and is motivated by a martingale property associated with survival data in models with endogenous covariates. We carry out a series of Monte Carlo simulations to investigate the small sample performance of the proposed approach in comparison with other existing methods. An empirical application of the method to the analysis of the effect of unemployment insurance on unemployment duration illustrates the approach.