Quantile regression with generated dependent variable and covariates

TL;DR

This paper develops a theoretical framework for linear quantile regression models where key variables are estimated in a preliminary step, addressing the challenges of non-smooth estimators and demonstrating its effectiveness through simulations and real data.

Contribution

It introduces a comprehensive asymptotic analysis for generated quantile regression, extending its applicability to complex models like endogenous and structural equation models.

Findings

Established the asymptotic distribution of the two-step estimator.

Demonstrated the estimator's performance via simulations.

Applied the method to auction data with positive results.

Abstract

We study linear quantile regression models when regressors and/or dependent variable are not directly observed but estimated in an initial first step and used in the second step quantile regression for estimating the quantile parameters. This general class of generated quantile regression (GQR) covers various statistical applications, for instance, estimation of endogenous quantile regression models and triangular structural equation models, and some new relevant applications are discussed. We study the asymptotic distribution of the two-step estimator, which is challenging because of the presence of generated covariates and/or dependent variable in the non-smooth quantile regression estimator. We employ techniques from empirical process theory to find uniform Bahadur expansion for the two step estimator, which is used to establish the asymptotic results. We illustrate the performance of the GQR estimator through simulations and an empirical application based on auctions.