Quantile models with endogeneity

Abstract

Abstract. In this article, we review quantile models with endogeneity. We focus on models that achieve identification through the use of instrumental variables and discuss conditions under which partial and point identification are obtained. We discuss key conditions, which include monotonicity and full-rank-type conditions, in detail. In providing this review, we update the identification results of Chernozhukov and Hansen (2005). We illustrate the modeling assumptions through economically motivated examples. We also briefly review the literature on estimation and inference.

The spatial extent of agglomeration economies across the wage earnings distribution

“…Thus, we report the effects on the selected percentiles of the wage earnings distribution for each of the selected percentiles of the four employment by distance band distributions. Chernozhukov and Hansen (2013) note that it is difficult to consider Equations (9) and 10 as a set of conventional demand and supply functions. The framework in Equations (9) and 10 may instead be interpreted as a model where the supply curve for labor (Equation 10) is perfectively inelastic and the demand curve for labor (Equation (9)) is elastic.…”

Education and wage inequality before and during the fiscal crisis: A quantile regression analysis for Greece 2006–2016

“…Nevertheless, it should be stressed that the validity of the proposed algorithm rests on the assumption of “rank similarity,” which was mentioned earlier, and that is formally stated as (Chernozhukov & Hansen, 2013):…”

“…Unfortunately, this assumption is not formally testable, and, thus, caution is needed when drawing conclusions (Autor, Houseman, & Kerr, 2017). However, it should be noted that similarity can partially be achieved using additional covariates to capture enough variation (Chernozhukov & Hansen, 2013).…”

Exact computation of GMM estimators for instrumental variable quantile regression models

“…The instrumental variable quantile regression (IVQR) and related models have been increasingly popular for studying the impacts of possibly endogenous covariates on the distribution of the outcome of interest. See a recent review by Chernozhukov and Hansen (2013) and references therein for the latest developments in identification, estimation, and inference as well as the list of empirical applications.…”