Publication Type
Working Paper
Publication Date
6-2014
Abstract
We consider the estimation of a semiparametric location-scale model subject to endogenous selection, in the absence of an instrument or a large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. In this context, we propose a simple estimator, which combines extremal quantile regressions with minimum distance. We establish the asymptotic normality of this estimator by extending previous results on extremal quantile regressions to allow for selection. Finally, we apply our method to estimate the black-white wage gap among males from the NLSY79 and NLSY97. We find that premarket factors such as AFQT and family background characteristics play a key role in explaining the level and evolution of the black-white wage gap.
Keywords
Sample selection models, extremal quantile regressions, black-white wage gap
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
64
Publisher
SSRN
Citation
D'HAULTFOEUILLE, Xavier; MAUREL, Arnaud; and ZHANG, Yichong.
Extremal quantile regressions for selection models and the black white wage gap. (2014). 1-64. Research Collection School Of Economics.
Available at: https://ink.library.smu.edu.sg/soe_research/2029
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Additional URL
https://ssrn.com/abstract=2460159