| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w20257 |
| 来源ID | Working Paper 20257 |
| Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap | |
| Xavier D'; Haultfoeuille; Arnaud Maurel; Yichong Zhang | |
| 发表日期 | 2014-06-26 |
| 出版年 | 2014 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Econometrics ; Estimation Methods ; Labor Economics ; Labor Compensation |
| URL | https://www.nber.org/papers/w20257 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/577930 |
| 推荐引用方式 GB/T 7714 | Xavier D',Haultfoeuille,Arnaud Maurel,et al. Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap. 2014. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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