Non-parametric bounds on quantiles under monotonicity assumptions: with an application to the Italian education returns
Non-parametric bounds on quantiles under monotonicity assumptions: with an application to the Italian education returns
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单调性假设下分位数的非参数界限：应用于意大利教育回报
DOI：
10.1002/jae.1132
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发表时间：
2011
影响因子：
2.1
通讯作者：
Pamela Giustinelli
中科院分区：
文献类型：
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作者：
Pamela Giustinelli
在预测一致处理分配t ∈ T下潜在结果P[y（t）]分布的推理背景下，本文讨论了在相对弱和可信的单调型假设下，个体响应函数和总体选择过程的兴趣分布Qα[y（t）]的α分位数的部分识别问题。在理论方面，通过引入和研究α-分位数单调处理选择（α-QMTS）、α-分位数单调工具变量（α-QMIV）及其组合的识别性质，扩充了无先验信息和单调处理反应（MTR）下分位数非参数界的已有结果.主要结果与平均值相似; MTR和α-QMTS以互补的方式帮助识别，因此将它们结合起来大大增加了识别能力。理论结果说明通过意大利返回教育资格的实证应用。估计了不同条件下ln（工资）的几个分位数的界和分位数处理效应（QTE），并与同一样本的参数分位数回归（α-QR）和α-IVQR估计进行了比较。值得注意的是，大学学位与小学教育的α-QTE的α-QMTS和MTR上限意味着逐年回报率低于相应的α-IVQR点估计。版权所有© 2010约翰威利父子有限公司.
Within the inferential context of predicting a distribution of potential outcomes P[y(t)] under a uniform treatment assignment t ∈ T, this paper deals with partial identification of the α-quantile of the distribution of interest Qα[y(t)] under relatively weak and credible monotonicity-type assumptions on the individual response functions and the population selection process. On the theoretical side, the paper adds to the existing results on non-parametric bounds on quantiles with no prior information and under monotone treatment response (MTR) by introducing and studying the identifying properties of α-quantile monotone treatment selection (α-QMTS), α-quantile monotone instrumental variables (α-QMIV) and their combinations. The main result parallels that for the mean; MTR and α-QMTS aid identification in a complementary fashion, so that combining them greatly increases identification power. The theoretical results are illustrated through an empirical application on the Italian returns to educational qualifications. Bounds on several quantiles of ln(wage) under different qualifications and on quantile treatments effects (QTE) are estimated and compared with parametric quantile regression (α-QR) and α-IVQR estimates from the same sample. Remarkably, the α-QMTS & MTR upper bounds on the α-QTE of a college degree versus elementary education imply smaller year-by-year returns than the corresponding α-IVQR point estimates. Copyright © 2010 John Wiley & Sons, Ltd.