INSTRUMENTAL VARIABLE QUANTILE REGRESSION WITH MISCLASSIFICATION
INSTRUMENTAL VARIABLE QUANTILE REGRESSION WITH MISCLASSIFICATION
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带有错误分类的工具变量分位数回归
DOI：
10.1017/s026646662000002x
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发表时间：
2016
影响因子：
0.8
通讯作者：
T. Ura
中科院分区：
文献类型：
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作者：
T. Ura
摘要本文研究了工具变量分位数回归模型(Chernozhukov和Hansen，2005，Economrica 73,245-261；2013，《经济学年报》，5，57-81)的二元内生处理。当治疗状态没有被直接观察时，它提供两个识别结果。第一个结果是，值得注意的是，结果变量对工具变量的简化分位数回归为随机单调性条件下的结构分位数处理效应提供了一个下限。这一结果是相关的，不仅当治疗变量容易被错误分类时，而且当治疗变量的任何测量都不可用时。第二个结果是在有误差的情况下测量处理状态时的结构分位数函数；在广泛使用的关于测量误差的假设下，尖锐识别集的特征是一组矩条件。此外，还提供了在存在其他协变量的情况下的推理方法。
Abstract This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics, 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the structural quantile treatment effect under the stochastic monotonicity condition. This result is relevant, not only when the treatment variable is subject to misclassification, but also when any measurement of the treatment variable is not available. The second result is for the structural quantile function when the treatment status is measured with error; the sharp identified set is characterized by a set of moment conditions under widely used assumptions on the measurement error. Furthermore, an inference method is provided in the presence of other covariates.
错误分类治疗的局部平均治疗效果的推断
DOI：
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发表时间：
2017
期刊：
影响因子：
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作者：
小沢 佳史;小沢佳史;Takahide Yanagi
通讯作者：
Takahide Yanagi