摘要翻译：
我们研究了当仪器与误分类误差相关时，带有误分类和内生二元回归子的非参数回归模型的辨识问题。我们证明了当一个二元工具变量和一个二元协变量满足以下条件时，回归函数是非参数辨识的。工具性变量纠正内生性；工具变量必须与未观察到的真正的潜在二元变量相关，必须与结果方程中的误差项无关，但允许与误分类误差相关。协变量修正错误分类；该变量可以是结果方程中的一个回归变量，必须与未观察到的真实潜在二元变量相关，并且必须与错误分类误差无关。我们还提出了一个基于混合的框架，用一个错误分类的和内生的二元回归模型来建模未观察到的异质性治疗效果，并表明如果真实的治疗效果与一个观察到的回归模型和另一个观察到的变量有关，治疗效果可以被识别。
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英文标题：
《Identification of Regression Models with a Misclassified and Endogenous
  Binary Regressor》
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作者：
Hiroyuki Kasahara and Katsumi Shimotsu
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable.
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PDF链接：
https://arxiv.org/pdf/1904.11143