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摘要翻译：
具有离散内生变量的模型通常在工具具有太少的值时被低估。本文提出了一种新的方法，通过匹配协变量对和工具来恢复三角模型中的点识别。该模型由连续结果的结构函数和离散内生变量的选择模型组成。在一个标量扰动中，结构结果函数必须是连续的和单调的，但它可以是不可分的。选择模型允许不受限制的异质性。在弱条件下得到全局辨识。本文还提供了结构结果函数的估计量。回归教育和选拔进入领先阶段的两个实证例子说明了该方法的价值和局限性。
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英文标题：
《Matching Points: Supplementing Instruments with Covariates in Triangular
Models》
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作者：
Junlong Feng
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最新提交年份：
2020
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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：
Models with a discrete endogenous variable are typically underidentified when the instrument takes on too few values. This paper presents a new method that matches pairs of covariates and instruments to restore point identification in this scenario in a triangular model. The model consists of a structural function for a continuous outcome and a selection model for the discrete endogenous variable. The structural outcome function must be continuous and monotonic in a scalar disturbance, but it can be nonseparable. The selection model allows for unrestricted heterogeneity. Global identification is obtained under weak conditions. The paper also provides estimators of the structural outcome function. Two empirical examples of the return to education and selection into Head Start illustrate the value and limitations of the method.
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PDF链接：
https://arxiv.org/pdf/1904.01159
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