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摘要翻译：
研究了离散三角方程组中因子结构的信息量。因子结构在横断面和面板数据模型中的各种设置中都有应用，在本文中，我们在治疗效果文献中经常使用的二元系统中形式化地量化了它们的识别能力。我们的主要发现是，在比这些模型通常要求的更弱的假设下，强加一个因素结构产生了感兴趣参数的点识别，如结果方程中与内生回归相关的系数。特别地，我们表明，即使在结果方程的所有回归子都是离散的情况下，要求结果方程中的一个解释变量被排除在处理方程之外的“非标准”排除限制对于识别不再是必要的。我们还在具有更一般因素结构的模型中建立了内生回归系数的识别，在一个人可以获得至少两个公共因素的连续测量的情况下。
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英文标题：
《Informational Content of Factor Structures in Simultaneous Binary
Response Models》
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作者：
Shakeeb Khan, Arnaud Maurel, Yichong Zhang
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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：
We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a "non-standard" exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor.
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PDF链接：
https://arxiv.org/pdf/1910.01318
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