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Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment

机译:具有不受限制的异质性和连续处理的工具变量

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摘要

This article discusses identification in continuous triangular systems without restrictions on heterogeneity or functional form. We do not assume separability of structural functions, restrictions on the dimensionality of unobservables, or monotonicity in unobservables. We do maintain monotonicity of the first stage relationship in the instrument and consider the case of real-valued treatment. Under these conditions alone, and given rich enough support of the data, potential outcome distributions, the average structural function, and quantile structural functions are point identified. If the support of the continuous instnument is not large enough, potential outcome distributions are partially identified. If the instrument is discrete, identification fails completely. If treatment is multi-dimensional, additional exclusion restrictions yieldidentification. The set-up discussed in this article covers important cases not covered by existing approaches such as conditional moment restrictions (cf. Newey and Powell, 2003) and control variables (cf. Imbens and Newey, 2009). It covers, in particular, random coefficient models, as well as systems of structural equations.
机译:本文讨论了在不限制异质性或功能形式的情况下,在连续三角形系统中进行识别的方法。我们不假定结构功能的可分离性,对不可观测对象的维数的限制或不可观测对象的单调性。我们确实在工具中保持了第一阶段关系的单调性,并考虑了实值处理的情况。仅在这些条件下,并在足够丰富的数据支持下,即可确定潜在的结果分布,平均结构函数和分位数结构函数。如果连续仪器的支持不够大,则可能会部分识别潜在的结果分布。如果仪器是离散的,则识别将完全失败。如果处理是多维的,则其他排除限制会导致识别。本文讨论的设置涵盖了现有方法未涵盖的重要情况,例如条件矩限制(参见Newey和Powell,2003年)和控制变量(参见Imbens和Newey,2009年)。它尤其涵盖了随机系数模型以及结构方程组。

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