ON THE COMPLETENESS CONDITION IN NONPARAMETRIC INSTRUMENTAL PROBLEMS
研究了非参数工具变量模型中两个随机元素之间的完备性概念，在加性可分离和大支撑条件下得到了不同版本的完备性，并用于建立有限内生回归元情形下工具变量非参数回归的识别，弥补了控制变量法的不足。
The notion of completeness between two random elements has been considered recently to provide identification in nonparametric instrumental problems. This condition is quite abstract, however, and characterizations have been obtained only in special cases. This paper considers a nonparametric model between the two variables with an additive separability and a large support condition. In this framework, different versions of completeness are obtained, depending on which regularity conditions are imposed. This result allows one to establish identification in an instrumental nonparametric regression with limited endogenous regressor, a case where the control variate approach breaks down.