On Completeness and Consistency in Nonparametric Instrumental Variable Models
研究了非参数工具变量模型中识别条件（完备性）的可检验性，证明数据能支持识别集很小或估计量渐近偏误很小，并给出识别集直径上界的估计方法。
This paper provides positive testability results for the identification condition in a nonparametric instrumental variable model, known as completeness, and it links the outcome of the test to properties of an estimator of the structural function. In particular, I show that the data can provide empirical evidence in favor of both an arbitrarily small identified set as well as an arbitrarily small asymptotic bias of the estimator. This is the case for a large class of complete distributions as well as certain incomplete distributions. As a byproduct, the results can be used to estimate an upper bound of the diameter of the identified set and to obtain an easy to report estimator of the identified set itself.