Simultaneous Confidence Intervals for Partially Identified Parameters
将Imbens和Manski以及Stoye针对标量部分识别参数的置信区间方法推广到向量参数，提出一种计算简单快速的同时置信区间，适用于各维度上下界有渐近正态估计的情形。
This article extends the Imbens and Manski and Stoye confidence interval for a partially identified scalar parameter to a vector-valued parameter. The proposed method produces uniformly valid simultaneous confidence intervals for each dimension, or, equivalently, a rectangular confidence region that covers points in the identified set with a specified probability. The method applies when asymptotically normal estimates of upper and lower bounds for each dimension are available. The intervals are computationally simple and fast relative to methods based on test inversion or bootstrapped calibration, and do not suffer from the conservativity of projection-based approaches.