Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments
提出一种新方法，通过对随机扰动的线性规划问题的值函数进行自助法，对由线性矩不等式定义的部分识别参数的线性泛函或标量子向量进行一致有效的推断，计算简单且无需对参数空间进行网格搜索。
This article proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to bootstrapping the value functions of randomly perturbed linear programming problems, and does not require the researcher to grid over the parameter space. The low-level conditions for uniform validity rely on genericity results for linear programs. The unconventional perturbation approach produces a confidence set with a coverage probability of 1 over the identified set, but obtains exact coverage on an outer set, is valid under weak assumptions, and is computationally simple to implement.