CONSTRAINT QUALIFICATIONS IN PARTIAL IDENTIFICATION
分析了随机规划中的约束规格与部分识别文献中高维假设之间的关系，发现纯矩不等式下的部分识别假设本质上等价于Mangasarian–Fromowitz约束规格，从而澄清了经典文献中假设的严格性和可验证性。
The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications. The literature on estimation and inference under partial identification frequently restricts the geometry of identified sets with diverse high-level assumptions. These superficially appear to be different approaches to closely related problems. We extensively analyze their relation. Among other things, we show that for partial identification through pure moment inequalities, numerous assumptions from the literature essentially coincide with the Mangasarian–Fromowitz constraint qualification. This clarifies the relation between well-known contributions, including within econometrics, and elucidates stringency, as well as ease of verification, of some high-level assumptions in seminal papers.