Data from: Core determining class and inequality selection

This record contains the underlying research data for the publication "Core determining class and inequality selection" and the full-text is available from: https://ink.library.smu.edu.sg/sis_research/3769The relations between unobserved events and observed outcomes can be characterized by a bipartite graph. We propose an algorithm that explores the structure of the graph to construct the "exact Core Determining Class," i.e., the set of irredudant inequalities. We prove that in general the exact Core Determining Class does not depend on the probability measure of the outcomes but only on the structure of the graph. For more general linear inequalities selection problems, we propose a statistical procedure similar to the Dantzig Selector to select the truly informative constraints. We demonstrate performances of our procedures in Monte-Carlo experiments.Based on paper given at American Economic Association Annual Meeting 2017, January 6-8, Chicago, IL

本数据集包含论文《核心决定类与不平等选择》（Core determining class and inequality selection）的支撑研究数据，论文全文可通过以下链接获取：https://ink.library.smu.edu.sg/sis_research/3769。未观测事件与观测结果之间的关联可通过二部图（bipartite graph）进行刻画。我们提出一种算法，通过挖掘该图的结构以构建精确核心决定类（exact Core Determining Class），即不可约不等式集合。我们证明，在一般情形下，精确核心决定类并不依赖于结果的概率测度，仅与图的结构相关。针对更具普适性的线性不等式选择问题，我们提出了一种类似丹齐格选择器（Dantzig Selector）的统计流程，用以筛选真正具备信息价值的约束条件。我们通过蒙特卡洛（Monte-Carlo）实验验证了所提方法的性能。本研究基于2017年1月6日至8日在伊利诺伊州芝加哥市举办的美国经济学会年会（American Economic Association Annual Meeting 2017）上发表的论文。