高等经济研究院教师论文被国际权威经济学期刊接收发表

2025年09月19日

近日，东北财经大学高等经济研究院 Hyeonseok Park 助理教授与华盛顿大学教授范延琴、华盛顿大学博士生许高谦合作的论文 “Quantifying Distributional Model Risk in Marginal Problems via Optimal Transport” 被国际权威期刊 Mathematics of Operations Research 接收并正式线上发表。

该论文研究边缘问题中的分布模型风险，假定每个边缘测度处于Wasserstein球内。研究结果证明了该分布模型风险的强对偶性、有限性，以及每个半径下均存在最优解。研究同时证明了Wasserstein分布模型风险作为半径函数的连续性。借助强对偶性，论文将两个给定边缘分布的随机变量之和与分布函数的Makarov边界，推广至Wasserstein分布稳健的Makarov边界。研究通过四个不同应用场景展示研究成果：当样本信息来自多重数据源且仅部分边缘参考测度被识别时——处理效应的部分识别、通过稳健福利函数实现外部有效处理选择、数据融合下的Wasserstein分布稳健估计，以及最差聚合风险度量的评估。

 

Assitant Professor Hyeonseok Park's Paper Accepted for Publication in Mathematics of Operations Research

September 19, 2025

Hyeonseok Park, Assistant Professor of IAER, has his paper accepted for publication in Mathematics of Operations Research. Entitled "Quantifying Distributional Model Risk in Marginal Problems via Optimal Transport", the paper was co-authored Yanqin Fan, Professor of Department of Economics at University of Washington, and Gaoqian Xu, Ph.D. student in Department of Economics at Universtiy of Washington.

This paper studies distributional model risk in marginal problems, where each marginal measure is assumed to lie in a Wasserstein ball. We establish fundamental results including strong duality, finiteness of the proposed Wasserstein distributional model risk, and the existence of an optimizer at each radius. We also show continuity of the Wasserstein distributional model risk as a function of the radius. Using strong duality, we extend the well-known Makarov bounds for the distribution function of the sum of two random variables with given marginals to Wasserstein distributionally robust Makarov bounds.  We illustrate our results on four distinct applications when the sample information comes from multiple data sources and only some marginal reference measures are identified: partial identification of treatment effects, externally valid treatment choice via robust welfare functions,  Wasserstein  distributionally  robust  estimation  under  data  combination,  and evaluation of the worst aggregate risk measures.