时 间：2025年09月19日 15:00-16:00
地 点：中北理科大楼A1514室
报告人：张新雨 中国科学院数学与系统科学研究院研究员
主持人：马慧娟 华东师范大学 副教授
摘 要：
<img class="akeylayout_img"d-frequency data, where variables are observed at different temporal resolutions, commonly occur in economic and financial studies. Classical synthetic control methods (SCM) are ill-suited for such data, often necessitating aggregation or prefiltering that may discard valuable information. This paper proposes a novel Mixed-Frequency Synthetic Control Method (MF-SCM) to integrate mixed-frequency data into the synthetic control framework effectively. We develop a flexible estimation procedure to construct synthetic control weights under mixed-frequency settings and establish the theoretical properties of the MF-SCM estimator. Specifically, we first prove that the estimator achieves asymptotic optimality, in the sense that it achieves the lowest possible squared prediction error among all potential treatment effect estimators from averaging outcomes of control units. We then derive the asymptotic distribution of the average treatment effect (ATE) estimator using projection theory and construct confidence intervals for the ATE estimator. The method’s effectiveness is demonstrated through numerical simulations and two empirical applications on air pollution alerts and policy study on Tax Cuts and jobs Act of 2017.
报告人简介：
张新雨，中国科学院数学与系统科学研究院研究员。长期从事统计和计量经济学理论与应用方面的研究工作，与合作者解决了模型平均研究中的多个难题，并将模型平均与迁移学习、随机森林等方法融合提出了具有创新性的新方法，同时将所提出的预测方法应用于实际问题为相关部门的决策提供了参考依据。先后主持国家自然科学基金委杰出及其延续项目等，曾获中国青年科技奖。