报告题目：Synthetic Control Method with Mixed Frequency Data

报告时间：2025年12月4日上午8:30

会议链接：https://meeting.tencent.com/dm/199UJQVUlFXU

会议 ID：233-599-207

主办单位：数学与统计学院/科研处

报告人：张新雨

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。主持多项国家级项目，曾获中国青年科技奖。

摘要：Mixed-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.