State-varying Synthetic Control Method
报告人： 孙玉莹 (中国科学院数学与系统科学研究院)
时间：2024-02-29 15:10-17:00
地点：Room 217, Guanghua Building 2
Abstract:
We propose a novel synthetic control method with a dynamic weighting scheme to evaluate the impacts of social policy. The basic idea is to utilize the interdependence between different control units in a panel dataset to create the counterfactuals locally. Unlike the existing literature, we allow the weights to change over state variables and thus it is expected to capture potentially nonlinear features in economics and finance. It is shown that the treatment-effect estimator is asymptotically optimal in the sense of achieving the lowest possible local squared prediction error. The rate of the selected weights converging to the optimal weights to minimizing the expected local quadratic loss is established. Simulations and empirical applications are conducted to evaluate the finite sample performance of the proposed method.
About the Speaker:
中国科学院数学与系统科学研究院副研究员、博士生导师。国家优秀青年科学基金项目获得者，第6届中国科协“青年人才托举工程”入选者。研究兴趣主要有计量经济学、经济预测理论与方法、机器学习等。多篇研究成果发表在Journal of Econometrics, European Journal of Operational Research, Journal of Travel Research, Energy Economics等国际权威期刊；多篇政策研究和疫情预测报告得到国家领导人批示或被中办、国办采用。曾获陈景润未来之星、中国科学院数学与系统科学研究院“重要科研进展奖（2017，2019）”、关肇直青年研究奖、中国管理科学与工程学会优秀博士学位论文奖等。
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