A1320
Title: Synthetic control method with mixed frequency data
Authors: Lu Zhang - University of Science and Technology of China (China) [presenting]
Abstract: 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. The aim is to propose a novel mixed-frequency synthetic control method (MF-SCM) to integrate mixed-frequency data into the synthetic control framework effectively. A flexible estimation procedure is developed to construct synthetic control weights under mixed-frequency settings and establish the theoretical properties of the MF-SCM estimator. Specifically, it is first proven 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. The asymptotic distribution of the average treatment effect (ATE) estimator is then derived, using projection theory, and confidence intervals are constructed for the ATE estimator. The method's effectiveness is demonstrated through numerical simulations and two empirical applications on air pollution alerts and a policy study on the Tax Cuts and Jobs Act of 2017 in the US.
