A1486
Title: Dynamic synthetic control method for semiparametric time-varying models
Authors: Shouxia Wang - Shanghai University of Finance and Economics (China) [presenting]
Songxi Chen - Peking University (China)
Xiangyu Zheng - JD Technology (China)
Abstract: Motivated by evaluating the treatment effects of a policy for nonlinear time-varying confounding variables, a dynamic synthetic control (DSC) method is proposed under the semiparametric time-varying additive autoregression outcome model. The proposed method allows for micro-level data with nonlinear time-varying confounders, multiple treated units and spatial correlations in the data. Spline-back-fitted-kernel estimation method is used to obtain good estimations of the unknown additive functions, which are then used for matching when the DSC weights are constructed. The DSC weights are constructed by the empirical likelihood, guaranteeing a unique solution and a consistent estimation of the average treatment effect on the treated group. The semiparametric additive model provides more flexibility in modelling and estimation, making it more favorable when either the parametric form of the model is unknown, or the model is incorrectly specified. An unconfounded assumption assessment test based on the estimated effects in the pre-treatment period and a normalized placebo test is developed to determine the significance of the estimated treatment effects. The proposed DSC method is demonstrated by numerical simulations and real data examples that highlight the effects of air pollution alerts in Beijing and the COVID-19 lockdown in Shanghai.
