[数量经济学研讨会]Estimation and Inference of Treatment Effects using a New Panel Data Approach with Application to US SYG Law

报告题目:Estimation and Inference of Treatment Effects using a New Panel Data Approach with Application to US SYG Law
报告人:  Qiankun Zhou (Department of Economics, Louisiana State University)

内容简介:This paper propose a new panel data approach to measure the impact of social policy. We consider a classical panel model with interactive effects, which allows the cross-sectional dependence through the presence of some (unobserved) common factors. Compared to the existing methods such as Synthetic Control Method (SCM) (Abadie et al., 2010) and the Generalized SCM (GSCM) (Xu,2017), our new approach has the advantages of: (1) there is no need to impose constraints on both observables and unobservables; (2) the number of parameters to be estimated in the model is greatly reduced. Instead of estimating the unobserved factors, we propose to use observed data. Moreover, we also establish the asymptotic results for the average treatment effect (ATE), which can be used to obtain valid inference for the significance of ATE or to construct confidence band for the treatment effects in the post-treatment periods. Monte Carlo simulations show that our approach works remarkably well and has very desirable finite sample performance in terms of estimation bias, mean square of errors, and empirical rejection. We apply our method to study the impact of US Stand Your Ground (SYG) law on the state level murder rate, and we find, in general, the SYG has increased the murder rate.
报告人简介:Qiankun Zhou is an assistant professor from the Department of Economics at Louisiana State University. He obtained his PhD in Economics from University of South California at 2015. His research interests include panel data models, nonparametric econometrics, and financial econometrics. His papers have been published in many top journals such as Journal of Econometrics, Econometric Theory, and Econometric Reviews.



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