会计系学术讲座系列2025年第13讲

来源：会计系

主　题：高维面板数据下的平均处理效应估计：基于随机森林样本选择的方法

主讲人：龙威（杜兰大学副教授）

协调人：周华、汝毅

时　间：6月17日（周二）上午10:00-11:30

地　点：明德商学楼1008室

语　言：中英文

讲座摘要：

It is challenging to conduct controlled experiments to assess the impacts of social policy. To address this, Hsiao et al. (2012) propose a panel data approach using factor models to estimate average treatment effects. The selection of control units is a critical step to balance the goodness of fit within-sample with the post-treatment forecasting error when the number of observed potential control units is sufficiently large. In this study, we propose using random forests, an ensemble learning method, which offers robustness and requires fewer candidate models compared to existing methods. We demonstrate that our approach effectively selects almost all relevant control units, and we provide rigorous asymptotic normality results and significance tests for policy interventions. Extensive simulations confirm the method's superior performance. Applying our approach to evaluate the impact of Brexit on the United Kingdom's GDP growth, we find an average quarterly decline of 1.86 percentage points between 2017 and 2019, primarily attributed to reduced private consumption and investment.

主讲人简介：

Wei Long is an associate professor of economic at Tulane University. He received a BA in accounting from Renmin University (2009) and Ph.D. in economics from Texas A&M University (2015). His research interests are in applied economics with a concentration on the public economics and econometrics with a concentration on financial econometrics. His works have been published in journals including Journal of Econometrics, Econometric Theory, Journal of Public Economics, Journal of the American Statistical Association, and Journal of Economic Behavior & Organization.

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