Seminar (Dept. of Accounting)

Title：Estimation of Average Treatment Effect for High-Dimensional Panel Data via Random Forests-Based Variable Selection

Speaker: Wei Long, Associate professor, Tulane University

Time：10:00-11:30, June 17th, 2025 (Tuesday)

Venue：Room 1008, Mingde Business Building

Language：Chinese & English

ABSTRACT:

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.

SHORT BIOGRAPHY:

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.