Title: Forward-Selected Panel Data Approach for Program Evaluation

Speaker: Zhentao Shi, Chinese University of Hong Kong

Time: September 27th, 2019  15:00-16:15

Venue: Conference Room 106B, IESR, Zhonghui Building (College of Economics)

About the speaker:

Zhentao Shi is currently an Assistant Professor in the Department of Economics, Chinese University of Hong Kong, which he joined after receiving Ph.D. from Yale University in 2014. Zhentao Shi's main interests are in econometric theory, in particular estimation and inference of machine learning methods in economic applications. He has published in such journals as Journal of Applied Econometrics, Econometrica, Journal of Econometrics, and etc. 

Abstract:

Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao, Ching and Wan, 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in their implementation, is nevertheless unresolved in data-rich environment when many possible controls are at the researcher's disposal. We propose the forward selection method to choose control units, and establish validity of post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings. Monte Carlo simulations are conducted to demonstrate the finite sample performance of the proposed method. Two empirical examples illustrate the usefulness of our procedure when many controls are available in real-world applications.