Date: Oct.8^th, 2019

Speaker: SHI Zhentao

Venue: Room 236, School of Economics, Yuquan Campus

【Speaker Intro】

Dr. Zhentao Shi is Assistant Professor at the Department of Economics, the Chinese University of Hong Kong. He specializes in econometric theory. He has published on top economic journals such as Econometrica, Journal of Econometrics, and Journal of Applied Econometrics. He obtained Ph.D. from Yale University, M.A. from Peking University, and B.A. from Zhejiang University. 

【Abstract】

Machine learning stands at the frontier of today’s technological progress, and it influences the way we conduct economic research. Given the wide availability of statistical software, economic applications of machine learning methods are burgeoning. However, there remains a wide gap to fill until machine learning becomes the mainstream and can be routinely employed in empirical research. The theory of machine learning is mostly established for generic statistical models, but not tailored for context of economic interest.

In this talk, I will review my works with collaborators in bridging machine learning and econometrics. They are either innovative machine learning algorithms that shed light on empirical economic questions, or studies of existing machine learning methods’ properties in economic settings, in particular nonstationary time series and panel data. We work under standard econometric theoretical frameworks and make progress in consistency and/or asymptotic statistical inference. We also develop open-source software to engage users.