The Economics Department

Kansas Econometrics Workshop


Date: April 18, 2020 postponed
Location: Embassy Suites, Olathe, KS

Presenters


Jason Abrevaya

Jason Abrevaya

University of Texas at Austin

Dr. Abrevaya's research has focused on econometric methodology, applied microeconomics, and demography. His work has utilized large-scale birth databases within the United States to research topics including birthweight inequality, the effects of smoking upon birth outcomes, and the practice of gender selection among specific ethnic groups. His research has been funded by the National Science Foundation and the Robert Wood Johnson Foundation.

Ying Fang

Ying Fang

Xiamen University

 

Ye Guo

Ye Guo

Xiamen University

Kosuke Imai

Kosuke Imai

Harvard University

Ted Juhl

Ted Juhl

University of Kansas

Arthur Lewbel

Arthur Lewbel

Boston University

Ming Lin

Ming Lin

Xiamen University

Wuqing Wu

Wuqing Wu

Renmin University of China

Yuya Sasaki

Yuya Sasaki

Vanderbilt University

Econometrics is the research field of Yuya Sasaki. He has worked on micro-econometric topics such as dynamic discrete choice models, income dynamics, measurement error models, panel data analysis, production functions, program evaluation methods, and quantile regressions. Many of his current projects concern robust and uniform nonparametric inference, as well as nonparametric identification in the above topics. His research is often motivated by issues encountered by empirical practitioners, and he is also interested in conducting empirical research by applying the knowledge and techniques in his expertise.

Shu Shen

Shu Shen

University of California, Davis

Shu Shen has expertise in econometric theory, applied econometrics and applied microeconometrics. 

Aman Ullah

Aman Ullah

University of California, Riverside

 

Dacheng Xiu

Dacheng Xiu

Chicago University

Dacheng Xiu’s research interests include developing statistical methodologies and applying them to financial data, while exploring their economic implications. His earlier research involved risk measurement and portfolio management with high-frequency data and econometric modeling of derivatives. His current work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.