Xu Cheng

Xu Cheng is a Professor of Economics at the University of Pennsylvania, specializing in econometrics and its applications. Her research focuses on developing robust econometric methods to address challenging empirical issues, including models with limited identification information, model misspecification, strong cross-sectional or time-series dependence, and high-dimensional estimation and inference with machine learning methods. She is a Fellow of the Journal of Econometrics and the International Association of Applied Econometrics and was selected as a Penn Faculty Fellow. Her dedication to teaching has been recognized twice with the Kravis Award for Distinction in Undergraduate Teaching at Penn. She obtained her Ph.D. in Economics from Yale University in 2010. In addition to teaching at Penn, she has held visiting positions at Princeton University and Yale University. She currently serves as a Co-editor of Econometric Theory and an associate editor of Quantitative Economics, Journal of Econometrics, Econometrics Journal, and Journal of Econometric Methods.

Xu Cheng is an economist and econometrician at the University of Pennsylvania, specializing in advanced econometric methods and their applications in economic analysis. His research focuses on estimation and inference in econometric models, particularly in settings with weak, semi-strong, and strong identification. He explores robust inference techniques in nonlinear models and develops methods for handling structural instabilities and high-dimensional data. His work includes contributions to GMM estimation, factor-augmented regression, and model averaging approaches. Cheng's research also addresses macro-finance topics, such as asset pricing models and cointegrating rank selection. His publications emphasize methodological innovations and their practical implications in economic modeling.