You are here

Tao Chen

Assistant Professor; Director of Big Data Research Lab; Senior Research Fellowship, Harvard Law School

519-888-4567 x32558
Office: HH 207
Curriculum Vitae (CV)

Big Data Research Lab

BA Finance (SISU); MA Economics; MS Math; PhD Economics; PhD Math [A.B.D.] (University of Connecticut)

Areas of specialization: Econometric theory; Applied econometrics; Functional data analysis; Labour economics

Research interests

I am primarily interested in theoretical econometrics, estimation and specification test of semi-parametric/nonparametric models in particular; and their applications in labor economics. I teach mathematical economics and econometrics both at undergraduate and graduate level.


Tao spent his first 22 years in Shanghai, China. Before he became an Assistant Professor at the University of Waterloo, he was in graduate school for 9 years in New England, USA.

His research on econometrics is both theoretical and applied. The theoretical part focuses mainly on micro-econometrics and functional data analysis; and the major applied fields are labor and urban economics.

Tao is a big fan of basketball, Age of Empires and enjoys reading “A Global History: From Prehistory to the 21st Century”. This is his favourite quote: Imagination is more important than knowledge. -- Albert Einstein

Selected publications

  • Chen, T., "Semiparametric efficiency for censored linear regression models with heteroskedastic errors", Econometric Theory, forthcoming.
  • Chen, T., and G. Tripathi, "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models", Journal of Econometrics, forthcoming. 
  • Chen, T., J Yuan, Y. Zhou and P. Zhu, “Testing conditional mean independence under symmetry”, Journal of Business & Economic Statistics, forthcoming.

  • Chen, T. and T. Parker, "Semiparametric Efficiency for Partially Linear Single-Index Regression Models",Journal of Multivariate Analysis, forthcoming.

  • Thakali, L., L. Fu and T. Chen, “Model-Based Versus Data-Driven Approach for Road Safety Analysis: Do More Data Help?”, Transportation Research Record: Journal of the Transportation Research Board, No. 2061, (2016).
University of Waterloo