Xiaohong Chen is an elected member of the American Academy of Arts and Sciences since 2019, a fellow of the Econometric Society since 2007, a founding fellow of the International Association for Applied Econometrics since 2018, a fellow of the Journal of Econometrics since 2012, and an international fellow of Cemmap since 2007. Chen is a winner of the 2017 China Economics Prize. Chen has been a keynote or an invited speaker in many international conferences. She was the 2018 Sargan Lecturer of the Econometric Society, the 2019 Hilda Geiringer Lecturer, and the 2017 Econometric Theory lecturer.
Chen’s research field is econometrics. She is known for her research in penalized sieve estimation and inference on semiparametric and nonparametric models, such as semiparametric models of nonlinear time series, empirical asset pricing, copula, missing data, measurement error, nonparametric instrumental variables, semi/nonparametric conditional moment restrictions, causal inference.
Chen has published peer-reviewed papers in top-ranked general-purpose journals in economics: Econometrica and Review of Economic Studies; as well as in top-ranked journals in statistics and engineering: Annals of Statistics, Journal of the American Statistical Association, IEEE Tran Information Theory, IEEE Trans Neural Networks. Chen published several invited review chapters. She also won Econometric Theory Multa Scripsit Award in 2012, The Journal of Nonparametric Statistics 2010 Best Paper Award, The Richard Stone Prize in Journal of Applied Econometrics for the years 2008 and 2009, The Arnold Zellner Award for the best theory paper published in Journal of Econometrics in 2006 and 2007.
Chen is an editor of Journal of Econometrics since Jan 2019.
What do you do with data science?
Chen’s research is mostly about penalized sieve (such as splines, wavelets, neural networks) estimation and inference on semiparametric and nonparametric models, such as semiparametric models of nonlinear time series, empirical asset pricing, copula, missing data, measurement error, nonparametric instrumental variables, semi/nonparametric conditional moment restrictions, causal inference. Some example publications are:
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation’’ X. Chen and Z. Qi, Proceedings of the 39th International Conference on Machine Learning, PMLR 162:3558-3582, 2022, ArXiv:2201.06169. https://proceedings.mlr.press/v162/chen22u.html
Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models’’ (C. Breunig and X. Chen), December 2021. Cowles Foundation Discussion Paper no 2238R. Revised for Econometrica.
Adaptive Estimation and Uniform Confidence Bands for Nonparametric Instrumental Variables’’ (X. Chen, T. Christensen and S. Kankanala), 2021, Cowles Foundation Discussion Paper no 2292, Revision requested by the Review of Economic Studies.
Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators’’ (Jiafeng Chen, Xiaohong Chen and Elie Tamer), Cowles Foundation Discussion Paper no 2319, Revision requested by Journal of Econometrics
Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models’’ by X. Chen and D. Pouzo, Econometrica, 2015, 83, 1013-1079.
High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data’’ by J. Chang, S. Chen and X. Chen, Journal of Econometrics, 2015, 185, 283-304
Semiparametric Efficiency in GMM Models with Auxiliary Data’’ by X. Chen, H. Hong and A. Tarozzi, Annals of Statistics, 2008, 36, 808-843
Large Sample Sieve Estimation of Semi-nonparametric Models’’ by X. Chen, chapter 76 in Handbook of Econometrics, Vol. 6B, 2007, eds. James J. Heckman and Edward E. Leamer, North-Holland
Measurement Error Models with Auxiliary Data’’ by X. Chen, H. Hong and E. Tamer, Review of Economic Studies, 2005, 72, 343-366
Asymptotic Properties of Some Projection-based Robbins-Monro Procedures in a Hilbert Space” by X. Chen and H. White, Studies in Nonlinear Dynamics and Econometrics 2002, vol. 6, issue 1, article 1.
Semiparametric ARX Neural Network Models with an Application to Forecasting Inflation” by X. Chen, J. Racine and N. Swanson, 2001, IEEE Transactions on Neural Networks, 12, 674-683.
Improved Rates and Asymptotic Normality for Nonparametric Neural Network Estimators” by X. Chen and H. White, 1999. IEEE Tran. Information Theory, Vol. 45, 682-691.
Sieve Extremum Estimates for Weakly Dependent Data” by X. Chen and X. Shen, 1998, Econometrica, 66, 289-314.