Yinchu Zhu

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Bio:

Yinchu Zhu is an assistant professor in the Department of Operations and Business Analytics at the Lundquist College of Business. His expertise includes statistics, machine learning, and econometrics. His research has been published in journals such as the Journal of Econometrics, Electronic Journal of Statistics, and Journal of the American Statistical Association.

Zhu received his PhD and MA from the University of California at San Diego. In 2017, he received the New Junior Faculty Research Award at the University of Oregon.

Machine learning
Econometrics
Casual inference

Journal Articles

Komunjer, Ivana, and Yinchu Zhu. "Likelihood Ratio Testing in Linear State Space Models: an Application to Dynamic Stochastic General Equilibrium Models." Journal of Econometrics, forthcoming.
Smith, Simon, Allan Timmermann, and Yinchu Zhu. "Variable Selection in Panel Models with Breaks." Journal of Econometrics, forthcoming. http://doi.org/10.2139/ssrn.3238230.
Chernozhukov, Victor, Kaspar Wuthrich, Yinchu Zhu. "Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data." Conference on Learning Theory, forthcoming.
Zhu, Yinchu and Jelena Bradic. "Linear Hypothesis Testing in Dense High-Dimensional Linear Models." Journal of the American Statistical Association 113, no. 524: 1583-1600. https://doi.org/10.1080/01621459.2017.1356319.
November 2018
Zhu, Yinchu and Jelena Bradic. "Significance Testing in Non-Sparse High-Dimensional Linear Models." Electronic Journal of Statistics 12, no. 2: 3312-3364. https://doi.org/10.1214/18-EJS1443.
October 2018

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