Educational Background
Doctor Degree,Economics,Purdue University, USA
Master Degree,Applied Statistics,University of Science and Technology of China
Bachelor Degree,Finance,University of Science and Technology of China
Awards on Research
2022.01,Denis Sargan Econometrics Prize,Royal Economic Society
2021.05,Bilsland Dissertation Fellowship,Purdue University
Awards on Teaching
Journal Papers
| 1. | Joshua C. C. Chan, Gary Koop, and Xuewen Yu. 2024. Large order-invariant Bayesian VARs with stochastic volatility. Journal of Business & Economic Statistics 42(2) 825-837.  |
| 2. | Mohitosh Kejriwal, Linh Nguyen, and Xuewen Yu. 2023. Multistep forecast averaging with stochastic and deterministic trends. Econometrics 11(4) 1-43. |
| 3. | Yong Bao and Xuewen Yu. 2023. Indirect inference estimation of dynamic panel data models. Journal of Econometrics 235(2) 1027-1053. |
| 4. | Joshua C.C. Chan and Xuewen Yu. 2022. Fast and accurate variational inference for large bayesian VARs with stochastic volatility. Journal of Economic Dynamics and Control 143 1-19. |
| 5. | Mohitosh Kejriwal, Pierre Perron, and Xuewen Yu. 2022. A two-step procedure for testing partial parameter stability in cointegrated regression models. Journal of Time Series Analysis 43(2) 219–237. |
| 6. | Mohitosh Kejriwal and Xuewen Yu. 2021. Generalized forecast averaging in autoregressions with a near unit root. The Econometrics Journal 24(1) 83-102. |
| 7. | Mohitosh Kejriwal, Xuewen Yu, and Pierre Perron. 2020. Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series. Journal of Time Series Analysis 41(5) 676-690. |
Research Projects
2025.01—2028.12, Member, Decision Tree-based Asset Heterogeneity, Regime Switching, and Uncommon Factor Structures, National Natural Science Foundation of China
2024.01—2026.12, Principal Investigator, The Econometric Testing of Asset Price Bubbles: Theory and Methods, National Natural Science Foundation of China
2023.04—2026.03, Principal Investigator, Bayesian Identification and Estimation of Large Vector Autoregression Models, Shanghai Municipal Commission of Science and Technology
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