Prof. Tae-Hwy Lee

Department of Economics

University of California, Riverside, USA

Email: [email protected]

Qualifications

1990 Ph.D., University of California, USA

1985 B.S., Seoul National University, Korea

Publications (Selected)

1. Chu, J., Lee, T. H., & Ullah, A. (2026). Asymmetric AdaBoost for Maximum Score Estimation of High-Dimensional Binary Choice Regression Models. Teaching Econometrics: A Tribute to R. Carter Hill, 233–261.
2. Lee, S., & Lee, T. H. (2025). How to Summarize the Survey of Professional Forecasters? University of California at Riverside, Department of Economics Working Papers.
3. Lee, S., & Lee, T. H. (2025). Solving the Forecast Combination Puzzle. Available at SSRN 5733863.
4. Hao, H., & Lee, T. H. (2025). Boosting GMM With Many Instruments When Some Are Invalid And/Or Irrelevant. Oxford Bulletin of Economics and Statistics, 87(5), 899–912.
5. Lee, T. H., & Seregina, E. (2025). Combining forecasts under structural breaks using Graphical LASSO. International Journal of Forecasting. Advance online publication.
6. Chavez-Lopez, P. I., & Lee, T. H. (2025). Quantile-Covariance Three-Pass Regression Filter. University of California at Riverside, Department of Economics Working Papers.
7. Padha, D., & Lee, T. H. (2025). Forecasting Using Supervised Factors and Idiosyncratic Elements. Available at SSRN 5111157.
8. Lee, T. H., & Padha, D. (2025). Forecasting Using Supervised Factors and Idiosyncratic Elements. University of California at Riverside, Department of Economics Working Papers.
9. Lee, T. H., & Wang, T. (2025). Estimation and testing of forecast rationality with many moments. Macroeconomic Dynamics, 29, Article e124.
10. Lee, T. H., Ullah, A., & Wang, H. (2024). The second-order bias and mean squared error of quantile regression estimators. Indian Economic Review, 59(Suppl 1), 11–68.
11. Hao, H., Huang, B., & Lee, T. (2024). Model averaging estimation of panel data models with many instruments and boosting. Journal of Applied Statistics, 51(1), 53–69.
12. Lee, T. H., & Seregina, E. (2024). Optimal portfolio using factor graphical lasso. Journal of Financial Econometrics, 22(3), 670–695.
13. Lee, T. H., Seregina, E., & Xu, Y. (2023). Elicitability and Encompassing for Volatility Forecasts by Bregman Functions. University of California at Riverside, Department of Economics Working Papers.
14. Sun, Y., Hong, Y., Wang, S., et al. (2023). Penalized time-varying model averaging. Journal of Econometrics, 235(2), 1355–1377.
15. Golan, A., Lee, T. H., Mao, M. Y., et al. (2023). A Flexible Information Theoretic Approach for Inference of Multiple Regression Function and Marginal Effects. Available at SSRN 4435051.
16. Lee, T. H., Wang, H., Xi, Z., et al. (2023). Density forecast of financial returns using decomposition and maximum entropy. Journal of Econometric Methods, 12(1), 57–83.
17. Lee, T. H., Parsaeian, S., & Ullah, A. (2022). Forecasting under structural breaks using improved weighted estimation. Oxford Bulletin of Economics and Statistics, 84(6), 1485–1501.
18. Lee, T. H., Parsaeian, S., & Ullah, A. (2022). Optimal forecast under structural breaks. Journal of Applied Econometrics, 37(5), 965–987.
19. Lee, T. H., Parsaeian, S., & Ullah, A. (2022). Efficient combined estimation under structural breaks. Econometric Reviews, 41(4), 433–459.
20. Banafti, S., & Lee, T. H. (2022). Inferential theory for granular instrumental variables in high dimensions. arXiv preprint arXiv:2201.06605.

Profile Details

https://faculty.ucr.edu/~taelee/
https://scholar.google.com/citations?user=gaHXhcwAAAAJ&hl=en
https://www.researchgate.net/scientific-contributions/Tae-Hwy-Lee-11148402

WOS ResearcherID: FJG-5859-2022