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Michael Qingliang Fan
Michael Fan is an Associate Professor of Economics at the The Chinese University of Hong Kong. He is interested in both theoretical and applied econometrics, with a focus on micro-econometrics in a data-rich environment, and machine learning in finance. Some of his ongoing research projects relate to labor economics, financial economics and decision science.
Michael Fan 范青亮
Department of Economics
The Chinese University of Hong Kong
Shatin, N.T., Hong Kong
Tel: (852) 3943-8001
Office: Room 903, Esther Lee Building, The Chinese University of Hong Kong
Links:
Github
publons
ReserchGate page
IDEAS page
ORCID
News:
2024.11 Two of my PhD students, Ziwei MEI and Ruike WU, are on the job market this year and looking for academic jobs. I am more than happy to provide a letter to any inquiries regarding their qualifications.
2024.11 The paper, Endogenous Treatment Effect Estimation with a Large and Mixed Set of Instruments and Control Variables, with Yaqian Wu, is published in the November 2024 issue of The Review of Economics and Statistics
2024.09 New working paper: Robust Bond Risk Premia Predictability Test in the Quantiles
2024.09 New working paper: Shocks-adaptive Robust Minimum Variance Portfolio for a Large Universe of Assets
2024.07 The paper A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates, with Ziwei Mei and Zijian Guo, is forthcoming at Journal of Business & Economic Statistics.
2024.06 Updated working paper: Inference for nonlinear endogenous treatment effects accounting for high-dimensional covariate complexity, with Z. Guo, Z. Mei and C.-H. Zhang. The paper proposes a double bias correction procedure in a high-dimensional model where the marginal effect function is nonparametric.
2024.03 New working paper: Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints
2024.01 New working paper: Robust Inference for Multiple Predictive Regressions with an Application on Bond Risk Premia
2023.12 Updated version of working paper: On the instrumental variable estimation with many weak and invalid instruments (final version published online in Journal of the Royal Statistical Society Series B, March 2024)
2023.10 New working paper: Uniform Inference for Nonlinear Endogenous Treatment Effects with High-Dimensional Covariates
2022. 12 R package "hdcate" for my paper: Fan et al. 2022 JBES is online
Hiring:
Full-time or part-time research assistant/fellow (both work in Hong Kong only). Get in contact (michaelqfan 'at' cuhk 'dot' edu 'dot' hk) if you are interested in academic careers. Please send a personal statement (emphasizing on past research experience and programming skills) and a writing sample. Preferred qualifications: Bachelor degree in Economics, Finance, Statistics, Computer Science, or other related subjects. Required qualifications: proficiency in at least one major statistical software, coursework in quantitative methods. Candidates whose research interests and expertise align with my ongoing research projects are welcome to apply. The successful applicant is expected to be a co-author of econometrics/statistics/machine learning publications and disseminate research findings at professional conferences.
Department of Economics
The Chinese University of Hong Kong
Shatin, N.T., Hong Kong
Tel: (852) 3943-8001
Office: Room 903, Esther Lee Building, The Chinese University of Hong Kong
Links:
Github
publons
ReserchGate page
IDEAS page
ORCID
News:
2024.11 Two of my PhD students, Ziwei MEI and Ruike WU, are on the job market this year and looking for academic jobs. I am more than happy to provide a letter to any inquiries regarding their qualifications.
2024.11 The paper, Endogenous Treatment Effect Estimation with a Large and Mixed Set of Instruments and Control Variables, with Yaqian Wu, is published in the November 2024 issue of The Review of Economics and Statistics
2024.09 New working paper: Robust Bond Risk Premia Predictability Test in the Quantiles
2024.09 New working paper: Shocks-adaptive Robust Minimum Variance Portfolio for a Large Universe of Assets
2024.07 The paper A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates, with Ziwei Mei and Zijian Guo, is forthcoming at Journal of Business & Economic Statistics.
2024.06 Updated working paper: Inference for nonlinear endogenous treatment effects accounting for high-dimensional covariate complexity, with Z. Guo, Z. Mei and C.-H. Zhang. The paper proposes a double bias correction procedure in a high-dimensional model where the marginal effect function is nonparametric.
2024.03 New working paper: Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints
2024.01 New working paper: Robust Inference for Multiple Predictive Regressions with an Application on Bond Risk Premia
2023.12 Updated version of working paper: On the instrumental variable estimation with many weak and invalid instruments (final version published online in Journal of the Royal Statistical Society Series B, March 2024)
2023.10 New working paper: Uniform Inference for Nonlinear Endogenous Treatment Effects with High-Dimensional Covariates
2022. 12 R package "hdcate" for my paper: Fan et al. 2022 JBES is online
Hiring:
Full-time or part-time research assistant/fellow (both work in Hong Kong only). Get in contact (michaelqfan 'at' cuhk 'dot' edu 'dot' hk) if you are interested in academic careers. Please send a personal statement (emphasizing on past research experience and programming skills) and a writing sample. Preferred qualifications: Bachelor degree in Economics, Finance, Statistics, Computer Science, or other related subjects. Required qualifications: proficiency in at least one major statistical software, coursework in quantitative methods. Candidates whose research interests and expertise align with my ongoing research projects are welcome to apply. The successful applicant is expected to be a co-author of econometrics/statistics/machine learning publications and disseminate research findings at professional conferences.
