Staff profile

Hong Il joined the Business School in September 2013. His recent research focuses on the econometric analysis of individual preferences, using data from stated preference surveys and economic experiments.

Prior to his current appointment, he studied economics at the University of New South Wales, Australia.

More information is available on his personal website.

Mini Biography

Hong Il joined the Business School in September 2013. His recent research focuses on the econometric analysis of individual preferences, using data from stated preference surveys and economic experiments.

Prior to his current appointment, he studied economics at the University of New South Wales, Australia.

• Behavioural Economics
• Econometrics
• Health Economics

• Economics & Finance Department
• Experimental Methods in Business Research

Chapter in book

• Lau, M. I., Yoo, H. I. & Zhao, H. (2021). Temporal Stability of Cumulative Prospect Theory. In Models of Risk Preferences: Descriptive and Normative Challenges. Harrison, G. W. & Ross, D Bingley, UK: Emerald, Research in Experimental Economics.
• Fiebig, D.G. & Yoo, H. I. (2019). Econometrics of Stated Preferences. In The Oxford Encyclopedia of Health Economics. Jones, Andrew M. Oxford: Oxford University Press.

Journal Article

• Gu, Ariel & Yoo, Hong Il (2021). Prospect Theory and Mutual Fund Flows. Economics Letters 201: 109776.
• Yoo, H.I. (2020). lclogit2: An enhanced command to fit latent class conditional logit models. Stata Journal 20(2): 405-425.
• Doiron, D. & Yoo, H.I. (2020). Stated preferences over job characteristics: a panel study. Canadian Journal of Economics 53(1): 43-82.
• Gu,A. & Yoo,H.I. (2019). vcemway: A one-stop solution for robust inference with multi-way clustering. Stata Journal 19(4): 900-912.
• Moffat, J. & Yoo, H.I (2020). Religion, Religiosity and Educational Attainment: Evidence from the Compulsory Education System in England. Applied Economics 52(4): 430-442.
• Harrison, G.W., Lau, M.I & Yoo, H.I (2020). Risk Attitudes, Sample Selection and Attrition in a Longitudinal Field Experiment. Review of Economics and Statistics 102(3): 552-568.
• Yan, Jin & Yoo, Hong Il (2019). Semiparametric estimation of the random utility model with rank-ordered choice data. Journal of Econometrics 211(2): 414-438.
• Groothuis-Oudshoorn, C.G.M., Flynn, T.N., Yoo, H.I., Magidson, J. & Oppe, M. (2018). Key Issues and Potential Solutions for Understanding Health Care Preference Heterogeneity Free from Patient Level Scale Confounds. The Patient - Patient-Centered Outcomes Research 11(5): 463-466.
• Hole, A.R. & Yoo, H.I. (2017). The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models. Journal of the Royal Statistical Society: Series C 66(5): 997-1013.
• Oviedo, J.L. & Yoo, H.I. (2017). A latent class nested logit model for rank-ordered data with application to cork oak reforestation. Environmental and Resource Economics 68(4): 1021-1051.
• Doiron, D. & Yoo, H.I. (2017). Temporal stability of stated preferences: the case of junior nursing jobs. Health Economics 26(6): 802-809.
• Abul Naga, R. H., Shen, Y. & Yoo, H. I. (2016). Joint Hypothesis Tests for Multidimensional Inequality Indices. Economics Letters 141: 138-142.
• Moffat, J. & Yoo, H. I. (2015). Who are the unemployed? Evidence from the United Kingdom. Economics Letters 132: 61-64.
• Yoo, H. & Doiron, D. (2013). The use of alternative preference elicitation methods in complex discrete choice experiments. Journal of Health Economics 32(6): 1166-1179.
• Pacifico, D. & Yoo, H. (2013). lclogit: A Stata command for fitting latent-class conditional logit models via the expectation-maximization algorithm. Stata Journal 13(3): 625-639.