Erasmus School of Economics
Assistant professor | Econometrics
- Email
- oneill@ese.eur.nl
Work
- Andrea A. Naghi, Eoghan O'Neill & Martina Danielova Zaharieva (2024) - The benefits of forecasting inflation with machine learning: New evidence - Journal of Applied Econometrics, 39 (7), 1321-1331 - doi: 10.1002/jae.3088 - [link]
- Eoghan O’Neill (2024) - Type I Tobit Bayesian Additive Regression Trees for censored outcome regression - Statistics and Computing, 34 (4) - doi: 10.1007/s11222-024-10434-4 - [link]
- Peng Hao, Jun Peng Guo, Eoghan O’Neill & Yong Heng Shi (2023) - When Will First-Price Work Well? The Impact of Anti-Corruption Rules on Photovoltaic Power Generation Procurement Auctions - Sustainability (Switzerland), 15 (4) - doi: 10.3390/su15043441 - [link]
- Wei Zhou, Alice Moncaster, Eoghan O'Neill, David M. Reiner, Xinke Wang & Peter Guthrie (2022) - Modelling future trends of annual embodied energy of urban residential building stock in China - Energy Policy, 165 - doi: 10.1016/j.enpol.2022.112932 - [link]
- Wei Zhou, Eoghan O’Neill, Alice Moncaster, David M. Reiner & Peter Guthrie (2022) - Modelling Chinese Urban Residential Stock Turnover Uncertainties Using System Dynamics and Bayesian Statistical Inference - doi: 10.1007/978-981-16-8352-7_14 - [link]
- KK Ni, BC Xie, Eoghan O'Neill & HZ Li (2021) - The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis - Utilities Policy, 69 - doi: 10.1016/j.jup.2021.101187 - [link]
- W Zhou, Eoghan O'Neill, A Moncaster, DM Reiner & P Guthrie (2020) - Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging - Applied Energy, 275 - doi: 10.1016/j.apenergy.2020.115388 - [link]
- ZY She, G Meng, BC Xie & Eoghan O'Neill (2020) - The effectiveness of the unbundling reform in China's power system from a dynamic efficiency perspective - Applied Energy, 264 - doi: 10.1016/j.apenergy.2020.114717 - [link]
