Eoghan O’Neill
Biographic Data
| ID | 6563519 |
|---|---|
| NAME | Eoghan O’Neill |
| GIVEN NAMES | Eoghan |
| FAMILY NAME | O’Neill |
| SIGNATURE | O’NEILL E |
| AFFILIATIONS | University of Cambridge |
| ORCID | 0000-0002-1274-4248 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Static and Dynamic Bart for Rank-Order Data
Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with l…
The benefits of forecasting inflation with machine learning
Medeiros et al. (2021) (Journal of Business & Economic Statistics, 39:1, 98–119) find that random forest (RF) outperforms US inflation forecasting benchmarks. We replicate the main results in Medeiros et al. (2021) and (1) considerably expand the set of machine learning methods, (2) analyse the predictive ability of both the initial and extended sets of methods on Canadian and UK data, (3) add results on coverage rates and widths of prediction in…
The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis
No prominent works on this page.
The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis
The benefits of forecasting inflation with machine learning
Medeiros et al. (2021) (Journal of Business & Economic Statistics, 39:1, 98–119) find that random forest (RF) outperforms US inflation forecasting benchmarks. We replicate the main results in Medeiros et al. (2021) and (1) considerably expand the set of machine learning methods, (2) analyse the predictive ability of both the initial and extended sets of methods on Canadian and UK data, (3) add results on coverage rates and widths of prediction in…
Static and Dynamic Bart for Rank-Order Data
Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with l…
Econometrics (2 works) · Economics (2 works) · Mathematics (2 works) · Artificial Intelligence (1 work) · Autoregressive model (1 work) · Bayesian probability (1 work) · Blind Source Separation Techniques (1 work) · Computer Science (1 work) · Data set (1 work) · Distribution (mathematics (1 work)