AUEB Among the Top 10 Finalists at the Econometric Game 2026听|听Open Seminar on the Finalist Paper:听Multivariate Electricity Price Forecasting听|听Monday, 15 June | 15:00鈥�16:30 | Troias Amphitheater
The 黑料科 of Economics and Business has once again achieved international distinction, advancing to the听Top 10 Finalist Institutions听at the听27th Econometric Game, hosted by the University of Amsterdam and widely regarded as the World Championship of Econometrics.
For the听second consecutive year, AUEB secured a place among the competition鈥檚 top finalists, competing against teams from 30 leading universities worldwide such as Harvard, Oxford, National University Singapore etc.
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Our听PhD in Economics听students,听George Malanos听(Team Leader) and听Christina Logotheti, and Master鈥檚 students听Vasileia Argyrou听and听George Skolarikis听from the听MSc in Business Economics with Analytics听tackled a highly relevant global challenge in energy economics:
Multivariate electricity price forecasting听using robust analytical models and dynamic, real-world data, including changing weather patterns, renewable energy production, and cross-border electricity flows.
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The AUEB finalist paper will be presented at an open seminar for the university's academic community on听Monday, 15 June (15:00 - 16:30)听at the听Troias Amphitheater. Those wishing to attend are kindly requested to complete the registration form available at:听
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Methodological Innovation
To address the complexity of the problem, the team implemented a sophisticated multi-layered forecasting strategy combining econometric and machine-learning techniques:
- Core Modeling: A HAR/UMIDAS approach tailored specifically for mixed-frequency data.
- Predictions & Refinement:听Tree-based Machine Learning methods for out-of-sample predictions, utilizing aggressive Recursive Feature Elimination for optimal parsimony.
- Explainability:听Shapley Additive Values (SHAP) were used to translate complex models into concrete, actionable policy recommendations.
- Advanced Forecasting: STL Decomposition utilizing Harmonic Regressions, Seasonal Na茂ves, and Random Walks with drift to move past naive univariate approaches for unobservable out-of-sample variables.
This achievement reflects the strength of AUEB鈥檚 research-driven education and its commitment to developing analytical talent capable of addressing complex global challenges. We are proud to see our students applying cutting-edge econometric and data-analytics methods to real-world problems and representing AUEB with distinction on the international stage.
