Seminars , Courses and Events Quantitative Economics

MLSE is a (mostly) bi-weekly seminar to foster cooperation between the Department of Microeconomics and Public Economics and the Department of Quantitative Economics. It aims to give researchers the opportunity to present their ongoing work and to facilitate cooperation

Website of MLSE : https://www.maastrichtuniversity.nl/mlse-seminar

In case you want to follow the seminar online, please let us know. Also let us know whenever you know people that would like to receive these emails.

If you would like to present in this seminar series, please send an email to either @Bodicky, Michal (ALGEC) or @Triêu, Anh (KE).

Seminar: 

Wednesday 3 June, 12:30-13:30, which will contain two presentations.

Room: TS53 A1.22

1^st talk: 12:30-13:00
Speaker: Pauline Hanssen
Title: Core selection with single-peaked preferences and endowment ownership
- Joint work with Bas Dietzenbacher and Yuki Tamura
Abstract: This paper studies reallocation problems where preferences are single-peaked and one agent (the supplier) is in possession of one unit of an infinitely divisible and non-disposable commodity. We characterize all allocations that belong to the core, i.e. no group of agents is better off by reallocating among themselves. Under excess supply, we show that all rules satisfying Pareto optimality and individual rationality select from the strong core. Under excess demand, we show that all rules satisfying Pareto optimality, individual rationality, and strategy-proofness select from the weak core, but no rule satisfies strategy-proofness and strong core selection.

2^nd talk: 13:00-13:30
Speaker: Luke Servat
Title: Optimal Investment for Retirement with Multiplicative External Habit Formation
- Joint work with Antoon Pelsser
Abstract: Differences in pensions between generations and cohorts have become a great worry for both retirees and funds, due to the tendency to transition to a defined contribution plan. Therefore, this paper investigates the optimal investment strategy for a cohort that evaluates their pension relative to an exponentially weighted moving average of pensions of past cohorts. More specifically, we find a closed-form solution to an optimal terminal wealth problem with external multiplicative habit formation. Next to this life-cycle strategy, a collective solution is considered in case risk-sharing is allowed in the fund.

Date and time: Wednesday 17 June, 12:30-13:30 PM.

Room: TS53 A1.22

Author: Javier Bas (Universidad Autónoma de Madrid)

Title: Classification of potential electric vehicle purchasers: A machine learning approach

Abstract: Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption.