Quantitative / Empirical Methods and Data Science
The Quant group is dedicated to developing and utilizing advanced operations research, econometric, and experimental methods to generate and analyze relevant data. We firmly believe that it is important to not just rely on existing methods, but to actively involve ourselves in the development of new concepts and techniques to better understand and address the challenges and problems facing society. Whether through developing new methods, applying existing techniques to new contexts, or collaborating with other researchers and practitioners, the group is always striving to push the boundaries of what is possible and to contribute to theoretically sound and data-based solutions.
Some of the methods that work on include machine learning, optimization, forecasting, time series analysis, revealed preferences, experimental methods, simulation, panel data econometrics, micro econometrics, and benchmarking. These methods allow us to approach problems in a systematic and data-driven way, using rigorous analytical techniques to uncover insights and inform decision-making.
In terms of application areas, the group is focused on addressing a range of societal challenges, including energy, innovation, health, inequality, and transparency. We are committed to using our expertise to find theoretically sound and empirically based solutions.
Faculty
External Funding
International and national public and private foundations have supported the research carried out in our group. Below a list of current externally funded projects:
- Public Sector Performance Measurement (Peter Bogetoft). Rockwool Foundation.
- Benchmarking-based incentives and regulatory applications (Peter Bogetoft, Dolores Romero Morales, Aleksandrs Smilgins). Danish Research Council.
- H2020 MSCA RISE NeEDS (Peter Bogetoft, Dolores Romero Morales). EU.
- Market design for a decentralized integrated European energy transformation (Jens Weibezahn), H2020 project, European Commission.
Courses
Our group teaches courses in Operations Research, Game Theory, Econometrics, Time Series Analysis, Data Science, Machine Learning and Benchmarking at both BSc, MSc and PhD levels.
Publications
We carry out research at a high international level published in highly ranked journals. Below are a few recent examples:
Distinguishing useful and wasteful slack, Operations Research, 2022. (Peter Bogetoft)
Mix Stickiness under Asymmetric Cost Information, Management Science, 2019. (Peter Bogetoft)
Procurement with asymmetric information about fixed and variable costs, Journal of Accounting Research, 2018. (Peter Bogetoft)
Risk Attitudes, Sample Selection and Attrition in Longitudinal Field Experiment, Review of Economics & Statistics, 2020. (Morten Lau)
Asset Integration and Attitudes to Risk: Theory and Practice, Review of Economics & Statistics, 2018. (Morten Lau)
Multiattribute Utility Theory, Intertemporal Utility and Correlation Aversion, International Economic Review, 2018. (Morten Lau)
Interpreting Clusters via Prototype Optimization, Omega, 2022. (Dolores Romero Morales)
On Sparse Optimal Regression Trees, European Journal of Operational Research, 2022. (Dolores Romero Morales)
Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach, Omega, 2020. (Dolores Romero Morales)
Fertility, Economic Incentives and Individual Heterogeneity: Register Data‐based Evidence from France and Germany, Journal of the Royal Statistical Society, Series A, 2022. (Ralf Wilke)
Measuring the Ex-ante Incentive Effects of Creditor Control Rights during Bankruptcy Reorganization, Journal of Financial Economics, 2022. (Jimmy Martinez-Correa)
Responses to Eliminating Saving Commitments: Evidence from Mortgage Run-off, Journal of Money, Credit and Banking, 2022. (Jimmy Martinez-Correa)
Foreign Influence, Control, and Indirect Ownership: Implications for Productivity Spillovers, Journal of International Business Studies, 2020. (Lisbeth la Cour)
Goals, Constraints, and Transparently Fair Assignments: A Field Study of Randomization Design in the UEFA Champions League , Management Science, 2022. (Marta Boczon)