The Institute for Econometrics and Data Science is situated at the intersection of econometrics, machine learning, and empirical finance. Econometric methods allow us to estimate the size of economically relevant parameters, such as the risk preferences of investors, and to make statements about the uncertainty of these estimates. New methods are continuously being developed to meet the specific requirements and conditions of current issues and datasets. Due to the wealth of data in the financial sector, machine learning methods have frequently been used in recent years to analyze patterns in observations flexibly. An exciting field of research arises from the question of how this flexibility can be combined with established financial theories and used to support classical econometric methods.
The teaching at the institute also deals with the research fields described above. The focus in undergraduate education is exclusively on methodology, while advanced master's courses increasingly highlight the intersections of methodology and empirical finance. We aim to open a wide range of career opportunities to students.
