Summer 24
Predictive Modeling Lecture & Tutorial (MA)
This course presents methods for making and evaluating statistical predictions based on data. We consider various types of predictions (mean, probability, quantile, and full distribution), all of which are practically relevant. In each case, we discuss selected modeling approaches and their implementation using R software. We consider various economic case studies. Furthermore, we present methods for absolute evaluation (assessing whether a given model is compatible with the data) and relative evaluation (comparing the predictive performance of alternative models).
Microeconometrics Lecture & Tutorial (BA)
Microeconometrics is concerned with modeling data from an individual (`micro') unit like a person, household or firm. The response variables of interest are often discrete. For example, a person's type of employment may be coded as a binary variable (e.g. working in IT sector versus not working in IT sector), and a person's choice of transportation mode can be cast as a multinomial variable (e.g. bike, train, car, or other). These examples differ from the basic econometric setting of a continuous response variable, and require nonlinear regression modeling.
Teamproject Project (BA)
A team of KIT students participated at the Explore Science fair in Mannheim (June 1216). Their contribution, developed in a course project supervised by our group, is a game in which players build a dam in order to insure themselves against random amounts of water. The game was very well received, with many players eager to beat the high score. Thanks to our students for this great effort!
Seminar (BA/MA)
Winter 24/25
Probabilistic Time Series Forecasting Challenge, (Projekt)Seminar (BA/MA)
Statistical forecasts are relevant across all fields of society. In this data science project, students make, evaluate and communicate their own statistical forecasts in a realtime setting. We consider probabilistic forecasts that involve a measure of uncertainty in addition to a point forecast. Students are asked to make forecasts of several realworld time series such as hourly energy demand. Historical data on all series are available from public sources that are updated as time proceeds. While the time series differ from each other in important ways, statistical methods can meaningfully be used for prediction in all cases. We focus on quantile forecasts which are useful to measure forecast uncertainty in a relatively simple way.
Applied Econometrics Lecture & Tutorial (MA)
Applied econometrics is concerned with answering causal questions (e.g., "How does an internship affect a person's future wage?") and making predictions (e.g., "What is the expected rental price for an apartment, given its size and location?"). This course presents econometric methods for these tasks, with an emphasis on causal inference.
Seminar (BA/MA)
Summer 2025
Statistics 1 Lecture (BA)
For more information, please refer to the website of the courses Statistics 1 and 2.
Seminar (BA/MA)
Winter 25/26
Statistics 2 Lecture (BA)
For more information, please refer to the website of the courses Statistics 1 and 2.
Probabilistic Time Series Forecasting Challenge, (Projekt)Seminar (BA/MA)
Statistical forecasts are relevant across all fields of society. In this data science project, students make, evaluate and communicate their own statistical forecasts in a realtime setting. We consider probabilistic forecasts that involve a measure of uncertainty in addition to a point forecast. Students are asked to make forecasts of several realworld time series such as hourly energy demand. Historical data on all series are available from public sources that are updated as time proceeds. While the time series differ from each other in important ways, statistical methods can meaningfully be used for prediction in all cases. We focus on quantile forecasts which are useful to measure forecast uncertainty in a relatively simple way.
Applied Econometrics Lecture & Tutorial (MA)
Applied econometrics is concerned with answering causal questions (e.g., "How does an internship affect a person's future wage?") and making predictions (e.g., "What is the expected rental price for an apartment, given its size and location?"). This course presents econometric methods for these tasks, with an emphasis on causal inference.
Seminar (BA/MA)
Past Teaching

Statistics I, Summer 2023

Statistics II, Winter 2023/24

Applied Econometrics (Lecture and Tutorial), since Winter 2019/20

Predictive Modeling (Lecture and Tutorial), since Summer 2020

Probabilistic Time Series Forecasting Challenge (Projectseminar; with J. Bracher, N. Koster and S. Lerch), since Winter 2021/22

Microeconometrics (Lecture and Tutorial), since Winter 2022/23

VWL III (Lecture), Summer 2022

Multiple econometrics seminars (with M. Schienle, R. Buse, and L. Rüter and others)