Probabilistic Time Series Forecasting Challenge, Project 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 real-time 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 real-world 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)

Statistics 1 Lecture (BA)

For more information, please refer to the website of the courses Statistics 1 and 2.

Seminar (BA/MA)

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 real-time 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 real-world 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)

• Statistics I, Summer 2023

• Statistics II, Winter 2023/24

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

• Microeconometrics (Lecture and Tutorial), Winter 2022/23 and Summer 2024

• Predictive Modeling (Lecture and Tutorial), since Summer 2020

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

• VWL III (Lecture), Summer 2022

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