Presentation
The course covers a selection of state-of-the-art methods in econometrics and machine
learning. It aims to provide students with a sound understanding of the methods discussed, such that
they are able to do research using modern econometric techniques, as well as critically assess existing
studies.
Please click here for the Syllabus.
Objectives
In particular, the course will likely cover the following topics:
• OLS and 2SLS (Recap)
• Regression Shrinkage Methods (Ridge, Lasso, Elastic Net)
• Decision Trees and Random Forests
• Causal Machine Learning (double machine learning, causal forest, effect heterogeneity)
• Difference-in-Differences, RDD estimation, IV-Lasso, IV validity tests and/or embeddings
Contact
Local Coordinator: Stefan Boes
Tuition fee
Registration (see course list)