The course will be a block lecture including tutorials and student presentations. 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. 

In particular, the course will likely cover the following topics: 

• Regression Shrinkage Methods (Ridge, Lasso, Elastic Net) 

• Decision Trees, Random/Causal Forests 

• Advanced Identification Strategies (e.g., Double Machine Learning) 

• Introduction to Neural Networks 

1 st part of the course (via Zoom): In the first meeting, we will briefly discuss the econometric methods. In this meeting I will also assign a (replication) project to each student, which (s)he will present at the second part of the course. Students are supposed to work on the replication project before the second part of the course begins. https://tum-conf.zoom.us/j/6610117956 (access code in a separate mail)

2 nd part of the course (in person): The second part of the course will include lectures and tutorials. Moreover, the students will discuss their (replication) projects similar to a reading course. In the morning sessions, we will discuss the econometric methods and/or machine learning techniques (including some applications to illustrate them). Students will then replicate recent research papers in economics and will present their projects in the afternoon sessions. All participants are expected to read the papers before the meetings. The presentation (roughly 30 minutes) together with a short report that summarizes the assigned paper (roughly 5 pages w/o figures, tables and references) will be relevant for the grading.