Teaching

Applied Microeconomics (PhD/MSc)

An introduction to applied microeconomic methods for PhD students, which Florian Zimmermann and I have been teaching since 2021. See here for the course book.

Applied Data Analytics (BSc)

A brand-new course in the first term of our brand-new BSc programme! Taught together with Aapo Stenhammar since Autumn 2024, the course introduces students to the practical application of data analysis using the Python programming language. Each week, students work collaboratively on case studies to gain hands-on experience in data analytics. The course focuses on three key areas:

  1. Programming Techniques: Students learn essential programming techniques that enable them to confidently utilize computers as powerful tools for analyzing economic relationships. This includes skills such as graphical representation of functions, data simulation, and visualization of data distributions.

  2. Descriptive Statistics: Students practice applying descriptive statistical measures to real-world datasets, developing an understanding of how to summarize and interpret data effectively.

  3. Interpretation Challenges: The course emphasizes the importance of critically evaluating empirical correlations and avoiding common pitfalls in data interpretation. Students learn to recognize and address issues such as prematurely inferring causal relationships from correlations and failing to account for selection effects.

By combining practical programming skills with a solid foundation in statistical analysis and critical thinking, this course equips students with the necessary tools to effectively analyze and interpret complex datasets in various economic contexts. Through collaborative group work and hands-on case studies, students gain valuable experience in applying data analytics techniques to real-world problems.

There is a companion website organised by chapter, which contains videos on all methodological aspects (statistics and programming) along with some background material.

Effective programming practices for economists (PhD, MSc)

Many economists spend much of their lives in front of a computer, analysing data or simulating economic models. Surprisingly few of them have ever been taught how to do this well. Class exposure to programming languages is most often limited to mastering {Stata, Python, R, Matlab, Julia, …} just well enough in order to perform simple tasks like running a basic regression. However, these skills do not scale up in a straightforward manner to handle complex projects such as a Master’s thesis, a research paper, or typical work in government or private business settings. As a result, economists spend their time wrestling with software, instead of doing work, but have no idea how reliable or efficient their programs are.

This course is designed to help fill in this gap. It is aimed at PhD students who expect to write their theses in a field that requires modest to heavy use of computations. MSc students expecting to write on a similar topic are invited to join as well. Examples include applied microeconomics, econometrics, macroeconomics, computational economics — any field that either involves real-world data; or that does not generally lead to models with simple closed-form solutions.

We will introduce students to programming methods that will substantially reduce their time spent programming while at the same time making their programs more dependable and their results reproducible without extra effort. The course draws extensively on some simple techniques that are the backbone of modern software development, which most economists are simply not aware of. It shows the usefulness of these techniques for a wide variety of economic and econometric applications by means of hands-on examples.

Together with Janoś Gabler, we gave the course a huge overhaul in 2023. See here for the 2024/2025 course site. There is a website with videos and quizzes that you can use to learn the material on your own. It is ordered by topic and acts like a textbook. Each run of the course will have an accompanying site, which will guide students (non-linearly) through these materials, adding exercises and assignments. Feedback is greatly appreciated!

Public Finance and Social Policy (BSc)

This course is taught in German (“Finanz- und Sozialpolitik”). Content-wise, students learn about the broad magnitudes of public finances and understand how to read official statistics. We put a lot of emphasis on gauging the (fiscal) impact of policy measures and the limits of making such assessments on the basis of empirical data alone. To generate quantitative results, students learn how to use Python, Pandas, and GETTSIM. The entire course is based on the flipped classroom concept. Before class, students acquaint themselves with the content based on texts of varying lengths, short screencasts, or programming exercises. Quizzes allow them to verify they understood the material. During class time, group activities ranging from joint coding to discussing the pros and cons of reforming aspects of the welfare state deepen their understanding.

I am not offering this course in the summer term 2025, but it will come back.

Past courses

I have been teaching a variety of courses over the years. This should be a reasonably exhaustive list:

  • Introductory Econometrics (at the time, a core course in the BSc)

  • Microeconometrics (elective course in the MSc)

  • Health economics (elective course in the BSc)

  • PhD Topics courses: Structural microeconomic modelling, The Economics of Social Insurance, Causality in economics and econometrics

  • Advanced Econometrics (core course in the PhD)

  • Computational econometric methods (core course in the Mannheim PhD programme)