Teaching and Supervision

Courses

Economic Policy for Sustainable Energy (TPM0637SET): The course – an introduction to environmental and ecological economics focused on energy – runs annually in the third quarter. It is designed for students in the Sustainable Energy Technology MSc program, but it is open to students from other programs as well.

Statistical Methods for Causal Inference and Prediction (TPM039A): The course introduces modern methods for causal inference based on observational data. MSc students from all programs interested in evidence-based policy making are welcome to enroll. MSc students who take the course in the first quarter of the second year can fruitfully apply the methods learned in their master thesis projects.

Systems Science for Health and Care (SEN181A): The course is part of the Health Track in the Complex Systems Engineering and Management MSc program. I teach a module on causal inference in public health.

To access course materials, please enroll for the courses on Brightspace.

I used to co-teach Macroeconomics for Policy Analysis, together with Servaas Storm, and also Preparation for the Master Thesis.

Master Thesis Supervision

I supervise MSc students in various programs (EPA, MOT, COSEM, IE, SET, etc.) interested in doing quantitative empirical research on economic problems. The thematic domain is broad and includes research problems related to the energy transition, housing, and labor markets.

In the academic year 2025/26, I’m particularly interested in projects related to the “housing affordability crisis” and the “European renovation wave”. The “Woononderzoek Nederland (WoON)” is one data set that could be used to tackle questions in either of these domains, but there are many others.

The exact research question is open – you define it together with me. There are many ways to develop a good research question. One of the quickest ways is to browse the scientific literature in order to identify a “role model.” By role model I mean a published scientific article i) that you like and ii) that more or less does what you want to do. You write your thesis and learn to do research by emulating the role model.

I prefer projects that involve the application of quantitative empirical methods. Most of my students use either input-output modeling or statistical methods for causal inference (e.g. regression, difference-in-differences, synthetic control).

Input-output models are not overly complicated, which means that you can start from scratch and learn by doing (i.e. while writing your master thesis). If you go this route, the textbook “Input-Output Analysis: Foundations and Extensions” by Miller & Blair should become your friend (read the first two chapters to get an impression). Input-output analysis is usually done in Matlab, R, or Python.
To apply quantitative statistical methods, you should have a background in probability, statistics, and linear regression before the start of the master thesis project, and you should be willing to learn new statistical methods by yourself using introductory textbooks such as “Introductory Econometrics” by Wooldridge, “Introduction to Econometrics” by Stock & Watson, “The Mixtape” by Cunningham, and “The Effect” by Huntington-Klein. Skills in ex-ante modeling and numerical simulations cannot make up for a lack of training in statistics.

Your thesis should constructively use economic theory and bear implications for economic policymaking. You should have passed at least one master-level course in economics; even better, you are doing the elective package “Economics and Finance”; ideally, you are taking the course “Statistical Methods for Causal Inference and Prediction.” Experience with empirical research and scientific programming (e.g., Stata, R, Julia, Python) is helpful but not essential.