Teaching & Supervision

I am the module manager of Economic Policy for Sustainable Energy (WM0637SET) and I teach the course annually in the third quarter. The course — an introduction to environmental and ecological economics — is designed for students in the Sustainable Energy Technology master program, but it is open to students from other programs as well.

With Servaas Storm I co-teach Macroeconomics for Policy Analysis (EPA1223). The course is part of the 1st-year core of the Engineering and Policy Analysis master program, and always runs in the fourth quarter.

I am the module manager of Statistical Methods for Causal Inference and Prediction (TPM039a), offered annually in the first quarter. This elective course is designed for 2nd-year master students who wish to learn about modern methods for causal inference based on observational data.

I used to be the module manager of Preparation for the Master Thesis (MOT2004) in the Management of Technology master program, but I no longer teach the course.

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

Supervision

I supervise master students in several different programs doing quantitative empirical research project in economics. The topical domain is broad and includes research problems related to the energy transition, climate change, globalization, and labor markets. Contact me if you are interested.

You develop the research question and define the project in consultation with me. You are encouraged to search the scientific literature in order to identify a “role model”, meaning a published scientific article that you like that more or less does what you want to do. You write your thesis and learn doing research by emulating the role model.

Your research question should be suitable for the application of quantitative empirical methods. Most of my students use either input-output modeling or some type of statistical method for prediction and 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 chapters 1 & 2 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 theory and 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. Knowledge of and skills in ex-ante modeling and numerical simulations cannot make up for a lack of training in statistics.

Your thesis must be written from an economic perspective, constructively use economic theory, and bear implications for economic policy making. You should have passed at least one master-level course in economics. Much better: you have completed the Economics and Finance specialization track in the TPM faculty, and you passed my course Statistical Methods for Causal Inference and Prediction with flying colors. Experience with empirical research and scientific programming (e.g. Stata, R, Matlab, Python) is helpful, but not essential.