If you are interested in data analysis, visit the seminar matching tool and select a high weight for this seminar.
Seminar Data Analysis (Master)
Each topic in this seminar consists of a recent paper at the frontier of current research. All papers are applied papers that focus on exciting applications over a broad range of topics. Each paper also takes you to the methodolgical frontier by using data analysis methods that slightly extend the basic methods you should have encountered in a previous lecture course (see requirements below). The idea is to learn about the topics, but also understand a few new methods. The new methods are close enough to what you know already that everybody should be able to understand them after the seminar.
The Seminar requirements consist of a paper, a presentation, and active participation in class discussion. The presentations will be blocked in June/July. I am flexible regarding timing and will decide together with the participants when the presentations will take place.
Applications and Registration
Applications for seminars is organized via web-based central seminar matching. Please sign in with your university mail account on following website:
Relevant deadlines for application are displayed within the tool. If you urgently require a place in a seminar it is recommended to sign in in several lists. According to the timeline you will receive feedback which seminar you are registered for.
We have seminar places for up to 15 master students.
The application concerns the seminar in general, there is no application for specific seminar topics possible! After you are registered for the seminar, topics will be allocated taking into account student’s preference. You will be informed about this and all further steps in more detail via e-mail after you are registered for the seminar.
Ths seminar requires a basic understanding of prediction and machine learning methods (cross-validation, LASSO, random forests) and/or methods of causal inference (treatment effects, diff-in-diff sn instrumetal variable estimation) on the level that is acquired e.g. in the classes Data Analysis (Gebhardt) or Market Analysis with Econometrics and Machine Learning (Kranz).
1. Predicting Risk Premia
This paper uses machine learning methods to predict riks premia.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
2. Predicting Bail Decisions
This paper uses machine learning methods to predict whether a criminal will commit another crime to decide whether the criminal should be released on bail. These deciaions are then compared to decisions by judges. Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. The quarterly journal of economics, 133(1), 237-293.
3. Identifying Customers to Target
This paper combines machine learning methods with causal inference to find customers that should be targeted to prevent customer churn.
Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research, 55(1), 80-98.
4. Robots and Jobs
This paper uses so called Bartik instruments and a diff-in-diff set-up to find out whether robots take away jobs from human beings.
Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188-2244.
5. Family Welfare Culture
This paper uses the personal characteristics of numoerous (welfare) judges to find out whether living on welfare transmits from parents to children.
Dahl, G. B., Kostøl, A. R., & Mogstad, M. (2014). Family welfare cultures. The Quarterly Journal of Economics, 129(4), 1711-1752.
6. The Value of Connections in Industrialized Countries
This paper uses a synthetic control approach to find out whether the old firms of the man, who became US Treasury Secretary, profit from his appointment.
Acemoglu, D., Johnson, S., Kermani, A., Kwak, J., & Mitton, T. (2016). The value of connections in turbulent times: Evidence from the United States. Journal of Financial Economics, 121(2), 368-391.
7. Football Stars and Taxation
This paper uses a synthetic control approach to find out whether countries can use attractive tax regimes to attract the best football players (or maybe other talent).
Kleven, H. J., Landais, C., & Saez, E. (2013). Taxation and international migration of superstars: Evidence from the European football market. American economic review, 103(5), 1892-1924.