The aim of the course is to help you making a link between empirical work and policy/business strategy advice. You learn about targeting treatments, how to build up an evidence base, and how to use the results of a study in other, related settings.
Econometrics I (MSc. Economics).
basic course in econometrics
This course is the follow-up to Econometrics 1. In Econometrics 1, we addressed identification of average treatment effects in a single study. In Econometrics 2, we go beyond average treatment effects because the assessment of policies and strategies often requires us to do so. These issues are of relevance to both micro and macroeconomics.
First, we allow for treatment heterogeneity. You learn how to estimate effects for subgroups of the population studied.
Second, we allow the treatment effect to vary with time. We look into panel data models were adjustment to some shock or treatment takes place over several time periods. Examples of applications are: shocks that percolate through a system (macro) and treatments that require learning (micro).
Third, we address mechanisms that are behind an estimated treatment effect. So far, the relationship between the treatment and outcome was a black box. How can we learn about mechanisms? This is of direct relevance to policy: what we should do greatly depends on what drives the outcome.
Fourth, we address what we can learn from the results of two or more studies, using basic Bayesian statistics. A judgment of whether a treatment ‘works’ is not based on a single study after all. How do we build an evidence base?
Fifth, we address external validity of studies. Empirical work is backward looking; it is based on things that occurred, but policy advice is forward looking: what can we learn from a study for the future and in other settings?
Type of instructions
lectures plus lab sessions
written final exam; weekly quizzes, paper reviews and computer assignments
Type of exams