This course covers advanced subjects in econometrics, focusing on causal inference and model building. We will cover common problems of regression analysis and potential remedies. Applied examples and assignments will be laid out to use the R language.
Topics covered in this course include endogeneity (omitted variables, simultaneity, data errors), instrumental variables (two-stage least squares), simultaneous equations models (seemingly unrelated regression), causal effects (prerequisites, model building, diff-in-diff), ...
After this course you will be equipped to conduct advanced econometric analyses. You will be aware of common pitfalls and how they may be dealt with. That includes a solid understanding of causal inference. You will be able to independently apply your knowledge using R and critically review applied research.
Attendance is compulsory.
The course consists of lectures with focus on econometric theory as well as practical issues. Assignments are designed to use R and will be worked on in groups.
Assessment will be based on three components:
- 40% group assignments
- 20% midterm exam
- 40% final exam
Sound knowledge of basic statistics, mathematics and matrix algebra. Successful completion of Econometrics I and basic knowledge in R is highly recommended.