0495 Econometrics II
Anna Matzner, MSc (WU)
Contact details
Weekly hours
Language of instruction
09/13/22 to 09/19/22
Registration via LPIS
Notes to the course
Day Date Time Room
Monday 10/10/22 01:00 PM - 03:00 PM TC.3.11
Monday 10/17/22 01:00 PM - 03:00 PM D2.0.392
Monday 10/24/22 01:00 PM - 03:00 PM TC.3.11
Monday 11/07/22 01:00 PM - 03:00 PM TC.3.11
Monday 11/14/22 01:00 PM - 03:00 PM TC.3.11
Monday 11/28/22 01:00 PM - 03:00 PM TC.3.11
Thursday 12/01/22 12:00 PM - 02:00 PM D3.0.222
Monday 12/05/22 01:00 PM - 03:00 PM TC.5.05
Monday 12/12/22 01:00 PM - 03:00 PM TC.3.11
Monday 12/19/22 01:00 PM - 03:00 PM TC.3.11
Monday 01/09/23 01:00 PM - 03:00 PM TC.3.11
Monday 01/16/23 01:00 PM - 03:00 PM TC.3.11
Thursday 01/19/23 09:00 AM - 11:00 AM TC.3.08
Monday 01/23/23 01:00 PM - 03:00 PM TC.2.03

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 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), ... 

Learning outcomes

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 and critically review applied research.

Attendance requirements

Attendance is compulsory. Students are allowed to miss up to 2 units (applies to on campus, hybrid, and online format).

Teaching/learning method(s)

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
Prerequisites for participation and waiting lists

Sound knowledge of basic statistics, mathematics and matrix algebra. Successful completion of Econometrics I and basic knowledge in R is highly recommended.

Availability of lecturer(s)
Last edited: 2022-06-01