The alternative mode for this course will be a mixed form, akin to the Hybrid Mode. Lectures will be held as usual for the appropriate amount of students, with video material (recorded and/or live) available for the rest. Lectures may also be held completely remotely via Teams, if deemed suitable or necessary.

Please note that in this case, the grading scheme may be subject to changes.

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.

The following topics will be covered in this course:

• 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)

Prior knowledge of the following topics is expected:

• Multivariate regression (application, interpretation)
• Regression properties (least squares estimation, classical assumptions, estimator properties, Gauss-Markov theorem)
• Regression inference (hypothesis testing, confidence intervals, model selection)
• Assumption failures and remedies (heteroskedasticity, serial correlation, multicollinearity)
• Functional forms (dummy variables, interaction terms, log-transformations)

These are covered in Econometrics I -- it is assumed that you have a solid understanding of them. In addition, it would be beneficial to have working knowledge of R, e.g. from the Statistics with R course.

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 -- under which conditions it is possible and how these may be induced. You will be able to independently apply your knowledge using R and critically review applied research.

Attendance is compulsory.

The course material will be presented in the form of slides, with accompanying assignments for better comprehension throughout. Assignments are designed to use R, will be worked on in groups, and need to be handed in in written form.
The first half of the course will focus more on econometric theory, while the second half will lean towards more practical issues. The midterm in December will cover these theoretical underpinnings. The final exam in the end of January will cover all of the course material.

Assessment will be based on three components:

• 40% group assignments
• 20% midterm exam
• 40% final exam

The grading scheme is as follows:

1. [88, 100]
2. [75, 88)
3. [62, 75)
4. [50, 62)
5. [0, 50)

Successful completion of Econometrics I is highly recommended.

nikolas.kuschnig@wu.ac.at

For a quick start to using R, see this short introduction. Many of this course's topics are covered in Introduction to Econometrics with R.

For more general and advanced material, see R for Data Science, and Advanced R.