This course covers advanced subjects in econometrics, focusing on temporal and panel data. Students will test their econometric skills via application in the context of a seminar paper. Applied examples and assignments will be laid out to use the R language.

The following topics will be covered in this course:

• Time series regression (stationarity, unit roots, cointegration, multivariate time series)
• Panel methods (fixed effects, diff-in-diff, dynamic panels)
• Research project (independent application or replication)

Prior knowledge of the following topics is expected:

• Multivariate regression (application, interpretation)
• Regression properties (least squares estimation, estimator properties, Gauss-Markov theorem)
• Regression inference (hypothesis testing, confidence intervals, model selection)
• Endogeneity (omitted variables, simultaneity, data errors)

These are covered in Econometrics I and II -- 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 independently conduct advanced econometric analyses. You should be able to replicate empirical studies published in scientific journals and use econometric methods to pursue your own research questions.

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 on time series and panel regression, while the second half will focus on developing the research projects. Students present their work over the semester and hand in a seminar paper at the end.

Assessment will be based on three components:

• 40% assignments and exam
• 30% presentations
• 30% research paper

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 required.

Successful completion of Econometrics II 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.