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 modules will be covered in this course:

1. Statistical Learning and the Role of Econometrics
2. Causality and DAGs
3. Threats to Causal Identification
4. Instrumental Variables
5. Non-Linear Models and Maximum Likelihood Estimation
6. More Identification Strategies

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, 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 or your experience in Econometrics I.

After this course, you

• will be equipped to conduct advanced econometric analyses,
• will be aware of common pitfalls and how they may be dealt with,
• will have a solid understanding of causal inference,
• will be able to independently apply your knowledge using R, and you
• will be able to critically review applied research.

Attendance is compulsory. Students are allowed to miss up to two units.

The course consists of 

• Lectures with focus on econometric theory,
• examples of applications during the lectures,
• and a project where you independently apply what we learned in the course, either alone or in groups.

Assessments are based on three components. 

• 30% Project
• 30% First partial exam on November 20
• 30% Second partial exam on January 22
• 10% Active participation

The grading scheme is

1. [87.5, 100]
2. [75, 87.5)
3. [62.5, 75)
4. [50, 62.5)
5. [0, 50)

Additionally, to pass the course, you must fulfill both of the following criteria:

• Obtain at least half of the available project points.
• Obtain at least half of the combined points from the first and second partial exams.

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

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maximilian.heinze@wu.ac.at, sannah.tijani@wu.ac.at