This course covers topics in econometrics and consists of three main parts:

1. Review of the linear regression model and extensions to be used when the usual assumptions are not fulfilled. Specifically, we discuss methods to be used when one or more of the regressors are endogenous (i.e., instrumental variable estimation).
2. Time series analysis and associated concepts such as stationarity; ARMA models are introduced alongside applications to predictions/forecasting and causal inference in a dynamic context.
3. Limited dependent variable models (e.g., logit and probit models), which includes an introduction to maximum likelihood estimation.

Theoretical input will be complemented with real-world applications in the statistical software R.

The course provides an introduction to analyzing economic data using econometric methods that go beyond the multiple regression model discussed in Econometrics I. After completing the course, students are able to understand and evaluate empirical studies that use the methods outlined in "Contents." In addition, students are able to perform their own statistical analyses which make use of these methods.

Attendance is mandatory for this course, two absences will be tolerated.

Course materials will be made available to participants in the form of slides and computer code. The concepts we discuss theoretically are illustrated empirically using the statistical software R.

To gain experience in working with empirical data and to illustrate real-world applications, students will work in groups on homeworks and on a small empirical project. Solutions must be handed in as written reports.

1. Exam
2. Homework
3. Project report

To pass the course, a positive score on each of these components is necessary (i.e., at least 50 percent of the respective total points).

Grading scheme:

• Excellent (1): [89, 100] points
• Good (2): [78, 89) points
• Satisfactory (3): [60, 78) points
• Sufficient (4): [50, 60) points
• Fail (5): [0, 50) points

• Automatic deregistration from the course in the event of an unexcused no show in the first lecture (participants will be added from the waiting list if applicable).
• Non-assessment in the event of unexcused no shows if no partial performance was provided.
• Negative assessment for unexcused no show if at least one partial performance has already been provided (e.g., case study).

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Successful completion of Econometrics I.