This course covers econometrics methods beyond linear models. We discuss time series data with a focus on stationarity and non-stationarity. ARMA and ARIMA models are introduced and their application to estimation and forecasting is being illustrated. In the second part of the course, we cover limited dependent variable models (logit and probit models) as well as count data regression.

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 the Contents. In addition, students are able to perform independently their own statistical analyzes which make use of these methods.

For this course participation is obligatory. Students are allowed to miss a maximum of 20% .

In-class, content is presented using the whiteboard and presentation slides. Moreover, the methods are illustrated via case studies using EViews and R. To ensure the in-depth applicability of the material presented, the students will work in groups on three extensive case studies and on a project.

The solutions must be handed in in form of written reports. The project will be presented in form of an oral presentation during the last two lectures.

The use of AI-based software for task solving and text generation (e.g. ChatGPT) is not permitted.

Grading scheme:

1: 72 – ∞
2: 64 – 71.99
3: 56 – 63.99
4: 48 – 55.99
5: 00 – 47.99

• Automatic deregistration from the course in the event of an unexcused no show in the first or second unit (if necessary, waiting list!).
• Non-assessment in the event of two unexcused no shows if no partial performance was provided.
• Negative assessment for two unexcused no shows if at least one partial performance has already been provided (e.g. first case study).

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Successful completion of Econometrics I.