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. If time allows, we also look into instrumental variables regression as a means to deal with endogeneity.

After this course, students are able to critically discuss empirical studies using the econometric methods covered in this course. Moreover, students can independently conduct their own analyses of economic data.

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, four extensive case studies have to be worked out; the solutions must be handed in in form of written reports.

4 case studies (in groups), 8 points each

2 written exams (individually), 24 points each

Grading scheme:

1: 72 – ∞

2: 64 – 71.99

3: 56 – 63.99

4: 48 – 55.99

5: 00 – 47.99

Class attendance is compulsory.

Successful completion of Econometrics I.