For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).
Syllabus
Registration via LPIS
Day | Date | Time | Room |
---|---|---|---|
Tuesday | 10/06/20 | 12:00 PM - 02:00 PM | TC.4.13 |
Tuesday | 10/13/20 | 12:00 PM - 02:00 PM | TC.4.13 |
Tuesday | 10/20/20 | 12:00 PM - 02:00 PM | TC.4.13 |
Tuesday | 10/27/20 | 12:00 PM - 02:00 PM | TC.4.13 |
Tuesday | 11/03/20 | 12:00 PM - 02:00 PM | Online-Einheit |
Tuesday | 11/10/20 | 12:00 PM - 02:30 PM | Online-Einheit |
Friday | 11/13/20 | 11:00 AM - 01:00 PM | Online-Einheit |
Tuesday | 11/17/20 | 12:00 PM - 02:30 PM | Online-Einheit |
Tuesday | 11/24/20 | 12:00 PM - 02:30 PM | Online-Einheit |
Tuesday | 12/01/20 | 12:00 PM - 02:30 PM | Online-Einheit |
Tuesday | 12/15/20 | 12:00 PM - 02:00 PM | Online-Einheit |
Friday | 12/18/20 | 11:00 AM - 01:00 PM | Online-Einheit |
1: 90 – 100
2: 80 – 89,99
3: 70 – 79,99
4: 60 – 69,99
The course covers basic concepts of econometrics. After an introduction into the characteristics of economic data, concepts such as causality and correlation are discussed. The classical regression model and the assumptions underlying the model are discussed in detail. The method of OLS estimation as well as asymptotic tests are explained in detail. Other topics include model selection such as choice of functional form, misspecification, dummy variables and heteroscedasticity.
The course provides an introduction to the analysis of economic data using econometric methods based on multiple regression. After the course, students will be able to understand and discuss empirical studies using the methods covered in this course. Moreover, students will learn how to independent 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. Part of the case studies will deal with the understanding of the theory.
In order to provide students with the necessary support, weekly tutorials on MS Teams will be offered by a tutor. While general questions can be asked, the main focus of the tutorials will be on the practical implementation of the case studies with EViews and R.
4 case studies (in groups), 10 points each
2 written exams (individually), 30 points each
Grading scheme:
1: 90 – 100
2: 80 – 89,99
3: 70 – 79,99
4: 60 – 69,99
5: 00 – 59,99
Class attendance is compulsory.
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