In case of limited activity on campus, this course will be conducted in synchronous hybrid mode. To this end, the participants will be split into two equal groups A and B. In one week, group A will be physically present at the lecture while group B will be able to watch the course live online (the course will be streamed). The roles of A and B alternate on a weekly basis, meaning that each student can attend the lecture on campus biweekly.
Under special circumstances (travel restrictions, health issues) it is also possible for students to participate in the course purely remotely.
More detailed issues on who is in what group will be communicated after September 15 and before the start of the term via learn or email.
Under the synchronous hybrid mode, there will be in-class teaching together with synchronous streaming. For the remotely-attending group there will be an opportunity to ask questions during the lecture through MS Teams.
Moreover, the weekly online tutorials via MS Teams will continue to take place. While general questions can be asked, the main focus is on queries about the practical implementation of case studies in EViews and R (see plan A).
Students are expected to be active in the class (or on online Q&A sessions). Moreover, students are expected to actively contribute to solving the case studies in their groups of 3 students.
Attendance is still mandatory both online and on campus and can only be suspended or converted to pure online attendance for a valid reason.
The assessment will be the same as in plan A: It is based on two written partial exams (worth 30 points each), which need to be solved individually, and four case studies (worth 10 points each), which need to be solved in teams of three. The grading scale is also the same as under plan A:
The exams are planned to take place on campus. Depending on the situation and travel restrictions, we will replace the exams on campus with synchronous online exams.
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.
For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).
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 addition, weekly online tutorials via MS Teams taught by a student tutor will take place. While general questions can be asked, the main focus is on queries about the practical implementation of case studies in EViews and R.
4 case studies (in groups), 10 points each
2 written exams (individually), 30 points each
1: 90 – 100
2: 80 – 89,99
3: 70 – 79,99
4: 60 – 69,99
5: 00 – 59,99
Class attendance is compulsory.