Syllabus

Title
0549 Econometrics I
Instructors
Eva Flonner, MSc (WU)
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/14/22 to 10/03/22
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Thursday 10/06/22 01:00 PM - 03:00 PM TC.3.06
Thursday 10/13/22 01:00 PM - 03:00 PM TC.3.06
Thursday 10/20/22 01:00 PM - 03:00 PM TC.3.06
Thursday 10/27/22 01:00 PM - 03:00 PM TC.3.06
Thursday 11/03/22 01:00 PM - 03:00 PM TC.3.06
Thursday 11/10/22 01:00 PM - 03:00 PM TC.3.06
Thursday 11/17/22 01:00 PM - 03:00 PM TC.3.06
Friday 11/18/22 02:00 PM - 04:00 PM TC.0.03 WIENER STÄDTISCHE
Thursday 11/24/22 01:00 PM - 03:00 PM TC.3.06
Thursday 12/01/22 01:00 PM - 03:00 PM TC.3.06
Thursday 12/15/22 01:00 PM - 03:00 PM TC.3.06
Wednesday 12/21/22 10:30 AM - 12:30 PM TC.0.02 Red Bull

Contents

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.

Learning outcomes

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.


Attendance requirements

For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

Teaching/learning method(s)

Content is presented using the whiteboard and presentation slides. Moreover, the methods are illustrated via case studies using R. To ensure the in-depth applicability of the material presented, four extensive case studies have to be worked out in groups using R; 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 (presumably online) tutorials 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.

Assessment

In addition to the case studies there will be two written exams.

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

Prerequisites for participation and waiting lists

The seats will be allocated in the following order:

  1. Present in the first lecture AND LV-fixed place
  2. Justified excuse for the first lecture handed in in advance AND LV-fixed place
  3. Present in the first lecture AND waiting list (order of waiting list)
  4. Justified excuse for the first lecture handed in in advance AND waiting list (order of waiting list)
  5. Present in the first lecture AND eligible for registration (but not yet registered)
  6. Not present in the first lecture AND LV-fixed place
  7. Not present in the first lecture AND waiting list (order of waiting list)

Readings

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Recommended previous knowledge and skills

Mathematics, Statistics

Availability of lecturer(s)

eva.flonner@wu.ac.at

Last edited: 2022-04-20



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