| Day | Date | Time | Room |
|---|---|---|---|
| Thursday | 03/07/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Thursday | 03/14/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Thursday | 03/21/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Thursday | 04/11/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Thursday | 04/18/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Thursday | 04/25/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Thursday | 05/02/24 | 10:30 AM - 01:30 PM | TC.3.07 |
| Tuesday | 05/07/24 | 12:00 PM - 03:00 PM | D5.1.001 |
| Tuesday | 05/07/24 | 03:00 PM - 04:00 PM | D5.1.001 |
| Thursday | 05/16/24 | 10:30 AM - 12:00 PM | Ort nach Ankündigung |
Data analysis is the basis of any evidence-based managerial decision-making. Data analysis is about recognizing patterns in data so that inferences about the real world can be made. The course teaches students about causal inference using selected methods of data creation, collection, and analysis. It draws on econometrics and statistical methods developed to estimate economic relationships, testing theoretical hypotheses and evaluating policies.
In particular, this course will provide a review of regression analysis including linear regression with multiple regressors, non-linear regression models and dummy variables. In addition the course will cover the methods of laboratory and field experiments, specific approaches to establish causal relations with observational data, such as Differences-in-Differences Regression and Regression Discontinuities. (We may also cover Instrumental Variables if there is time.)
On successful completion of the course, you should:
Full attendance is expected for all lectures. If you cannot attend a lecture due to exceptional/unforeseen circumstances, please contact the lecturer.
The data course is centered on specific problem-based examples and case studies. Typically, we will start a topic with one or more examples and discuss how to find and/or collect data to answer these questions. This is followed by an introduction of the respective analysis method. In in-class tasks and homework assignments, students are asked to try out data analysis themselves, with data provided to them.
Participation in class and/or homework presentations (15%)
Homework assignments (15%)
There will be homework assigned in nearly all lectures. Homework assignments can be done in groups and need to be submitted before the next lecture. We will discuss homework in class.
Group project (30%)
Final exam (40%)
The final exam will cover the entire course.
Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.
Resources available to students consist of lecture slides that will be provided online after class.
The statistical software used in the course will be provided by the university. It is helpful if students bring their own laptops to class for data analysis.
Relevant chapters from textbooks will be announced in the lecture slides.