2130 Empirical Data Analysis
Hooman Habibnia, MSc., Jakob Möller, MSc (WU)
Contact details
Weekly hours
Language of instruction
09/15/22 to 09/18/22
Registration via LPIS
Notes to the course
Day Date Time Room
Thursday 10/06/22 10:00 AM - 01:00 PM TC.3.11
Thursday 10/13/22 10:00 AM - 01:00 PM TC.3.11
Thursday 10/20/22 10:30 AM - 01:00 PM D3.0.218
Thursday 10/27/22 10:00 AM - 01:00 PM TC.3.11
Thursday 11/03/22 10:00 AM - 01:00 PM TC.3.11
Thursday 11/10/22 10:00 AM - 01:00 PM TC.3.11
Thursday 11/17/22 10:00 AM - 01:00 PM TC.3.11
Thursday 11/24/22 10:00 AM - 01:00 PM TC.3.11
Thursday 12/01/22 03:00 PM - 05:00 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.)

Learning outcomes

On successful completion of the course, you should:

  • understand the concept of evidence-based decision-making;
  • be able to choose the right method of statistical data analysis to answer a research question;
  • have a good understanding of the discussed methods as well as their limitations;
  • understand the difference between causality and correlation;
  • be able to present and discuss findings from your research; 
  • perform simple analysis using statistical software.

Attendance requirements

Full attendance is expected for all lectures. If you cannot attend a lecture due to exceptional/unforeseen circumstances, please contact the lecturer. If you show symptoms of COVID-19 or are affected by quarantine, do NOT participate in in-person lectures. Please contact your lecturer by email, and we will deal with the absence on a case by case basis. If mode of lectures is affected by a change in the COVID-19 situation, we will announce any changes in due course in class and by email.

Teaching/learning method(s)

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.


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.

Last edited: 2022-08-03