1752 Empirical Research and Analysis II
Philipp Külpmann
Contact details
Weekly hours
Language of instruction
11/21/19 to 11/25/19
Registration via LPIS
Notes to the course
Day Date Time Room
Thursday 11/28/19 10:00 AM - 01:00 PM D4.0.022
Thursday 12/05/19 10:00 AM - 01:00 PM D4.0.022
Thursday 12/12/19 10:00 AM - 01:00 PM D4.0.022
Thursday 12/19/19 10:00 AM - 01:00 PM D4.0.022
Thursday 01/09/20 10:00 AM - 01:00 PM D4.0.022
Thursday 01/16/20 10:00 AM - 01:00 PM D4.0.022
Thursday 01/23/20 10:00 AM - 01:00 PM D4.0.022
Thursday 01/30/20 10:00 AM - 12:00 PM D4.0.022

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 cover the methods of laboratory and field experiments, specific approaches to establish causal relations with observational data, such as Instrumental Variables Regression, Differences-in-Differences Regression, Regression Discontinuities and Dynamic Causal Effects.

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 with STATA (or R);
Attendance requirements

students are required to attend 80% of the lectures

Teaching/learning method(s)

The data course is centered on specific problem-based example and case studies. Typically, we will start a topic with one or more examples and discuss how to find and/or generate 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 generation and analysis themselves, with data provided to them.


Oral presentations and participation in group discussions (20%)

Home work assignments (30%)
There will be homework assigned in nearly all lectures. Homework assignments can be done in groups and need to be submitted via email before the next lecture. Every class will start with a discussion of the homework.

Final exam (50%)
The final exam will cover the entire course.


Resources available to students consist of lecture slides that will be provided online after class.

You are encouraged to use STATA but you can use R if you prefer.
STATA is available on lab computers or you can purchase a student license. R and RStudio are free and open-source source software.

Relevant chapters from textbooks will be announced in the lecture slides.

Last edited: 2019-11-30