ZEW Summer Courses 2024 in Cooperation with the CDSE

LECTURER
Erik Snowberg, University of Utah

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits:

Grades will be determined based on a presentation (40%), referee report (40%), and class participation (20%).

SCHEDULE
Date of the Course: August 26 – 30, 2024

PREREQUISITES
All first year CDSE or equivalent courses. Standard graduate econometrics.

COURSE CONTENT
This course provides an overview on the following topics of experimental economics:

• Measurement Theory, Replication
• Preference Elicitations, Belief Elicitations
• Communication, Survey Methods, Lab in the Field,
• Repeated Games, Social Networks,
• Choice Process Data, Experimetrics

COMPETENCES ACQUIRED
Design a “good” experiment with clearly spelled out goals and methods. Students should still expect to need input on their experiments from mentors, but the class should also help facilitate familiarity with advice given by such mentors.

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)
The lecturer will provide 10 papers covering the topics above. These are currently being drafted as a handbook of experimental economics methods, and should be available about a month before the course.

LECTURER
Hans-Martin von Gaudecker, University of Bonn

Tim Mensinger, University of Bonn

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits:

Coding assignment and seminar paper based on it.

SCHEDULE
Date of the Course: July 22 – 26, 2024

PREREQUISITES
All first year CDSE or equivalent courses. Standard graduate econometrics.

COURSE CONTENT
This course will be structured in the following way:

• Introduction to shell and Git
• Basic Python I
• Basic Python II
• Intermediate Git
• Data Management
• Project Templates
• Software engineering
• Finding your way around remaining materials

COMPETENCES ACQUIRED
Upon completion of the course, students will have:

1. Making research projects reproducible from day
2. Solving reasonably complex economic problems using a computer
3. Writing correct code in an efficient manner employing modern software engineering techniques
4. Learning about the structure of economic data and how to handle it efficiently

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)
The course uses the flipped classroom concept:

• Before the class, students will watch screencasts, work on quizzes, or solve short problems
•  During the class, students will solve problems in a group

LITERATURE

• Effective Programming Practices for Economists: Topics
• Coding for Economists
• Research Software Engineering with Python
• The Turing Way: A handbook for reproducible, ethical and collaborative research

LECTURER
Michael Lechner, Universität St. Gallen

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS credits:

Class participation (30%)

Group project presentations (about 2 weeks after the course)

SCHEDULE
Date of the Course: August 20 – 23, 2024

PREREQUISITES
All first year CDSE or equivalent courses. Standard graduate econometrics.

COURSE CONTENT
The course has three parts. In the first part, we discuss the use of machine and statistical learning methods for predicting outcomes. In the second part, we focus on the most popular causal research designs used in econometrics, like selection-on-observables, IV, regression-discontinuity and difference-in-difference. The third part concerns causal machine learning, i.e. how to combine the prediction methods of the machine learning literature with the causal research designs to obtain reliable causal inference in empirical studies.

COMPETENCES ACQUIRED
Students will obtain a basic knowledge of several popular machine/statistical learning methods, of the most important research designs, and how to combine both to obtain reliable and robust causal inference. They will be able to use these methods to conduct own empirical studies with causal machine learning methods.

FURTHER INFORMATION LITERATURE AND RECOMMENDED TEXTBOOKS)
Literature will be announced prior to the course.

LECTURER
Nicolas Schutz, Universität Mannheim

Course Type: elective

Credits: 5 ECTS

Grading and assignment of ECTS-credits:

Paper presentation (50%), referee report (50%)

SCHEDULE
Date of the Course: Lectures: July 8 – 12, 2024

PREREQUISITES
All first year CDSE or equivalent courses.

COURSE CONTENT
This course will cover simple microeconomic models that can be used to derive testable predictions, motivate empirical specifications, and explain empirical findings. We will cover a number of recent papers in industrial organization and international trade that have relied on combining applied-theoretical modelling with reduced-form empirical evidence. A reading list will be communicated at a later stage.

COMPETENCES ACQUIRED
Students are able to build simple micro models and use them as building blocks for empirical work.

FURTHER INFORMATION (LITERATURE AND RECOMMENDED TEXTBOOKS)
Literature will be announced prior to the course.