• Please contact Martin Kies.

 

 

This Master level course explores methods to model and empirically estimate the behavior of consumers and the strategic interactions of firms. The methods are useful in a wide field of applications. Here are some examples:

 

• Marketing and Firm Strategy, for determining optimal prices and product features and predicting competitor’s reactions
• Competition policy, e.g. predicting the effects of a merger on market outcomes and total welfare
• Planning of public projects, e.g. predicting the demand of new public transportation route
• Environmental policy, determining the effects of subsidies or taxes on consumer behavior
• International trade, predicting the effects of changes in tariffs and exchange rates on imports and exports in specific markets

A large part of the course consists of analyzing data, models, and methods using the freely available statistical programming language R.

You don't need previous knowledge of R, but it might be helpful. To help you to understand the concepts and their implementation in R better, we use several interactive R problem sets. They allow you to directly check your solution on your own computer and provide immediate feedback on whether your code is correct and provide hints. While solving these sort of problem sets should be fun in itself, we also add some extrinsic incentives: active participation in solving the problem sets counts up to 10% of the final grade.

If the class is not too large, there will be an oral exam. You can freely choose whether you want to take the exam in German or English. (If the class is too large, there will be a written exam instead, in which you are free to answer in German or English)

 

• Lectures & Exercises:
  • Wednesday: 16:15 - 17:45, He18 / 120
  • Thursday: 14:15 - 15:45,  He18 / 220

• Office Hour: After lectures or by appointment: Prof. Dr. Sebastian Kranz

• Teaching Assistant: Martin Kies

Please sign up on the Moodle page of the course:

https://moodle.uni-ulm.de/course/view.php?id=4619

for course material and announcements.