The unit consists of four parts:

Online:

I)    an organizational session to discuss the seminar arrangement

On-site:    

II)       an introductory lecture on experimental methods and designs in which topics are assigned,

III)     a block course with own experiments combined with a lecture on data analysis of experiments,

IV)     and a block course with presentations of the own experimental results combined with a lecture on how to write a paper on experimental results.

Course assessments: conducting an own experiment (part II), presenting the results (part III), and writing a seminar paper until August 31th.


The seminar "Contemporary Issues in Behavioural Economics" introduces students to the scientific literature in selected fields of Behavioural Economics. We will read some of the key contributions to these fields.

The application procedure and further information will be provided soon


Econometrics helps to estimate the relationships between economic events. We will discuss which methods we can use to estimate these relationships and what are the underlying assumptions of these methods

The aim of this course is to introduce students to empirical methods in economics. The goals of  the course are to explain (1) which methods one can use to estimate the relationship between economic events, (2) assumptions of these methods, and (3) how to use these methods.

During the lectures you will learn: 

  • What are the key concepts in probability and statistics?
  • Why and when to use maximum likelihood methods?
  • What are the assumptions of the linear regression?
  • How to provide hypothesis testing?
  • When to use the non-linear regressions?
  • What are the assumptions of multiple regressions model?
  • How to provide joint-hypothesis testing?
  • Why and when to use Bootstrapping?
  • Why and when to use Machine Learning?
  • Why and when to use Bayesian methods?

During the seminars you will learn:

  • How to use statistical package R
  • How to work with data sets
  • How to provide a statistical analysis
  • How to interpret and present your results

Prerequisites: Probability Theory

Preferred previous courses: Statistics, Econometrics, Programming in R

Course Structure

  1. Review of Probability and Statistics
  2. Review of Maximum likelihood and Method of Moments
  3. Review of Linear Regression
  4. Review of Models with Multiple Regressors
  5. Bootstrap
  6. Machine Learning
  7. Bayesian methods