Econometrics, Statistics and Empirical Economics

 S415 Machine Learning in Econometrics


Lecturer Dr. Jantje Sönksen



successful participation in either

S411 Advanced Time Series Analysis or

S422 Advanced Microeconometrics

Language English
Time and place

Thursday:    14:00-18:00, OSA-Keplerstraße - HS01 (lecture)

Friday:          08:00-09:40, PC-lab (practical class)

                      10:00-11:55, Neue Aula - HS02 (lecture)


written exam
Credit points 6 ECTS
Start of lecture 18-04-2019
End of lecture presumably 07-06-2019
Limited attendance 25 (application required, see below for details)


Participation in the course requires prior application. If you are interested in taking the class, please apply by sending your current transcript of records to jantje.soenksenspam no later than April 11, 2019. Applicants will be informed on the outcome of their application by April 14, 2019. If the number of applicants exceeds the capacities of the course, preferential treatment is given to students who satisfy the prerequisites mentioned above.


This module illustrates how machine learning techniques can be used in economic research and applications. It offers a thorough analysis of various tools in statistical learning and links them to econometric analysis. The course focuses on supervised machine learning techniques, such as: decision/regression trees, (logistic) regressions, naïve Bayes, local regressions, nearest neighbors, artificial neural networks, and support vector machines. The lecture also covers hyper-parameter tuning methods and different feature selection and regularization techniques. A practical PC-Lab class is an essential part of the module.

Practical class

The lecture is accompanied by a practical class. During these sessions, students deepen their understanding by applying machine learning techniques to various problems using statistical software. All students are expected to be present during these sessions.


  • Hastie/Tibshirani/Friedman: The Elements of Statistical Learning
  • Bishop: Pattern Recognition and Machine Learning
  • selected papers