SoSe 26: Module offerings

• Einführung in die Ökonometrie (Introduction to Econometrics)

  0171dB2.1
  learning objectives: Students learn to quantify and verify economic behavioral equations using statistical methods and observational data. ... read more

  learning objectives:
  Students learn to quantify and verify economic behavioral equations using statistical methods and observational data. They learn to describe and apply the basic methods of regression analysis including testing parameters. Through a trained understanding of econometric models, they can also identify the effects of deviating or violating models on estimates and regression parameter tests and develop appropriate strategic solutions. By including a practical computational exercise, students learn to perform regression analyses by themselves and interpret the results appropriately. Another important objective is to use student diversity as a resource and make a conscious effort to include it in the students’ daily life.
  
  

  course content:
  Fundamental methods of econometrics, for example: The classical linear regression model, parameter estimation with the least squares method, confidence ranges and parameter tests, modeling structural breaks and season, heteroscedasticity, and autocorrelation of residuals.
  
  

  language of instruction:
  German / English
  
  workload
  180 hours (6 ECTS)
  
  duration / frequency
  one semester / irregular

  close
  • 10121201 Lecture
    Introduction to Econometrics (V) (Dieter Nautz)
    Schedule: Mo 10:00-12:00 (Class starts on: 2026-04-13)
    Location: Hs 105 Hörsaal (Garystr. 21)
    
  • 10121226 Methods Tutorial
    Introduction to Econometrics (Ü1) (Michael Tran Xuan)
    Schedule: Do 10:00-12:00 (Class starts on: 2026-04-16)
    Location: Hs 105 Hörsaal (Garystr. 21)
    
  • 10121202 Practice seminar
    Introduction to Econometrics (Ü2) (N.N.)
    Schedule: Do 10:00-12:00 (Class starts on: 2026-04-16)
    Location: Do K 006a PC Pool 1 (Garystr. 21), Do K 006b PC-Pool 2 (Garystr. 21)
    

• Sampling Methods

  0171dB2.5
  learning objectives: Students gain an introduction into the field of survey statistics and learn the basic methods of sampling theory. ... read more

  learning objectives:
  Students gain an introduction into the field of survey statistics and learn the basic methods of sampling theory. They also learn about the most important sampling techniques and how to use them. In addition, they use example cases to learn how to deal with nonreponse and how to use calibration methods. In the tutorial section, students learn how statistics software can be used to draw samples, for example, from the Campus-Files from the Federal Statistical Office. They also learn the relevant methods and are thus able to assess critically the practical implementations of sampling procedures. Moreover, they learn to explain and evaluate survey data generated through polling. The module takes into consideration gender and diversity to establish conditions that make it possible for all students to participate.
  
  

  course content:
  Population and sampling probabilities, Simple random sampling, Stratified sampling, Cluster sampling, Two-stage (multi-stage) sampling, Selection schemes with unequal probabilities, Regression estimation
  
  

  language of instruction:
  German / English
  
  workload
  180 hours (6 ECTS)
  
  duration / frequency
  one semester / irregular

  close
  • 10122001 Lecture
    Machine Learning (Philipp Bach)
    Schedule: Mi 14:00-17:00 (Class starts on: 2026-04-22)
    Location: Hs 103 Hörsaal (Garystr. 21)
    
  • 10122026 Methods Tutorial
    Machine Learning (Philipp Bach)
    Schedule: Mi 17:00-18:00 (Class starts on: 2026-04-22)
    Location: Hs 103 Hörsaal (Garystr. 21)
    

• Modules w/o courses in this semester

  1 Module
  • Statistische Modellierung (Statistical Modeling) 0171dB2.2