105.126 Microeconometrics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VO, 3.0h, 4.0EC


  • Semester hours: 3.0
  • Credits: 4.0
  • Type: VO Lecture

Learning outcomes

After successful completion of the course, students are able to

  • explain the maximum likelihood method and the asymptotic properties of ML estimates,
  • explain models for micro data and to chose suitable models for given data and problem settings,
  • compute (ML) estimates and to analyse and to interpret the results.

Subject of course

Principles of maximum likelihood estimation and asymptotic theory. Qualitative Response Models (Logit, Probit, Multinomial-, Conditional- und Nested-Logit), Sample Selection (Tobit Models), Duration and Survival Analysis, Count Data Models. 

Teaching methods

lecture on blackbord

Mode of examination


Additional information



Course dates

Tue11:00 - 13:0003.03.2020 - 10.03.2020Sem.R. DB gelb 04 Mikroökonometrie
Wed14:00 - 15:0004.03.2020 - 11.03.2020Sem.R. DB gelb 04 Mikroökonometrie
Microeconometrics - Single appointments
Tue03.03.202011:00 - 13:00Sem.R. DB gelb 04 Mikroökonometrie
Wed04.03.202014:00 - 15:00Sem.R. DB gelb 04 Mikroökonometrie
Tue10.03.202011:00 - 13:00Sem.R. DB gelb 04 Mikroökonometrie
Wed11.03.202014:00 - 15:00Sem.R. DB gelb 04 Mikroökonometrie

Examination modalities

single exam

Course registration

Not necessary



  • Amemiya , Advanced Econometrics, 1985.
  • Cameron and Trivedi, Microeconometrics, 2005.
  • Greene, Econometric Analysis, 2005. 
  • Kalbfleisch & Prentice, The Statistical Analysis of Failure Time Data, 2002.
  • Kleiber & Zeileis, Applied Econometrics in R, 2008.
  • Maddala, Limited Dependent and Qualitative Variables, 1983. 
  • Verbeek, A Guide to Modern Econometrics, 2012.

Previous knowledge

Basic knowledge of linear algebra, proabability theory and statistics as well as basic econometrics (linear regression models).

Accompanying courses