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.

2021S, VO, 3.0h, 4.0EC

Properties

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

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

Oral

Additional information

The lectures will most likely take place during the usual times:

Tuesday, 9-11h

Wednesday 10-11h




Lecturers

Institute

Examination modalities

single exam

Course registration

Not necessary

Curricula

Study CodeObligationSemesterPrecon.Info
033 203 Statistics and Mathematics in Economics Mandatory elective
860 GW Optional Courses - Technical Mathematics Not specified

Literature

  • 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

Language

German