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.

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. 

lecture on blackbord

First meeting on March 1, 9h15 in seminar room Freihaus DB 04 gelb.

The course is planned entirely in presence. Changes are possible due to the current COVID situation.

single exam

• 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.

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