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

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).