Event
Microeconometrics [SS242500032]
Lecturers
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
- Brick Microeconometrics | Industrial Engineering and Management (B.Sc.)
- Brick Microeconometrics | Economics Engineering (B.Sc.)
- Brick Microeconometrics | Digital Economics (B.Sc.)
- Brick Microeconometrics | Information Systems (M.Sc.)
- Brick Microeconometrics | Information Systems (B.Sc.)
- Brick Microeconometrics | Information Engineering and Management (B.Sc.)
Literature
Winkelmann, R., Boes, S. (2006): Analysis of Microdata. Springer.
Appointments
- 16.04.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 23.04.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 30.04.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 07.05.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 14.05.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 28.05.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 04.06.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 11.06.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 18.06.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 25.06.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 02.07.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 09.07.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 16.07.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
- 23.07.2024 11:30 - 13:00 - Room: 30.96 Seminarraum 104 (1. OG)
Note
Microeconometrics is concerned with modeling data from an individual (`micro') unit like a person, household or firm. The response variables of interest are often discrete. For example, a person's type of employment may be coded as a binary variable (e.g. working in IT sector versus not working in IT sector), and a person's choice of transportation mode can be cast as a multinomial variable (e.g. bike, train, car, or other). These examples differ from the basic econometric setting of a continuous response variable, and require nonlinear regression modeling.
The course first introduces maximum likelihood estimation which is particularly useful in microeconometrics. We then discuss econometric models for various types of response variables (binary, ordered, multinomial, censored), as well as methods for estimation and model evaluation. Throughout the course, implementation via R software plays an important role.
Prerequisites: Course participants are expected to have a good working knowledge of the linear regression model (e.g. by having attended the course `Volkswirtschaftslehre III: Einführung in die Ökonometrie', or attending it in the same semester as `Microeconometrics').