Course for Master's Programmes

Module M.WIWI-BWL.0139

Lecturer: Stephen Youngjun Park Ph.D.

---

Time and Place:

Lecture:

Mondays, 2:15 pm – 3:45 pm, 21.10.2024 - 24.11.2024, Room: MZG 7.153

Thursdays, 2:15 pm – 3:45 pm, 24.10.2024 - 28.11.2024, Room: MZG 5.111

There is no lecture on Thurstday, October 31, 2024

---

Required Examination:

Term Paper (max. 6000 words) - 6 Credits

---

Learning outcomes/core skills:

Discrete choice modeling deals with analyzing choice behavior of individuals (e.g., consumers) as a function of variables that describe the choice alternatives and/or the individuals. After successful attendance the students will understand the methodological principles of discrete choice modeling. Further, they will be able to estimate own discrete choice models using the statistical programming language R. (Previous knowledge in R is not required!)

---

Contents of the lecture:

• Random Utility Theory


• Collecting Choice Data


• Choice-based Conjoint




• Consumer Purchase Data


• Analyzing Choice Data




• Multinomial Logit (MNL) Models




• Finite Mixture and Mixed MNL Models


• Hierarchical Bayesian MNL Models