Discrete Choice Modeling
Dr. Ossama Elshiewy
- Master students
(MDM, UF, FRS, Wi-Inf, Wi-Päd, Steuerlehre, Wirtschafts & Sozialgeschichte, International Economics, Development Economics, Angewandte Statistik, Psychologie)
- All PhD students (Faculty of Economic Sciences or GRK 1666 "GlobalFood") interested in discrete choice modeling
- Discrete choice modeling deals with analyzing choice behavior of individuals (consumers, firms, etc.) 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!)
The course consists of two parts:
1. Attending a lecture (with integrated exercises)
2. Writing a term paper.
Important note: The lecture will be held in English, but the term paper can be written in either English or German.
- Brief introduction to R
- Random Utility Theory
- Collecting Choice Data
---- Choice-based Conjoint
---- Consumer Purchase Data
- Analyzing Choice Data
---- Multinomial Logit (MNL) Models
---- Generalized Extreme Value Models
---- Finite Mixture and Mixed MNL Models
---- Hierarchical Bayesian MNL Models
The term paper should contain a self-conducted empirical project.
Students will be provided with topics and empirical data, but are also welcome to analyze own projects.
Students are advised to use the statistical programming language R (and submit their code), but can be allowed to use different software in exceptional cases.
Train (2009). "Discrete Choice Methods with Simulation". 2nd Edition, Cambridge University Press.
Rossi, Allenby, and McCulloch (2005). "Bayesian Statistics and Marketing". Wiley.
Time and place:
Lecture (with integrated exercises)
(First date: October 24, 2018)
Time: 12:15 - 13:45 h
Place: MZG 7.153 (Blauer Turm - WiSoRZ)
Term paper (~6000 words)
Credits: 6 ECTS
Deadline for submission: March, 2019
Theoretical, methodological and empirical elaboration of a selected topic in discrete choice modeling.
- Probability theory and distributions
- Hypothesis testing
- (Logistic) Regression analysis