Discrete Choice Modelling

Spring 2024, Half-time, Norrköping

Semester Spring 2024
Place of study Norrköping
Pace of study Half-time
Level Second cycle
Teaching form On-Campus
Education Time Day-time
Education Language English
Course offering id LIU-42103
Period 202404 - 202413
Number of Places 2

Specific requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subject areas: social- and natural sciences, engineering, statistics, or mathematics
  • 15 ECTS credits in statistics, computer science, mathematics, or equivalent at advanced level
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Selection

Tuition fees

SEK 17600 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Richard Öhrvall, course director

Madelene Töpfer, course administrator

Jonas Johansson, study adviser

Course syllabus

Discrete Choice Modelling

Spring 2025, Half-time, Norrköping

Semester Spring 2025
Place of study Norrköping
Pace of study Half-time
Level Second cycle
Teaching form On-Campus
Education Time Day-time
Education Language English
Course offering id LIU-42103
Period 202504 - 202513
Number of Places 2

Specific requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subject areas: social- and natural sciences, engineering, statistics, or mathematics
  • 15 ECTS credits in statistics, computer science, mathematics, or equivalent at advanced level
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish

Selection

Tuition fees

SEK 17600 - NB: Applies only to students from outside the EU, EEA and Switzerland.

If you have questions about the course, contact us

Richard Öhrvall, course director

Madelene Töpfer, course administrator

Jonas Johansson, study adviser

Course syllabus

This course enables students to perform their own empirical research using discrete choice methods. Students learn how to create discrete choice datasets, estimate discrete choice models, including binomial, multinomial, and conditional logistic regression, and interpret model output. The focus will be on the practical aspects of modeling. During intensive computer labs, hands on experience will be provided using real data drawn from examples in the areas of consumer choice, migration, and labor market mobility. More advanced models for handling panel data and unobservable heterogeneity, as well as identification of latent groups will be examined and deployed. Applications to counterfactual and agent-based simulation will also be explored during lab sessions.