Discrete Choice Modelling, 7.5 credits (771A20)

Modellering av diskreta val, 7.5 hp

Course description

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.

Main field of study

Computational Social Science

Level

Second cycle

Course type

Single subject course

Examiner

Richard Öhrvall

Course coordinator

Richard Öhrvall

Director of studies or equivalent

Karl Wennberg

Available for exchange students

Yes

Contact

Benjamin Jarvis, course director

Karl Wennberg, director of studies

Course offered for Semester Weeks Language Campus VOF
Single subject course (Half-time, Day-time) Spring 2020 v202004-202013 English Norrköping
Single subject course (Half-time, Day-time) Spring 2020 v202004-202013 English Norrköping

Main field of study

Computational Social Science

Course level

Second cycle

Advancement level

A1X

Entry requirements

A bachelor's degree or equivalent in social science, physical science, biological science, engineering, statistics or math. Additionally required at least 15 ECTS credits in statistics, computer science, mathematics or equivalent at advanced level or higher.
English corresponding to the level of English in Swedish upper secondary education (English 6/B).

Intended learning outcomes

After completion of the course, the student should on an advanced level be able to:

  • Describe which models are suitable for specific applications;
  • Identify problems most suitably modeled with discrete choice models;
  • Develop appropriate discrete choice model specifications;
  • Create appropriate data structures for estimating discrete choice models;
  • Critically review and interpret model results of statistically complex discrete selection models,
  • Use statistical software to estimate discrete choice models and interpret and analyze results.

Course content

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.

 

Teaching and working methods

The teaching consists of lectures, readings, computor labs and seminars. Homework and independent studies are a necessary complement to the course.

Language of instruction: English

Examination

The course is examined through written assignments, active participation on seminars, computer labs and a final written individual assignment.

Detailed information about the examination can be found in the course’s study guide. 

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Grades

ECTS, EC

Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Department

Institutionen för ekonomisk och industriell utveckling

Books

Long, J. Scott, (1997) Regression models for categorical and limited dependent variables

ISBN: 9780803973749,0803973748

Train, Kenneth, (2009) Discrete choice methods with simulation 2nd ed. Cambridge : New York, NY : Cambridge University Press, 2009.

ISBN: 0511592493,9780511592492,9780521766555,9780521747387

Additional literature

Books

Ben-Akiva, Moshe E., Lerman, Steven R., (1985) Discrete choice analysis. theory and application to travel demand Cambridge, Mass. : MIT Press, cop. 1985

ISBN: 0262022176

HEM2 Take Home Exam EC 3.5 credits
UPG2 Written report EC 3 credits
LAB2 Laboratory EC 1 credits

This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page. There are no files available for this course.