Discrete Choice Modelling, 7.5 credits (771A19)

Modellering av diskreta val, 7.5 hp

Main field of study

Computational Social Science

Level

Second cycle

Course type

Programme course

Examiner

Benjamin Jarvis

Course coordinator

Benjamin Jarvis

Director of studies or equivalent

Karl Wennberg
Course offered for Semester Weeks Language Campus VOF
F7MCD Master´s Programme in Computational Social Science 2 (Spring 2019) v201904-201908 English Norrköping o

Main field of study

Computational Social Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Master´s Programme in Computational Social Science

Entry requirements

A bachelor's degree or equivalent in the humanities, social-, cultural-, behavioural-, natural-, computer-, or engineering-sciences.
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:

  • Identify problems most suitably modeled with discrete choice methods;
  • Describe which models are suitable for specific applications using panel or cross-sectional data;
  • 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 choice models;
  • Use statistical software to estimate discrete choice models, calculate predictions, 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

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

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

ISBN: 9780803973749,0803973748

HEM1 Take Home Exam EC 2.5 credits
LAB1 Laboratory EC 2 credits
UPG1 Written report EC 3 credits

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