DRL035 Stated Choice Analysis (Spring 2025)

This practical-based course provides an in-depth introduction to stated choice survey design and modelling. It focuses on the definition, design, and deployment of rigorous and robust techniques, based on sound microeconomic theory. Investigating agents’ preferences with respect to hypothetical scenarios allows predicting the behavioural change that adopting any intervention policy could produce.​

One can use Stated Preference and Discrete Choice Models whenever examining individual/group choices in any given hypothetical context. These techniques are extensively used in fields as diverse as transport, health, marketing, and environmental economics.

The course intertwines lectures, computer tutorials (using the R software), and case studies. It covers all the practical steps required for successful choice modelling analyses, from inception via survey design and data collection to modelling, analysis, and interpretation.

​Topics

• The theoretical and conceptual platform covers topics such as random utility theory, homo economicus, experimental design, econometrics modeling, economic value decomposition, and willingness to pay.Stated choice data acquisition. Setting research objectives. Conducting supporting qualitative studies. Developing questionnaires. Defining sampling strategies. Performing data collection.
• Stated choice data modeling. Utility specification. Maximum likelihood estimation. Multinomial logit model. Preference heterogeneity. Attribute importance. Willingness to pay measures.
• Programming with R. Basic functions and objects. Writing, adapting, and running codes. Interpreting software outcomes.

Case studies

• Transportation will be the primary focus, but case studies may also draw from non-transportation fields. Particular attention will be given to the specific topics suggested by students.

After completing the course, the student should have acquired the following:

Knowledge

• In-depth of the theoretical foundation for stated preference and discrete choice models and how they can be practically applied in a selected topic in which the student chooses to specialize.
• Learning how to apply cutting hedge methods and be familiar with survey design, modelling, analysis, and interpretation.

Skills

• Ability to apply and to practically develop data acquisition and modeling to support research at PhD level. In fact, the methods presented in the course can be used to investigate any specific topic within social science disciplines as a basis for writing scientific papers.

Competence

• Analytical, programming, presenting, and writing abilities through hands-on exercises and assignments.

• Form of assessment: Individual home exam in the form of a written report based on data provided by the teacher
• Proportion: 100%

Slides and articles will be provided during the course.

Indicative literature references for the course content

• Hensher, D. A., Rose, J. M., Greene, W. H. (2015). Applied Choice Analysis: A Primer. Cambridge University Press, Cambridge. 2nd edition. https://doi.org/10.1017/CBO9781316136232.
• Aizaki, H., Nakatani, t., Sato, K. (2014). Stated Preference Methods Using R. CRC Press. https://doi.org/10.1201/b17292
• Marcucci, E., Gatta, V., Valeri, E., Stathopoulos, A. (2013). Urban freight transport modelling: an agent-specific approach. MILANO:FrancoAngeli, ISBN: 9788856859836

Recommended readings prior to the course:

• Varian, H., Melitz, M. (2024). Intermediate Microeconomics. A Modern Approach. Norton & Company. Chapters 1-7.
• Stock, J., Watson, M. (2020). Introduction to Econometrics, Pearson. ISBN-13: 9780136879787. Ch. 1-11.