By the end of the course, you'll have an understanding of:

• the basic concept of microeconomic theory and bounded rationality
• the link between the mathematical models and the phenomena they are able to reproduce, i.e. the pros and cons (strengths and weaknesses) of each model
• the nature of the data that can be used to estimate and forecast individual choices and the ability to choose the right data for the right phenomenon and/or problem under study.

You'll also have learnt how to:

• formulate the specification of mathematical models (from the basic models to the more advanced ones), mastering the microeconomic and mathematical conditions (such as the identification properties)
• analyse data using both standard and advanced discrete choice models
• use the software to estimate both standard and advanced DCMs
• interpret and compared the results based on statistical analysis
• provide a convincing argument concerning the usefulness of the different models for a specific problem.

You'll have the opportunity to read and discuss some papers where some advanced discrete choice models and their choice is estimated.

You'll be able to:

• identify the adequate model structure (among those discussed in the lectures) to simulate the phenomenon
• justify the theoretical reasons behind your choice, for each specific problem under study (involving individual choice)
• build (specify, estimate and evaluate) the mathematical models discussed in the lectures to simulate individual choices in a variety of contexts
• evaluate the best model (among those discussed in the lectures), using statistical tests and theoretical consideration
• forecast the demand under various policies using the models estimated (among those discussed in the lectures)
• compute the valuation measures discussed in the lectures and demand elasticity using the models estimated (among those discussed in the lectures)
• identify if a dataset is adequate for the simulation of the phenomenon and/or problems under study
• write a methodologically sound report containing a description of the phenomenon, treatment of data, and a theoretical description of the model specifications (including both standard and advanced DCMs), including discussion on the microeconomic and the identification properties, estimation and a critical discussion of the results.

Course Outline

• Introduction
  • Why it is important to study the decision process and model individual choices
  • Understanding and forecasting individual choices
  • Examples from various disciplines (transport, health, economics, marketing, urban etc).
• Theory of individual choice
  • Definition of decision maker, choice set, feasibility of alternatives and constraints
  • Concept of utility, compensatory decisions and maximisation
  • Non compensatory choice and other heuristics
  • The role of behaviour, attitudes, goals and social influence in the decision process
  • Behaviour changes and preference formation.
• Mathematical models
  • Microeconomic and mathematical derivation of the basic discrete choice models (multinomial logit model)
  • Modelling preference heterogeneity among individuals, correlation among alternatives and a variety of substitution patterns (the mixed logit model)
  • Utility specification and model estimation in practice
  • Modelling effect of bounded rationality, such as habit/inertia effect, learning effects, attitude and perceptions (the hybrid choice model)
  • How to choose the best model
  • When it is appropriate/correct to use each type of model.
• Understanding and using multiple data sources
  • Nature of data: Psychological indicators, Revealed and Stated preference data, Cross sectional, short and long Panel data
  • Modelling with multiple data sources.

Please see above

The course will be of particular interest to those working in transport companies (road, rail, air, sea, and working with colleagues in demand management, planning, marketing and so on), and those working in public administration.

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