Applied Choice Modelling using Apollo (3-Day)

Choice modelling is one the core methods of the applied economic sciences, including environmental, health, and transport economics.

This 3-day masterclass provides participants with an introduction to applied choice modelling using the Apollo software. Apollo is open-source software for estimating choice models available in the R computing environment.


Level 2 - runs over 3 days

Stephane Hess is Professor of Choice Modelling and Director of the Choice Modelling Centre at The University of Leeds (UK). Stephane is a globally recognised leader in choice modelling with interests in behavioural models, travel behaviour, health choices, and decision making. His specific academic contributions include developing advanced choice models and empirical contributions across many fields. He is the editor of the Journal of Choice Modelling (the leading international, interdisciplinary journal in choice modelling), the chair of the International Choice Modelling Conference, and co-developer of Apollo (with David Palma, Leeds, UK), a widely used (open-source) estimation software for choice models.

About this course: 

This is a lab-based masterclass making extensive use of the Apollo software. The masterclass will introduce participants to the software and to the family of choice models estimable within Apollo. The focus will be “hands-on” exercises and estimating choice models using Apollo with data provided by the instructor. The masterclass will place particular emphasis on model estimation and interpretation. The exercises will span a range of application areas including environmental, health, and transport economics.


The target audience is primarily an academic one including academic faculty, PhD students, and applied researchers from government agencies and departments.

Course syllabus: 

Topics scheduled are:

Day 1

  • Introduction to choice modelling and the Apollo software;
  • multinomial logit estimation;
  • specification testing;
  • analysis and interpretation of results;
  • computer exercises with estimation software (Apollo).


Day 2

  • Models with flexible error structures;
  • nested logit;
  • mixed logit;
  • latent class;
  • computer exercises with estimation software (Apollo).


Day 3

  • Alternatives to random utility models including random regret;
  • hybrid choice models;
  • emerging data sources for choice models;
  • computer exercises with estimation software (Apollo).


Course format: 

This is a lab-based masterclass.

Participants should bring their own laptops.

Participants are encouraged to install the R software on their laptops before starting the masterclass.


Recommended Background: 

Participants should have some familiarity with linear and logistic regression. There is no requirement for previous experience using R.

Recommended Texts: 

Hess, Stephane and David Palma (2019), “Apollo: A Flexible, Powerful and Customisable Freeware Package for Choice Model Estimation and Application, Journal of Choice Modelling, 32 (September).


Hess, Stephane and David Palma (2019), Apollo Version 0.0.8: User Manual.