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COURSE PROGRAM

Each week consists of a 3.5-hour live online lecture, plus a series of pre-recorded videos that explain key concepts/theories. Further, most weeks also contain a 1-hour live software demonstration and a series of pre-recorded software videos. The following tools are discussed and used in the course:

 

  • Apollo, for estimating choice models

  • Ngene, for generating designs for choice experiments

  • SurveyEngine, for creating questionnaires to collect choice data

 

Course participants are also given exercises to complete in their own time each week. To fully engage with the course, we strongly recommend that you reserve 5 hours each week for homework. A typical course week with live sessions and a recommended schedule for homework is shown below.

CourseProgramTable.png

WEEK 1: INTRODUCTION TO CHOICE MODELLING

  • Introduction to key concepts

  • A guide to: Planning a choice study

  • Case studies: study plan

  • Decision rules

  • The multinomial logit model

WEEK 2: CHOICE DATA COLLECTION

  • Stated and revealed preference data

  • A guide to: Data collection

  • Creating questionnaires

  • Case study: revealed preference data collection

  • Case study: stated preference data collection

WEEK 3: CHOICE MODEL SPECIFICATION AND ESTIMATION

  • A guide to: Model building

  • Formulating utility functions

  • Model estimation and outputs

  • Model comparison

  • Case study: model specification and estimation

WEEK 4: DESIGNING CHOICE EXPERIMENTS

  • A guide to: Experimental design

  • Orthogonal designs

  • Efficient designs

  • Bayesian efficient designs

  • Case study: experimental design

WEEK 5: INTERPRETATION OF OUTPUTS AND MODEL APPLICATION

  • Attribute importance

  • Willingness-to-pay and other marginal rates of substitution

  • Predictions and elasticities

  • Determining precision of derived measures of interest

  • Case study: model interpretation and application

WEEK 6: OTHER CHOICE MODELLING CONSIDERATIONS

  • Willingness-to-pay space models

  • Nested logit and other GEV models

  • Joint estimation

  • Heteroskedastic choice models

  • Case study

WEEK 7: ACCOUNTING FOR RANDOM PREFERENCE HETEROGENEITY

  • Heterogeneity: an overview

  • Latent class analysis

  • Mixed logit

  • Marginal rates of substitution with random heterogeneity

  • Case study: random preference heterogeneity

WEEK 8: ADVANCED CHOICE MODELLING AND EXPERIMENTAL DESIGN

  • Advanced experimental designs

  • Posterior analysis

  • Hybrid choice models

  • Discrete-continuous choice models

  • Q&A and feedback

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