Table of Contents

Handbook of Choice Modelling

Handbook of Choice Modelling

Elgar original reference

Edited by Stephane Hess and Andrew Daly

Choice modelling is an increasingly important technique for forecasting and valuation, with applications in fields such as transportation, health and environmental economics. For this reason it has attracted attention from leading academics and practitioners and methods have advanced substantially in recent years. This Handbook, composed of contributions from senior figures in the field, summarises the essential analytical techniques and discusses the key current research issues. It will be of interest to academics, students and practitioners in a wide range of areas.

Chapter 21: Simple ways to estimate choice models for single consumers

Bart Frischknecht, Christine Eckert, Jordan Louviere and Tiago Ribeiro

Subjects: economics and finance, environmental economics, transport, environment, environmental economics, transport, urban and regional studies, transport


Choice models were first proposed by Thurstone (1927) for pairs of options. Models for multiple choice options are due to Luce (1959) and McFadden (1974). Except for laboratory choice experiments in psychology, it is rare to see discrete choice models estimated for single people. Following Chapman’s (1984) discussion of the difficulties of using rank-order expansion, there was little work on ways to measure and model single person choices in survey applications until recently. A notable exception, Finn and Louviere (1992), demonstrated that the best-worst scaling method, where a respondent makes best and worst choices from among sets of objects according to an underlying construct such as preference, importance, or concern, could provide individual-level estimates of preferences. The best-worst scaling approach was extended by Louviere et al. (2008) to allow for the estimation of choice models for single persons. The purpose of this chapter is to show that one can use simple methods to model single person choices by extending the Louviere et al. (2008) approach to estimation methods familiar to most academics and practitioners, such as ordinary least squares (OLS) regression and weighted least squares (WLS) regression. Past work modeling individuals is described in section 2. The two new methods described in section 3 yield biased estimates of the choice probabilities, but we demonstrate that one can improve these estimates rather simply.

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