Handbook of Choice Modelling
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Handbook of Choice Modelling

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.
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Chapter 17: Hybrid choice models

Maya Abou-Zeid and Moshe Ben-Akiva


The hybrid choice model (HCM) is a modeling framework that attempts to bridge the gap between discrete choice models and behavioral theories by representing explicitly unobserved elements of the decision-making process, such as the influence of attitudes, perceptions and decision protocols. It integrates discrete choice models with latent (or unobserved) variable models. Latent variable models, also known as structural equation models, will be presented later in this chapter. The origins of the HCM can be traced to several researchers including work by McFadden (1986), Ben-Akiva et al. (2002a, 2002b), Morikawa et al. (2002), Walker and Ben-Akiva (2002) and Ashok et al. (2002). Many applications in various contexts have followed, including vehicle type choice (Bolduc and Alvarez-Daziano, 2010; Choo and Mokhtarian, 2004), mode choice (Johansson et al., 2006), residential location choice (Kitrinou et al., 2010; Walker and Li, 2007), and so on. The purpose of this chapter is not to review this literature but rather to focus on the advantages of incorporating latent variables in discrete choice models through the HCM. We discuss four types of advantages. The first advantage is the ability to explicitly model unobserved heterogeneity, such as the dependence of taste parameters on underlying latent variables such as attitudes. The second advantage is a gain in statistical efficiency of the parameter estimates due to the additional information provided by indicators of latent variables.

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