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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