Extended Discrete Choice Models: Integrated Framework, Flexible Error Structures, and Latent Variables

Discrete choice methods model a decision-maker's choice among a set of mutually exclusive and collectively exhaustive alternatives. They are used in a variety of disciplines (transportation, economics, psychology, public policy, etc.) in order to inform policy and marketing decisions and to better understand and test hypotheses of behavior. This dissertation is concerned with the enhancement of discrete choice methods. The workhorses of discrete choice are the multinomial and nested logit models. These models rely on simplistic assumptions, and there has been much debate regarding their validity. Behavioral researchers have emphasized the importance of amorphous influences on behavior such as context, knowledge, and attitudes. Cognitive scientists have uncovered anomalies that appear to violate the microeconomic underpinnings that are the basis of discrete choice analysis. To address these criticisms, researchers have for some time been working on enhancing discrete choice models. While there have been numerous advances, typically these extensions are examined and applied in isolation. In this dissertation, we present, empirically demonstrate, and test a generalized methodological framework that integrates the extensions of discrete choice. The basic technique for integrating the methods is to start with the multinomial logit formulation, and then add extensions that relax simplifying assumptions and enrich the capabilities of the basic model. The extensions include: SpeciJyingfactor analytic (probit-like) disturbances i order to provide a flexible covariance structure, thereby relaxing the IIA condition and enabling estimation of unobserved heterogeneity through techniques such as random parameters. Combining revealed and stated preferences in order to draw on the advantages of both types of data, thereby reducing bias and improving efficiency of the parameter estimates. Incorporating latent variables in order to provide a richer explanation of behavior by explicitly representing the formation and effects of latent constructs such as attitudes and perceptions. Stipulating latent classes in order to capture latent segmentation, for example. in terms of taste parameters, choice sets, and decision protocols.