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 22: Hybrid choice models: the identification problem

Akshay Vij and Joan L. Walker


Conventional microeconomic theory has tended to regard individual consumers as rational self-interested actors engaged in a constant process of evaluating the costs and benefits associated with any decision in the marketplace as they strive to maximize their personal well-being. The random utility maximization model has been the model of choice for studies on consumer behavior over the last several decades. Random utility maximization, or discrete choice, models examine potential outcomes from among a set of mutually exclusive alternatives, and have found wide application in fields as diverse as travel demand analysis, marketing, education, labor force participation, and so on. Early applications almost exclusively used some model form belonging to the generalized extreme value (GEV) family of models, owing largely to the computational tractability offered by these models. The multinomial logit and nested logit models proved by far to be the most popular (Carrasco and Ortuzar, 2002), earning their colloquial appellation of the workhorses of discrete choice analysis. Numerous studies have since devoted attention towards improving the specification of the logit model.

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