Applied Choice Analysis, Second Edition, a major revision with almost totally new material has being published on June 11, 2015 in the UK and Europe. The book authors are David Hensher (BRTCoE member), from the University of Sydney, John M. Rose, from The University of South Australia, and William H. Greene, from New York University.
Find the book at Cambridge Press Website
Book Description:
The second edition of this popular book brings students fully up to date with the latest methods and techniques in choice analysis. Comprehensive yet accessible, it offers a unique introduction to anyone interested in understanding how to model and forecast the range of choices made by individuals and groups. In addition to a complete rewrite of several chapters, new topics covered include ordered choice, scaled MNL, generalized mixed logit, latent class models, group decision making, heuristics and attribute processing strategies, expected utility theory, and prospect theoretic applications. Many additional case studies are used to illustrate the applications of choice analysis with extensive command syntax provided for all Nlogit applications and datasets available online. With its unique blend of theory, estimation, and application, this book has broad appeal to all those interested in choice modeling methods and will be a valuable resource for students as well as researchers, professionals, and consultants.
- A comprehensive and clear introduction to the theory and practice of choice analysis; assumes little background knowledge and offers an entry point for any individual interested in understanding how to model and forecast the range of choices made by individuals and groups
- The fully updated and revised second edition includes new developments over the last decade, such as attribute processing, perceptual conditioning, and risk attitude
- Introduces readers to the full range of discrete choice modelling methods, including labelled and unlabelled alternatives as well as ordered and unordered alternatives; uses many case studies with NLOGIT command syntax to illustrate the application of one or more state-of-the-art choice models
