Hensher D.A., Rose J.M., Greene W.H. Applied Choice Analysis

Cambridge: Cambridge University Press, 2015. — 1218 p.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.Getting started
In the beginning
Choosing
Choice and utility
Families of discrete choice models
Estimating discrete choice models
Experimental design and choice experiments
Statistical inference
Other matters that analysts often inquire about
Software and data
Nlogit for applied choice analysis
Data set up for Nlogit
The suite of choice models
Getting started modeling: the workhorse – multinomial logit
Handling unlabeled discrete choice data
Getting more from your model
Nested logit estimation
Mixed logit estimation
Latent class models
Binary choice models
Ordered choices
Combining sources of data
Advanced topics
Frontiers of choice analysis
Attribute processing, heuristics, and preference construction
Group decision making
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