Applied Choice Analysis
Cambridge University Press
Edition: 2, 4/30/2015
EAN 9781107465923, ISBN10: 1107465923
Paperback, 1196 pages, 24.6 x 17.6 x 5.1 cm
Language: English
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.
Preface
Part I. Getting Started
1. In the beginning
2. Choosing
3. Choice and utility
4. Families of discrete choice models
5. Estimating discrete choice models
6. Experimental design and choice experiments
7. Statistical inference
8. Other matters that analysts often inquire about
Part II. Software and Data
9. Nlogit for applied choice analysis
10. Data set up for Nlogit
Part III. The Suite of Choice Models
11. Getting started modeling
the workhorse - multinominal logit
12. Handling unlabeled discrete choice data
13. Getting more from your model
14. Nested logit estimation
15. Mixed logit estimation
16. Latent class models
17. Binary choice models
18. Ordered choices
19. Combining sources of data
Part IV. Advanced Topics
20. Frontiers of choice analysis
21. Attribute processing, heuristics, and preference construction
22. Group decision making
Select glossary
References
Index.