Discrete Choice Methods with Simulation
Kenneth Train
Published by Cambridge University Press
First edition, 2003
Second edition, 2009

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Front Material and Quotations on Jacket
Chapter 1. Introduction
Chapter 2. Properties of Discrete Choice Models
Chapter 3. Logit
Chapter 4. GEV
Chapter 5. Probit
Chapter 6. Mixed Logit
Chapter 7. Variations on a Theme
Chapter 8. Numerical Maximization
Chapter 9. Drawing from Densities
Chapter 10. Simulation-Assisted Estimation
Chapter 11. Individual-Level Parameters
Chapter 12. Bayesian Procedures
Chapter 13. Endogeneity
Chapter 14. EM Algorithms
Index
Bibliography

http://elsa.berkeley.edu/books/choice2.html

others
http://www.pinggu.org/bbs/z_thre ... 50f1be56e6774a86d71