Discrete Choice Methods with Simulation

by Kenneth E. Train

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 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 stimulated 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. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Genres: Economics, Mathematics, Unfinished, Nonfiction, Academic

400 pages, Paperback

First published January 13, 2003

Reviews

[Florent Buisson · Rating 3 out of 5]

I found the theoretical presentation of the models in Part I pretty accessible, but Part II about simulations presented equations and graphs, not a single line of code. Not useful for what I'm trying to do

[Tri · Rating 5 out of 5]

Read twice. So clear on discrete choice models, but for dynamic discrete choice models, need to find other resources. Haven't found a single book on dynamic models which can be as good as this one.