BibTeX
@MISC{Train02discretechoice,
author = {Kenneth E. Train},
title = {Discrete Choice Methods with Simulation},
year = {2002}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
This book describes the new generation of discrete choice meth-ods, 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 (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, the method of simulated moments, and the method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as antithetics 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 topics, which have risen in the past 20 years. The procedures are applicable in many fields, includ-ing energy, transportation, environmental studies, health, labor, and marketing.
Keyphrases
discrete choice method new generation simulated score environmental study many field extreme value discrete choice meth-ods variance reduction technique metropolis hastings algorithm halton draw maximum simulated likelihood many advance bayesian procedure includ-ing energy statistical method recent advance simulated moment mixed logit simulation-assisted estimation procedure cross-nested logits variant gibbs sampling major model
