Discrete Choice Methods with Simulation

Train, Kenneth E.

ISBN 10: 0521747384 / ISBN 13: 9780521747387

Edité par Cambridge University Press, 2009

A propos de cet article

Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLYNO ACCESS CODE, NO CD, Ships with Emailed Tracking.

N° de réf. du vendeur SKU0508050

Détails bibliographiques

• Titre: Discrete Choice Methods with Simulation
• Éditeur: Cambridge University Press
• Date de parution: 2009
• Langue: anglais
• ISBN à 10 chiffres: 0521747384
• ISBN à 13 chiffres: 9780521747387
• Reliure: Paperback
• Édition: 2ème Édition
• État de l'article: Good

A propos de ce titre

Synopsis

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. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This 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.

À propos de l?auteur

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 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.