Description

Book 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.

Table of Contents
1. Introduction; Part I. Behavioral Models: 2. Properties; 3. Logit; 4. GEV; 5. Probit; 6. Mixed logit; 7. Variations on a theme; Part II. Estimation: 8. Numerical maximization; 9. Drawing from densities; 10. Simulation-assisted estimation; 11. Individual-level parameters; 12. Bayesian procedures; 13. Endogeneity; 14. EM algorithms.

Discrete Choice Methods with Simulation

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    A Paperback by Kenneth E. Train

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      View other formats and editions of Discrete Choice Methods with Simulation by Kenneth E. Train

      Publisher: Cambridge University Press
      Publication Date: 6/30/2009 12:00:00 AM
      ISBN13: 9780521747387, 978-0521747387
      ISBN10: 0521747384

      Description

      Book 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.

      Table of Contents
      1. Introduction; Part I. Behavioral Models: 2. Properties; 3. Logit; 4. GEV; 5. Probit; 6. Mixed logit; 7. Variations on a theme; Part II. Estimation: 8. Numerical maximization; 9. Drawing from densities; 10. Simulation-assisted estimation; 11. Individual-level parameters; 12. Bayesian procedures; 13. Endogeneity; 14. EM algorithms.

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