Abstract
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.
Keywords
Discrete choiceLogitProbitComputer scienceEconometricsGibbs samplingBayesian probabilityNested logitMixed logitVariance (accounting)Variance reductionVariety (cybernetics)Operations researchSampling (signal processing)StatisticsLogistic regressionMathematicsEconomicsMachine learningArtificial intelligenceMonte Carlo method
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Publication Info
- Year
- 2001
- Type
- book
- Citations
- 6184
- Access
- Closed
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Cite This
Kenneth Train
(2001).
Discrete Choice Methods with Simulation.
Cambridge University Press eBooks
.
https://doi.org/10.1017/cbo9780511805271
Identifiers
- DOI
- 10.1017/cbo9780511805271