Discrete Choice Methods with Simulation

Kenneth E. Train

doi:10.1017/CBO9780511805271

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.

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Contents

Frontmatter
pp i-vi
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Contents
pp vii-x
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1 - Introduction
pp 1-8
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Part I - Behavioral Models
pp 9-10
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2 - Properties of Discrete Choice Models
pp 11-33
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3 - Logit
pp 34-75
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4 - GEV
pp 76-96
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5 - Probit
pp 97-133
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6 - Mixed Logit
pp 134-150
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7 - Variations on a Theme
pp 151-182
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Part II - Estimation
pp 183-184
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8 - Numerical Maximization
pp 185-204
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9 - Drawing from Densities
pp 205-236
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10 - Simulation-Assisted Estimation
pp 237-258
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11 - Individual-Level Parameters
pp 259-281
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12 - Bayesian Procedures
pp 282-314
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13 - Endogeneity
pp 315-346
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14 - EM Algorithms
pp 347-370
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Bibliography
pp 371-384
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Index
pp 385-388
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Discrete Choice Methods with Simulation

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Contents

Frontmatter
pp i-vi

Contents
pp vii-x

1 - Introduction
pp 1-8

Part I - Behavioral Models
pp 9-10

2 - Properties of Discrete Choice Models
pp 11-33

3 - Logit
pp 34-75

4 - GEV
pp 76-96

5 - Probit
pp 97-133

6 - Mixed Logit
pp 134-150

7 - Variations on a Theme
pp 151-182

Part II - Estimation
pp 183-184

8 - Numerical Maximization
pp 185-204

9 - Drawing from Densities
pp 205-236

10 - Simulation-Assisted Estimation
pp 237-258

11 - Individual-Level Parameters
pp 259-281

12 - Bayesian Procedures
pp 282-314

13 - Endogeneity
pp 315-346

14 - EM Algorithms
pp 347-370

Bibliography
pp 371-384

Index
pp 385-388

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Discrete Choice Methods with Simulation

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