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Discrete Choice Methods with Simulation

ISBN-10: 0521747384
ISBN-13: 9780521747387
Edition: 2nd 2009
Authors: Kenneth E. Train
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Book details

Edition: 2nd
Copyright year: 2009
Publisher: Cambridge University Press
Publication date: 6/30/2009
Binding: Paperback
Pages: 400
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 1.166
Language: English

Introduction
Motivation
Choice Probabilities and Integration
Outline of Book
A Couple of Notes
Behavioral Models
Properties of Discrete Choice Models
Overview
The Choice Set
Derivation of Choice Probabilities
Specific Models
Identification of Choice Models
Aggregation
Forecasting
Recalibration of Constants
Logit
Choice Probabilities
The Scale Parameter
Power and Limitations of Logit
Nonlinear Representative Utility
Consumer Surplus
Derivatives and Elasticities
Estimation
Goodness of Fit and Hypothesis Testing
Case Study: Forecasting for a New Transit System
Derivation of Logit Probabilities
GEV
Introduction
Nested Logit
Three-Level Nested Logit
Overlapping Nests
Heteroskedastic Logit
The GEV Family
Probit
Choice Probabilities
Identification
Taste Variation
Substitution Patterns and Failure of IIA
Panel Data
Simulation of the Choice Probabilities
Mixed Logit
Choice Probabilities
Random Coefficients
Error Components
Substitution Patterns
Approximation to Any Random Utility Model
Simulation
Panel Data
Case Study
Variations on a Theme
Introduction
Stated-Preference and Revealed-Preference Data
Ranked Data
Ordered Responses
Contingent Valuation
Mixed Models
Dynamic Optimization
Estimation
Numerical Maximization
Motivation
Notation
Algorithms
Convergence Criterion
Local versus Global Maximum
Variance of the Estimates
Information Identity
Drawing from Densities
Introduction
Random Draws
Variance Reduction
Simulation-Assisted Estimation
Motivation
Definition of Estimators
The Central Limit Theorem
Properties of Traditional Estimators
Properties of Simulation-Based Estimators
Numerical Solution
Individual-Level Parameters
Introduction
Derivation of Conditional Distribution
Implications of Estimation of $$
Monte Carlo Illustration
Average Conditional Distribution
Case Study: Choice of Energy Supplier
Discussion
Bayesian Procedures
Introduction
Overview of Bayesian Concepts
Simulation of the Posterior Mean
Drawing from the Posterior
Posteriors for the Mean and Variance of a Normal Distribution
Hierarchical Bayes for Mixed Logit
Case Study: Choice of Energy Supplier
Bayesian Procedures for Probit Models
Endogeneity
Overview
The BLP Approach
Supply Side
Control Functions
Maximum Likelihood Approach
Case Study: Consumers' Choice among New Vehicles
EM Algorithms
Introduction
General Procedure
Examples of EM Algorithms
Case Study: Demand for Hydrogen Cars
Bibliography
Index

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