Discrete choice methods with simulation

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Discrete choice methods with simulation / Kenneth E. Train

版:: 2nd ed
資料種別:: 図書
出版情報:: New York : Cambridge University Press, 2009
形態:: x, 388 p. ; 23 cm
著者名:: Train, Kenneth <DA00375025>
ISBN:: 9780521766555 [0521766559] (: hbk)
9780521747387 [0521747384] (: pbk)
書誌ID:: BA91054877

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注記:

Includes bibliographical references and index

主題:

Consumers' preferences--Simulation methods; Decision making--Simulation methods

言語:

英語

目次情報:

Introduction / 1：

Motivation / 1.1：

Choice Probabilities and Integration / 1.2：

Outline of Book / 1.3：

A Couple of Notes / 1.4：

Behavioral Models / Part I：

Properties of Discrete Choice Models / 2：

Overview / 2.1：

The Choice Set / 2.2：

Derivation of Choice Probabilities / 2.3：

Specific Models / 2.4：

Identification of Choice Models / 2.5：

Aggregation / 2.6：

Forecasting / 2.7：

Recalibration of Constants / 2.8：

Logit / 3：

Choice Probabilities / 3.1：

The Scale Parameter / 3.2：

Power and Limitations of Logit / 3.3：

Nonlinear Representative Utility / 3.4：

Consumer Surplus / 3.5：

Derivatives and Elasticities / 3.6：

Estimation / 3.7：

Goodness of Fit and Hypothesis Testing / 3.8：

Case Study: Forecasting for a New Transit System / 3.9：

Derivation of Logit Probabilities / 3.10：

GEV / 4：

Nested Logit / 4.1：

Three-Level Nested Logit / 4.3：

Overlapping Nests / 4.4：

Heteroskedastic Logit / 4.5：

The GEV Family / 4.6：

Probit / 5：

Identification / 5.1：

Taste Variation / 5.3：

Substitution Patterns and Failure of IIA / 5.4：

Panel Data / 5.5：

Simulation of the Choice Probabilities / 5.6：

Mixed Logit / 6：

Random Coefficients / 6.1：

Error Components / 6.3：

Substitution Patterns / 6.4：

Approximation to Any Random Utility Model / 6.5：

Simulation / 6.6：

Case Study / 6.7：

Variations on a Theme / 7：

Stated-Preference and Revealed-Preference Data / 7.1：

Ranked Data / 7.3：

Ordered Responses / 7.4：

Contingent Valuation / 7.5：

Mixed Models / 7.6：

Dynamic Optimization / 7.7：

Numerical Maximization / Part II：

Notation / 8.1：

Algorithms / 8.3：

Convergence Criterion / 8.4：

Local versus Global Maximum / 8.5：

Variance of the Estimates / 8.6：

Information Identity / 8.7：

Drawing from Densities / 9：

Random Draws / 9.1：

Variance Reduction / 9.3：

Simulation-Assisted Estimation / 10：

Definition of Estimators / 10.1：

The Central Limit Theorem / 10.3：

Properties of Traditional Estimators / 10.4：

Properties of Simulation-Based Estimators / 10.5：

Numerical Solution / 10.6：

Individual-Level Parameters / 11：

Derivation of Conditional Distribution / 11.1：

Implications of Estimation of $$ / 11.3：

Monte Carlo Illustration / 11.4：

Average Conditional Distribution / 11.5：

Case Study: Choice of Energy Supplier / 11.6：

Discussion / 11.7：

Bayesian Procedures / 12：

Overview of Bayesian Concepts / 12.1：

Simulation of the Posterior Mean / 12.3：

Drawing from the Posterior / 12.4：

Posteriors for the Mean and Variance of a Normal Distribution / 12.5：

Hierarchical Bayes for Mixed Logit / 12.6：

Bayesian Procedures for Probit Models / 12.7：

Endogeneity / 13：

The BLP Approach / 13.1：

Supply Side / 13.3：

Control Functions / 13.4：

Maximum Likelihood Approach / 13.5：

Case Study: Consumers' Choice among New Vehicles / 13.6：

EM Algorithms / 14：

General Procedure / 14.1：

Examples of EM Algorithms / 14.3：

Case Study: Demand for Hydrogen Cars / 14.4：

Bibliography

Index

Properties

Mixed logit

Variations on a theme

Numerical maximization

Drawing from densities

Simulation-assisted estimation

Individual-level parameters

Bayesian procedures

EM algorithms

Introduction / 1：

Motivation / 1.1：

Choice Probabilities and Integration / 1.2：

Outline of Book / 1.3：

A Couple of Notes / 1.4：

Behavioral Models / Part I：

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