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: |