| Introduction | p. 1 |
| Motivation | p. 1 |
| Choice Probabilities and Integration | p. 3 |
| Outline of Book | p. 7 |
| A Couple of Notes | p. 8 |
| Behavioral Models | |
| Properties of Discrete Choice Models | p. 11 |
| Overview | p. 11 |
| The Choice Set | p. 11 |
| Derivation of Choice Probabilities | p. 14 |
| Specific Models | p. 17 |
| Identification of Choice Models | p. 19 |
| Aggregation | p. 29 |
| Forecasting | p. 32 |
| Recalibration of Constants | p. 33 |
| Logit | p. 34 |
| Choice Probabilities | p. 34 |
| The Scale Parameter | p. 40 |
| Power and Limitations of Logit | p. 42 |
| Nonlinear Representative Utility | p. 52 |
| Consumer Surplus | p. 55 |
| Derivatives and Elasticities | p. 57 |
| Estimation | p. 60 |
| Goodness of Fit and Hypothesis Testing | p. 67 |
| Case Study: Forecasting for a New Transit System | p. 71 |
| Derivation of Logit Probabilities | p. 74 |
| GEV | p. 76 |
| Introduction | p. 76 |
| Nested Logit | p. 77 |
| Three-Level Nested Logit | p. 86 |
| Overlapping Nests | p. 89 |
| Heteroskedastic Logit | p. 92 |
| The GEV Family | p. 93 |
| Probit | p. 97 |
| Choice Probabilities | p. 97 |
| Identification | p. 100 |
| Taste Variation | p. 106 |
| Substitution Patterns and Failure of IIA | p. 108 |
| Panel Data | p. 110 |
| Simulation of the Choice Probabilities | p. 114 |
| Mixed Logit | p. 134 |
| Choice Probabilities | p. 134 |
| Random Coefficients | p. 137 |
| Error Components | p. 139 |
| Substitution Patterns | p. 141 |
| Approximation to Any Random Utility Model | p. 141 |
| Simulation | p. 144 |
| Panel Data | p. 145 |
| Case Study | p. 147 |
| Variations on a Theme | p. 151 |
| Introduction | p. 151 |
| Stated-Preference and Revealed-Preference Data | p. 152 |
| Ranked Data | p. 156 |
| Ordered Responses | p. 159 |
| Contingent Valuation | p. 164 |
| Mixed Models | p. 166 |
| Dynamic Optimization | p. 169 |
| Estimation | |
| Numerical Maximization | p. 185 |
| Motivation | p. 185 |
| Notation | p. 185 |
| Algorithms | p. 187 |
| Convergence Criterion | p. 198 |
| Local versus Global Maximum | p. 199 |
| Variance of the Estimates | p. 200 |
| Information Identity | p. 202 |
| Drawing from Densities | p. 205 |
| Introduction | p. 205 |
| Random Draws | p. 205 |
| Variance Reduction | p. 214 |
| Simulation-Assisted Estimation | p. 237 |
| Motivation | p. 237 |
| Definition of Estimators | p. 238 |
| The Central Limit Theorem | p. 245 |
| Properties of Traditional Estimators | p. 247 |
| Properties of Simulation-Based Estimators | p. 250 |
| Numerical Solution | p. 257 |
| Individual-Level Parameters | p. 259 |
| Introduction | p. 259 |
| Derivation of Conditional Distribution | p. 262 |
| Implications of Estimation of $$ | p. 264 |
| Monte Carlo Illustration | p. 267 |
| Average Conditional Distribution | p. 269 |
| Case Study: Choice of Energy Supplier | p. 270 |
| Discussion | p. 280 |
| Bayesian Procedures | p. 282 |
| Introduction | p. 282 |
| Overview of Bayesian Concepts | p. 284 |
| Simulation of the Posterior Mean | p. 291 |
| Drawing from the Posterior | p. 293 |
| Posteriors for the Mean and Variance of a Normal Distribution | p. 294 |
| Hierarchical Bayes for Mixed Logit | p. 299 |
| Case Study: Choice of Energy Supplier | p. 305 |
| Bayesian Procedures for Probit Models | p. 313 |
| Endogeneity | p. 315 |
| Overview | p. 315 |
| The BLP Approach | p. 318 |
| Supply Side | p. 328 |
| Control Functions | p. 334 |
| Maximum Likelihood Approach | p. 340 |
| Case Study: Consumers' Choice among New Vehicles | p. 342 |
| EM Algorithms | p. 347 |
| Introduction | p. 347 |
| General Procedure | p. 348 |
| Examples of EM Algorithms | p. 355 |
| Case Study: Demand for Hydrogen Cars | p. 365 |
| Bibliography | p. 371 |
| Index | p. 385 |
| Table of Contents provided by Ingram. All Rights Reserved. |