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