# Discrete Choice Methods with Simulation

Cambridge University Press, Jan 13, 2003 - Business & Economics - 334 pages
Focusing on the many advances that are made possible by simulation, this book describes the new generation of discrete choice methods. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

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İçindekiler bölümüne bakınca yakınsama falan var iyi yani, örneğin sıçramalar düzensiz olduğunda acaba kesikli durumdaki zaman skalası ne yapar. İncelemek gerekli. www.google.com.tr "discrete * continuous distributions * maximum likelihood"

### Contents

 Properties of Discrete Choice Models 15 23 Derivation of Choice Probabilities 18 24 Specific Models 21 25 Identification of Choice Models 23 26 Aggregation 33 27 Forecasting 36 28 Recalibration of Constants 37 Logit 38
 73 Ranked Data 160 74 Ordered Responses 163 75 Contingent Valuation 168 76 Mixed Models 170 77 Dynamic Optimization 173 Numerical Maximization 189 83 Algorithms 191 84 Convergence Criterion 202

 32 The Scale Parameter 44 33 Power and Limitations of Logit 46 34 Nonlinear Representative Utility 56 35 Consumer Surplus 59 36 Derivatives and Elasticities 61 37 Estimation 64 38 Goodness of Fit and Hypothesis Testing 71 Forecasting for a New Transit System 75 310 Derivation of Logit Probabilities 78 GEV 80 42 Nested Logit 81 43 ThreeLevel Nested Logit 90 44 Overlapping Nests 93 45 Heteroskedastic Logit 96 46 The GEV Family 97 Probit 101 52 Identification 104 53 Taste Variation 110 54 Substitution Patterns and Failure of IIA 112 55 Panel Data 114 56 Simulation of the Choice Probabilities 118 Mixed Logit 138 62 Random Coefficients 141 63 Error Components 143 64 Substitution Patterns 145 66 Simulation 148 67 Panel Data 149 68 Case Study 151 Variations on a Theme 155 72 StatedPreference and RevealedPreference Data 156
 85 Local versus Global Maximum 203 86 Variance of the Estimates 204 87 Information Identity 205 Drawing from Densities 208 93 Variance Reduction 217 SimulationAssisted Estimation 240 102 Definition of Estimators 241 103 The Central Limit Theorem 248 104 Properties of Traditional Estimators 250 105 Properties of SimulationBased Estimators 253 106 Numerical Solution 260 IndividualLevel Parameters 262 112 Derivation of Conditional Distribution 265 113 Implications of Estimation of Θ 267 114 Monte Carlo Illustration 270 115 Average Conditional Distribution 272 Choice of Energy Supplier 273 117 Discussion 283 12 Bayesian Procedures 285 122 Overview of Bayesian Concepts 287 123 Simulation of the Posterior Mean 294 124 Drawing from the Posterior 296 125 Posteriors for the Mean and Variance of a Normal Distribution 297 126 Hierarchical Bayes for Mixed Logit 302 Choice of Energy Supplier 308 128 Bayesian Procedures for Probit Models 316 Bibliography 319 Index 331 Copyright

### Popular passages

Page 329 - Mixed logit models for recreation demand'. in J. Herriges and C. Kling. eds., Valuing Recreation and the Environment. Edward Elgar, Northampton. MA. Train. K. (2000), 'Halton sequences for mixed logit'.

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