Discrete Choice Methods with Simulation

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Cambridge University Press, Jan 13, 2003 - Business & Economics - 334 pages
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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
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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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