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Discrete Choice Methods with Simulation [Pehme köide]

(University of California, Berkeley)
  • Formaat: Paperback / softback, 342 pages, kõrgus x laius x paksus: 229x154x23 mm, kaal: 460 g, 13 Tables, unspecified; 44 Line drawings, unspecified
  • Ilmumisaeg: 13-Jan-2003
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521017157
  • ISBN-13: 9780521017152
Teised raamatud teemal:
  • Formaat: Paperback / softback, 342 pages, kõrgus x laius x paksus: 229x154x23 mm, kaal: 460 g, 13 Tables, unspecified; 44 Line drawings, unspecified
  • Ilmumisaeg: 13-Jan-2003
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521017157
  • ISBN-13: 9780521017152
Teised raamatud teemal:
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.

The author investigates recent discrete choice methods, focusing on advances that simulation has made possible.

Arvustused

'Simulation methods have unshackled discrete choice analysis, breaking down the computational barriers to use of plausible, interpretable models. Kenneth Train provides an excellent road map for both econometric specialists and practitioners through this comprehensive, readable treatment that pulls together the research literature and provides many new and useful results.' Daniel McFadden, Nobel Laureate, University of California, Berkeley 'An outstanding textbook for advanced students and a reference for experienced practitioners of discrete choice analysis. The text covers modern simulation methods that advanced choice modelers should know. The book is blessed by Kenneth Train's unique gift for simplifying and explaining the topic.' Moshe Ben-Akiva, Massachusetts Institute of Technology 'A must have, must read book for academics and practitioners interested in understanding, modelling and predicting decision-making and choice behavior. As we have come to expect from Ken, his new book makes very complex topics accessible to a wide audience. The book covers the basics through to leading-edge thought and work in complex model systems using Bayesian and simulation estimation methods. But wait there's more! There's 'Ken the movie'! You also can watch and listen to Ken's lectures on each topic via his UC Berkeley website and access his lecture notes. An unbeatable package for serious students that sets a new standard in educational communication for the field of probabilistic discrete choice modelling.' Jordan Louviere, University of Technology, Sydney, Australia 'Discrete Choice Methods with Simulation represents a timely and welcome addition to the literature on discrete choice modelling.' Journal of Applied Econometrics

Muu info

The author investigates recent discrete choice methods, focusing on advances that simulation has made possible.
Introduction
1(14)
Motivation
1(2)
Choice Probabilities and Integration
3(4)
Outline of Book
7(1)
Topics Not Covered
8(3)
A Couple of Notes
11(4)
Part I Behavioral Models
Properties of Discrete Choice Models
15(23)
Overview
15(1)
The Choice Set
15(3)
Derivation of Choice Probabilities
18(3)
Specific Models
21(2)
Identification of Choice Models
23(10)
Aggregation
33(3)
Forecasting
36(1)
Recalibration of Constants
37(1)
Logit
38(42)
Choice Probabilities
38(6)
The Scale Parameter
44(2)
Power and Limitations of Logit
46(10)
Nonlinear Representative Utility
56(3)
Consumer Surplus
59(2)
Derivatives and Elasticities
61(3)
Estimation
64(7)
Goodness of Fit and Hypothesis Testing
71(4)
Case Study: Forecasting for a New Transit System
75(3)
Derivation of Logit Probabilities
78(2)
GEV
80(21)
Introduction
80(1)
Nested Logit
81(9)
Three-Level Nested Logit
90(3)
Overlapping Nests
93(3)
Heteroskedastic Logit
96(1)
The GEV Family
97(4)
Probit
101(37)
Choice Probabilities
101(3)
Identification
104(6)
Taste Variation
110(2)
Substitution Patterns and Failure of IIA
112(2)
Panel Data
114(4)
Simulation of the Choice Probabilities
118(20)
Mixed Logit
138(17)
Choice Probabilities
138(3)
Random Coefficients
141(2)
Error Components
143(2)
Substitution Patterns
145(1)
Approximation to Any Random Utility Model
145(3)
Simulation
148(1)
Panel Data
149(2)
Case Study
151(4)
Variations on a Theme
155(34)
Introduction
155(1)
Stated-Preference and Revealed-Preference Data
156(4)
Ranked Data
160(3)
Ordered Responses
163(5)
Contingent Valuation
168(2)
Mixed Models
170(3)
Dynamic Optimization
173(16)
Part II Estimation
Numerical Maximization
189(19)
Motivation
189(1)
Notation
189(2)
Algorithms
191(11)
Convergence Criterion
202(1)
Local versus Global Maximum
203(1)
Variance of the Estimates
204(1)
Information Identity
205(3)
Drawing from Densities
208(32)
Introduction
208(1)
Random Draws
208(9)
Variance Reduction
217(23)
Simulation-Assisted Estimation
240(22)
Motivation
240(1)
Definition of Estimators
241(7)
The Central Limit Theorem
248(2)
Properties of Traditional Estimators
250(3)
Properties of Simulation-Based Estimators
253(7)
Numerical Solution
260(2)
Individual-Level Parameters
262(23)
Introduction
262(3)
Derivation of Conditional Distribution
265(2)
Implications of Estimation of θ
267(3)
Monte Carlo Illustration
270(2)
Average Conditional Distribution
272(1)
Case Study: Choice of Energy Supplier
273(10)
Discussion
283(2)
Bayesian Procedures
285(34)
Introduction
285(2)
Overview of Bayesian Concepts
287(7)
Simulation of the Posterior Mean
294(2)
Drawing from the Posterior
296(1)
Posteriors for the Mean and Variance of a Normal Distribution
297(5)
Hierarchical Bayes for Mixed Logit
302(6)
Case Study: Choice of Energy Supplier
308(8)
Bayesian Procedures for Probit Models
316(3)
Bibliography 319(12)
Index 331