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Choice Modelling: Foundational Contributions

Choice Modelling: Foundational Contributions

David A. Hensher , John M. Rose

Edited by David A. Hensher, Professor of Management and Founding Director, Institute of Transport and Logistics Studies (ITLS), The University of Sydney Business School and the University of Sydney and John M. Rose, Professor in Transport and Logistics, Institute of Transport and Logistic Studies (ITLS), University of Sydney, Australia

2011 968 pp Hardback 978 0 85793 727 8

Hardback £325.00 on-line price £292.50

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Series: Classics in Transport and Environmental Valuation series






Description
Choice modelling is an area of growing popularity as many researchers and consultants seek to find better ways to explain the choices made by individuals, households and firms in many application contexts such as transportation, health services, environmental science, marketing, finance, economics, tourism, vacationing, education and employment. Choice modelling as a field began as long ago as 1927 but it was the research in the 1960s and 1970s that cemented the field as a dominant one for studying choice. This authoritative volume, along with an original introduction by the editors, brings together seminal papers that laid out the main features of the booming literature on discrete choice modelling. This timely collection will be of immense value to anyone with an interest in this evolving field of study.

Contents
44 articles, dating from 1927 to 2007 Contributors include: M. Ben-Akiva, A. Daly, W. Greene, R. Luce, D. McFadden, T. Morikawa, D. Revelt, L.L Thurstone, K. Train, H.C.W.L Williams

Further information

Choice modelling is an area of growing popularity as many researchers and consultants seek to find better ways to explain the choices made by individuals, households and firms in many application contexts such as transportation, health services, environmental science, marketing, finance, economics, tourism, vacationing, education and employment. Choice modelling as a field began as long ago as 1927 but it was the research in the 1960s and 1970s that cemented the field as a dominant one for studying choice. This authoritative volume, along with an original introduction by the editors, brings together seminal papers that laid out the main features of the booming literature on discrete choice modelling. This timely collection will be of immense value to anyone with an interest in this evolving field of study.

Full table of contents

Contents:

Acknowledgements

Introduction David A. Hensher and John M. Rose

1. L.L. Thurstone (1927), ‘A Law of Comparative Judgement’
2. Jacob Marschak (1960), ‘Binary-Choice Constraints and Random Utility Indicators’
3. R. Duncan Luce (1977), ‘The Choice Axiom after Twenty Years’
4. John I. Yellott, Jr. (1977), ‘The Relationship between Luce’s Choice Axiom, Thurstone’s Theory of Comparative Judgement, and the Double Exponential Distribution’
5. Kelvin J. Lancaster (1966), ‘A New Approach to Consumer Theory’
6. André de Palma, Gordon M. Myers and Yorgos Y. Papageorgiou (1994), ‘Rational Choice Under an Imperfect Ability to Choose’
7. Charles F. Manski (1977), ‘The Structure of Random Utility Models’
8. Daniel McFadden (1974), ‘Conditional Logit Analysis of Qualitative Choice Behavior’
9. H.C.W.L. Williams (1977), ‘On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit’
10. Kenneth A. Small and Harvey S. Rosen (1981), ‘Applied Welfare Economics with Discrete Choice Models’
11. Moshe Ben-Akiva and Steven R. Lerman (1979), ‘Disaggregate Travel and Mobility-Choice Models and Measures of Accessibility’
12. Charles F. Manski and Steven R. Lerman (1977), ‘The Estimation of Choice Probabilities from Choice Based Samples’
13. Daniel McFadden (1979), ‘Quantitative Methods for Analysing Travel Behaviour of Individuals: Some Recent Developments’
14. Joel L. Horowitz (1984), ‘Testing Disaggregate Travel Demand Models by Comparing Predicted and Observed Market Shares’
15. Carlos F. Daganzo, Fernando Bouthelier and Yosef Sheffi (1977), ‘Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm’
16. Daniel McFadden (1989), ‘A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration’
17. Andrew Daly (1987), ‘Estimating ‘Tree’ Logit Models’
18. David A. Hensher and William H. Greene (2002), ‘Specification and Estimation of the Nested Logit Model: Alternative Normalisations’
19. Frank S. Koppelman and Chieh-Hua Wen (1998), ‘Alternative Nested Logit Models: Structure, Properties and Estimation’
20. Axel Börsch-Supan (1990), ‘On the Compatibility of Nested Logit Models with Utility Maximization’
21. David S. Bunch (1991), ‘Estimability in the Multinomial Probit Model’
22. Joel L. Horowitz (1983), ‘Statistical Comparison of Non-Nested Probabilistic Discrete Choice Models’
23. Paula Armstrong, Rodrigo Garrido and Juan de Dios Ortúzar (2001), ‘Confidence Intervals to Bound the Value of Time’
24. William H. Greene and David A. Hensher (2003), ‘A Latent Class Model for Discrete Choice Analysis: Contrasts with Mixed Logit’
25. J. Hayden Boyd and Robert E. Mellman (1980), ‘The Effect of Fuel Economy Standards on the U.S. Automotive Market: An Hedonic Demand Analysis’
26. N. Scott Cardell and Frederick C. Dunbar (1980), ‘Measuring the Societal Impacts of Automobile Downsizing’
27. David Revelt and Kenneth Train (1998), ‘Mixed Logit with Repeated Choices: Households’ Choices of Appliance Efficiency Level’
28. Daniel McFadden and Kenneth Train (2000), ‘Mixed MNL Models for Discrete Response’
29. David Brownstone and Kenneth Train (1999), ‘Forecasting New Product Penetration with Flexible Substitution Patterns’
30. Chandra R. Bhat (2001), ‘Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model’
31. David A. Hensher and William H. Greene (2003), ‘The Mixed Logit Model: The State of Practice’
32. Michael P. Keane (1997), ‘Current Issues in Discrete Choice Modeling’
33. Lesley Chiou and Joan L. Walker (2007), ‘Masking Identification of Discrete Choice Models Under Simulation Methods’
34. Peter E. Rossi and Greg M. Allenby (1993), ‘A Bayesian Approach to Estimating Household Parameters’
35. Jordan J. Louviere and George Woodworth (1983), ‘Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data’
36. Joffre Swait and Jordan Louviere (1993), ‘The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models’
37. Moshe Ben-Akiva and Takayuki Morikawa (1990), ‘Estimation of Travel Demand Models from Multiple Data Sources’
38. M.A. Bradley and A.J. Daly (1997), ‘Estimation of Logit Choice Models using Mixed Stated-Preference and Revealed-Preference Information’
39. David Hensher, Jordan Louviere and Joffre Swait (1999), ‘Combining Sources of Preference Data’
40. David A. Hensher (2006), ‘How do Respondents Process Stated Choice Experiments? Attribute Consideration Under Varying Information Load’
41. W. Michael Hanemann (1984), ‘Discrete/Continuous Models of Consumer Demand’
42. Jeffrey A. Dubin and Daniel L. McFadden (1984), ‘An Econometric Analysis of Residential Electric Appliance Holdings and Consumption’
43. Chandra R. Bhat (2005), ‘A Multiple Discrete-Continuous Extreme Value Model: Formulation and Application to Discretionary Time-Use Decisions’
44. Kenneth Train and Melvyn Weeks (2005), ‘Discrete Choice Models in Preference Space and Willing-to-Pay Space’



 
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