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Marketing Letters

, Volume 13, Issue 3, pp 163–175 | Cite as

Hybrid Choice Models: Progress and Challenges

  • Moshe Ben-Akiva
  • Daniel Mcfadden
  • Kenneth Train
  • Joan Walker
  • Chandra Bhat
  • Michel Bierlaire
  • Denis Bolduc
  • Axel Boersch-Supan
  • David Brownstone
  • David S. Bunch
  • Andrew Daly
  • Andre De Palma
  • Dinesh Gopinath
  • Anders Karlstrom
  • Marcela A. Munizaga
Article

Abstract

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

choice modeling mixed logit logit kernel simulation estimation latent variables 

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References

  1. Ben-Akiva, M., D. Bolduc, and J. Walker (2001). ''Specification, Estimation, & Identification of the Logit Kernel (or Continuous Mixed Logit) Model,'' Working Paper, MIT.Google Scholar
  2. Ben Akiva, M., M. Bradley, T. Morikawa, J. Benjamin, T. Novak, H. Oppewal, and V. Rao (1994). ''Combining Revealed and Stated Preferences Data,'' Marketing Letters, 5(4), 335–350.Google Scholar
  3. Ben-Akiva, M., McFadden, M. Abe, U. Böckenholt, D. Doldue, D. Gopinath, T. Morikawa, V. Ramaswamy, V. Rao, D. Revelt, and D. Steinberg (1997). ''Modeling Methods for Discrete Choice Analysis,'' Marketing Letters, 8(3), 273–286.Google Scholar
  4. Ben-Akiva, M., D. McFadden, T. Gärling, D. Gopinath, J. Walker, D. Bolduc, A. Boersch-Supan, P. Delquié, O. Larichev, T. Morikawa, A. Polydoropoulou, and V. Rao (1999). ''Extended Framework for Modeling Choice Behavior,'' Marketing Letters, 10(3), 187–203.Google Scholar
  5. Bhat, C. R. (2001a). ''Quasi-random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model,'' Transportation Research, vol 35B, pp. 677–693, August 2001.Google Scholar
  6. Bhat, C. R. (2001b). ''Simulation Estimation of Mixed Discrete Choice Models Using Randomized and Scrambled Halton Sequences,'' Working Paper, University of Texas at Austin.Google Scholar
  7. Bhat, C.R., and S. Castelar (2002). ''A Unified Mixed Logit Framework for Modeling Revealed and Stated Preferences: Formulation and Application to Congestion Pricing Analysis in the San Francisco Bay Area,'' Transportation Research, vol 36B, No. 7, pp. 593–616, August 2002.Google Scholar
  8. Bierlaire, M. (2001). ''A General Formulation of the Cross-Nested Logit Model,'' Proceedings of the 1st Swiss Transportation Research Conference, Ascona, Switzerland.Google Scholar
  9. Bierlaire, M., K. Axhausen, and G. Abay (2001). ''Acceptance of Model Innovation: The Case of the Swissmetro,'' Proceedings of the 1st Swiss Transportation Research Conference, Ascona, Switzerland.Google Scholar
  10. Bolduc, D. (1999). ''A Practical Technique to Estimate Multinomial Probit Models in Transportation,'' Transportation Research B, 33, 63–79.Google Scholar
  11. Bolduc, D., Fortin, B., and Gordon, S. (1997). “Multinomial Probit Estimation of Spatially Interdependent Choices: An Empirical Comparison of Two New Techniques,” International Regional Science Review, 20(1&2), 77–101.Google Scholar
  12. Bolduc, D., Khalaf, L., and Moyneur, É. (2001). “Joint Discrete/Continuous Models with Possibly Weak Identification,“ Working Paper, Département d'économique, Université Laval.Google Scholar
  13. Brownstone, D., Bunch, D. S., and Train, K. (2000). ''Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-fuel Vehicles,'' Transportation Research B, 34(5), 315–338.Google Scholar
  14. Brownstone, D., Golob, T. F., and Kazimi, C. (2001). ''Modeling Non-ignorable Attrition and Measurement Error in Panel Surveys: An Application to Travel Demand Modeling,'' Chapter 25 in Survey Nonresponse, Editors, R. M. Groves, D. Dillman, J. L. Eltinge and R. J. A. Little, New York: Wiley, forthcoming.Google Scholar
  15. Bunch, D. S. (2001). ''Information and Sample Size Requirements for Estimating Non-IID Discrete Choice Models Using Stated-Choice Experiments,'' Working Paper, Graduate School of Management, University of California, Davis.Google Scholar
  16. Daly, A. J. (2001). ''The Recursive Nested Extreme Value Model,'' Working Paper 559, Institute for Transport Studies, University of Leeds.Google Scholar
  17. de Palma, A., and Kilani, K. (2001). ''Switching Probabilities for Discrete Choice Model Estimations,'' Working Paper, Thema, University of Cergy-Pontoise, France.Google Scholar
  18. Dufour, J.-M., and J. Jasiak. (2000). ''Finite Sample Limited Information Inference Methods for Structural Equations and Models with Generated Regressors,'' International Economic Review, forthcoming.Google Scholar
  19. Eymann, A., A. Boersch-Supan, and R. Euwals. (2001). ''Risk Attitude, Impatience, and Portfolio Choice,'' Working Paper, University of Mannheim, Germany.Google Scholar
  20. Karlstrom, A. (2001). ''Developing Generalized Extreme Value Models Using the Pickands' Representation Theorem,'' Working Paper, Infrastructure and Planning, Royal Institute of Technology, Stockholm, Sweden.Google Scholar
  21. McFadden, D. (2001). ''Economic Choices,'' The American Economic Review, 91(3), 351–378.Google Scholar
  22. McFadden, D., and K. Train. (2000). ''Mixed MNL Models for Discrete Response,'' Journal of Applied Econometrics, 15(5), 447–470.Google Scholar
  23. Munizaga, M. A., and R. Alvarez-Daziano. (2001). ''Mixed Logit Versus Nested Logit and Probit,'' Working Paper, Departamento de Ingenieria Civil, Universidad de Chile.Google Scholar
  24. Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley.Google Scholar
  25. Train, K. (1999). ''Halton Sequences for Mixed Logit,'' Working Paper, Department of Economics, University of California, Berkeley.Google Scholar
  26. Train, K. (2001). ''A Comparison of Hierarchical Bayes and Maximum Simulated Likelihood for Mixed Logit,'' Working Paper, Department of Economics, University of California, Berkeley.Google Scholar
  27. Walker, J., and M. Ben-Akiva. (2001). ''Extensions of the Random Utility Model,'' Working Paper, MIT.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Moshe Ben-Akiva
    • 1
  • Daniel Mcfadden
    • 2
  • Kenneth Train
    • 2
  • Joan Walker
    • 1
  • Chandra Bhat
    • 3
  • Michel Bierlaire
    • 4
  • Denis Bolduc
    • 5
  • Axel Boersch-Supan
    • 6
  • David Brownstone
    • 7
  • David S. Bunch
    • 8
  • Andrew Daly
    • 9
  • Andre De Palma
    • 10
  • Dinesh Gopinath
    • 11
  • Anders Karlstrom
    • 12
  • Marcela A. Munizaga
    • 13
  1. 1.Massachusetts Institute of TechnologyUSA
  2. 2.University of California at BerkeleyUSA
  3. 3.USA
  4. 4.Ecole Polytechnique Fédérale de LausanneCanada
  5. 5.Université LavalCanada
  6. 6.Universität MannheimCanada
  7. 7.University of California at IrvineUSA
  8. 8.University of California at DavisUSA
  9. 9.RAND EuropeUK
  10. 10.University of Cergy-PontoiseFrance
  11. 11.Mercer Management ConsultingUSA
  12. 12.Royal Institute of TechnologySweden
  13. 13.Universidad de ChileChile

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