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Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model

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  • William H. Greene
  • David A. Hensher

Abstract

Latent class models offer an alternative perspective to the popular mixed logit form, replacing the continuous distribution with a discrete distribution in which preference heterogeneity is captured by membership of distinct classes of utility description. Within each class, preference homogeneity is usually assumed, although interactions with observed contextual effects are permissible. A natural extension of the fixed parameter latent class model is a random parameter latent class model which allows for another layer of preference heterogeneity within each class. This article sets out the random parameter latent class model and illustrates its applications using a stated choice data set on alternative freight distribution attribute packages pivoted around a recent trip in Australia.

Suggested Citation

  • William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
  • Handle: RePEc:taf:applec:45:y:2013:i:14:p:1897-1902
    DOI: 10.1080/00036846.2011.650325
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    References listed on IDEAS

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    1. Hensher, David A. & Puckett, Sean M. & Rose, John M., 2007. "Agency decision making in freight distribution chains: Establishing a parsimonious empirical framework from alternative behavioural structures," Transportation Research Part B: Methodological, Elsevier, vol. 41(9), pages 924-949, November.
    2. Rose, John M. & Bliemer, Michiel C.J. & Hensher, David A. & Collins, Andrew T., 2008. "Designing efficient stated choice experiments in the presence of reference alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 395-406, May.
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    4. Junyi Shen, 2009. "Latent class model or mixed logit model? A comparison by transport mode choice data," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2915-2924.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, March.
    6. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    7. Sean Puckett & David Hensher & John Rose & Andrew Collins, 2007. "Design and development of a stated choice experiment for interdependent agents: accounting for interactions between buyers and sellers of urban freight services," Transportation, Springer, vol. 34(4), pages 429-451, July.
    8. Everitt, B. S., 1988. "A finite mixture model for the clustering of mixed-mode data," Statistics & Probability Letters, Elsevier, vol. 6(5), pages 305-309, April.
    9. Danielis, Romeo & Marcucci, Edoardo & Rotaris, Lucia, 2005. "Logistics managers' stated preferences for freight service attributes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(3), pages 201-215, May.
    10. Angel Bujosa & Antoni Riera & Robert Hicks, 2010. "Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(4), pages 477-493, December.
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