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Reconsidering the multinomial probit model

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  • Horowitz, Joel L.
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    Abstract

    Recently developed computational methods have greatly reduced the difficulty of estimating multinomial probit models and may soon make multinomial probit a computationally feasible option in applied travel demand modeling. This paper discusses some of the benefits and costs that are associated with the use of multinomial probit in demand modeling. It is argued that although there are situations in which multinomial probit is essential for achieving a satisfactory model, most problems with existing demand models are unlikely to be mitigated by the use of multinomial probit.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 25 (1991)
    Issue (Month): 6 (December)
    Pages: 433-438

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    Handle: RePEc:eee:transb:v:25:y:1991:i:6:p:433-438

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    Cited by:
    1. Mokhtarian, Patricia L & Ory, David T, 2005. "Don't Work, Work at Home, or Commute? Discrete Choice Models of the Decision for San Francisco Bay Area Residents," Institute of Transportation Studies, Working Paper Series qt5cs0q85s, Institute of Transportation Studies, UC Davis.
    2. Tang, Wei & Mokhtarian, Patricia L & Handy, Susan L, 2008. "The Role of Neighborhood Characteristics in the Adoption and Frequency of Working at Home: Empirical Evidence from Northern California," Institute of Transportation Studies, Working Paper Series qt13x2q3rb, Institute of Transportation Studies, UC Davis.
    3. Can, Vo Van, 2013. "Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 149-159.
    4. Bhat, Chandra R., 1997. "Covariance heterogeneity in nested logit models: Econometric structure and application to intercity travel," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 11-21, February.
    5. Lambe, Thomas A., 1996. "Driver choice of parking in the city," Socio-Economic Planning Sciences, Elsevier, vol. 30(3), pages 207-219, September.
    6. Koppelman, Frank S. & Wen, Chieh-Hua, 2000. "The paired combinatorial logit model: properties, estimation and application," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 75-89, February.
    7. Ory, David T. & Mokhtarian, Patricia L., 2005. "Modeling the Joint Labor-Commute Engagement Decisions of San Francisco Bay Area Residents," University of California Transportation Center, Working Papers qt7600m6qv, University of California Transportation Center.
    8. GRAMMIG, Joachim & HUJER, Reinhard & SCHEIDLER, Michael, 2001. "The econometrics of airline network management," CORE Discussion Papers 2001055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    10. Yai, Tetsuo & Iwakura, Seiji & Morichi, Shigeru, 1997. "Multinomial probit with structured covariance for route choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 31(3), pages 195-207, June.
    11. Bhat, Chandra R., 1998. "Accommodating flexible substitution patterns in multi-dimensional choice modeling: formulation and application to travel mode and departure time choice," Transportation Research Part B: Methodological, Elsevier, vol. 32(7), pages 455-466, September.
    12. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    13. Ory, D T & Mokhtarian, Patricia L, 2005. "Don’t Work, Work at Home, or Commute? Discrete Choice Models of the Decision for San Francisco Bay Area Residents," Institute of Transportation Studies, Working Paper Series qt71q8b94r, Institute of Transportation Studies, UC Davis.

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