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Multinomial Probit with Time-Series Data: Unifying State Dependence and Serial Correlation Models

Author

Listed:
  • C F Daganzo

    (Department of Civil Engineering, University of California, Berkeley, CA 94720, USA)

  • Y Sheffi

    (Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA)

Abstract

This paper develops a general method for treating discrete data sets containing individuals that have made more than one choice under varying stimuli. The multinomial probit model is shown to possess properties that make it very attractive for this application, as with it, it is possible to develop an estimation process that uses all the information in the data, and is both relatively inexpensive and consistent with utility maximization. The method, which is a generalization of Heckman's binary model, can include taste variations and more than two alternatives.

Suggested Citation

  • C F Daganzo & Y Sheffi, 1982. "Multinomial Probit with Time-Series Data: Unifying State Dependence and Serial Correlation Models," Environment and Planning A, , vol. 14(10), pages 1377-1388, October.
    • Handle: RePEc:sae:envira:v:14:y:1982:i:10:p:1377-1388
    DOI: 10.1068/a141377
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    References listed on IDEAS

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    1. Carlos F. Daganzo & Fernando Bouthelier & Yosef Sheffi, 1977. "Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm," Transportation Science, INFORMS, vol. 11(4), pages 338-358, November.
    2. Charles E. Clark, 1961. "The Greatest of a Finite Set of Random Variables," Operations Research, INFORMS, vol. 9(2), pages 145-162, April.
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    Cited by:

    1. Karthik K. Srinivasan & Hani S. Mahmassani, 2005. "A Dynamic Kernel Logit Model for the Analysis of Longitudinal Discrete Choice Data: Properties and Computational Assessment," Transportation Science, INFORMS, vol. 39(2), pages 160-181, May.
    2. 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.
    3. Víctor Cantillo & Juan de Dios Ortúzar & Huw C. W. L. Williams, 2007. "Modeling Discrete Choices in the Presence of Inertia and Serial Correlation," Transportation Science, INFORMS, vol. 41(2), pages 195-205, May.
    4. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 92233, University Library of Munich, Germany.
    5. Kitamura, Ryuichi, 1990. "Panel Analysis in Transportation Planning: An Overview," University of California Transportation Center, Working Papers qt86v0f7zh, University of California Transportation Center.
    6. Kitamura, Ryuichi & Bunch, David S., 1990. "Heterogeneity and State Dependence in Household Car Ownership: A Panel Analysis Using Ordered-Response Probit Models with Error Components," University of California Transportation Center, Working Papers qt0qv4q55r, University of California Transportation Center.
    7. Fischer, Manfred M. & Nijkamp, Peter, 1987. "From static towards dynamic discrete choice modelling : A State of the Art Review," Regional Science and Urban Economics, Elsevier, vol. 17(1), pages 3-27, February.

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