IDEAS home Printed from https://ideas.repec.org/a/sae/envira/v14y1982i10p1377-1388.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/a141377
    Download Restriction: no
    File URL: https://libkey.io/10.1068/a141377?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 90260, 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tasos Nikoleris & Mark Hansen, 2012. "Queueing Models for Trajectory-Based Aircraft Operations," Transportation Science, INFORMS, vol. 46(4), pages 501-511, November.
    2. Kenneth Train, "undated". "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Working Papers _009, University of California at Berkeley, Econometrics Laboratory Software Archive.
    3. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    4. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
    5. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    6. Wada, Kentaro & Usui, Kento & Takigawa, Tsubasa & Kuwahara, Masao, 2018. "An optimization modeling of coordinated traffic signal control based on the variational theory and its stochastic extension," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 907-925.
    7. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    8. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    9. Federica Bomboi & Christoph Buchheim & Jonas Pruente, 2022. "On the stochastic vehicle routing problem with time windows, correlated travel times, and time dependency," 4OR, Springer, vol. 20(2), pages 217-239, June.
    10. Bolduc, Denis & Kaci, Mustapha, 1993. "Estimation des modèles probit polytomiques : un survol des techniques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 69(3), pages 161-191, septembre.
    11. F. L. Wolf & L. A. Grzelak & G. Deelstra, 2022. "Cheapest-to-deliver collateral: a common factor approach," Quantitative Finance, Taylor & Francis Journals, vol. 22(4), pages 707-723, April.
    12. Urban, Timothy L. & Chiang, Wen-Chyuan, 2016. "Designing energy-efficient serial production lines: The unpaced synchronous line-balancing problem," European Journal of Operational Research, Elsevier, vol. 248(3), pages 789-801.
    13. Wen-Chyuan Chiang & Timothy L. Urban & Chunyong Luo, 2016. "Balancing stochastic two-sided assembly lines," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6232-6250, October.
    14. Hanemann, W. Michael, 1981. "Applied Welfare Analysis with Quantal Choice Models," CUDARE Working Papers 198217, University of California, Berkeley, Department of Agricultural and Resource Economics.
    15. Qingyun Tian & Yun Hui Lin & David Z. W. Wang, 2021. "Autonomous and conventional bus fleet optimization for fixed-route operations considering demand uncertainty," Transportation, Springer, vol. 48(5), pages 2735-2763, October.
    16. Sajid, Mohammad & Raza, Zahid, 2017. "Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system," Energy, Elsevier, vol. 125(C), pages 258-274.
    17. Trietsch, Dan & Mazmanyan, Lilit & Gevorgyan, Lilit & Baker, Kenneth R., 2012. "Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation," European Journal of Operational Research, Elsevier, vol. 216(2), pages 386-396.
    18. Yao Zhao & David Simchi-Levi, 2006. "Performance Analysis and Evaluation of Assemble-to-Order Systems with Stochastic Sequential Lead Times," Operations Research, INFORMS, vol. 54(4), pages 706-724, August.
    19. Samir Ghazouani & Mohamed Goaïed, 1993. "Analyse micro-économétrique de la demande de transport urbain pour la ville de Tunis," Économie et Prévision, Programme National Persée, vol. 108(2), pages 47-62.
    20. Barış Ata & Anton Skaro & Sridhar Tayur, 2017. "OrganJet: Overcoming Geographical Disparities in Access to Deceased Donor Kidneys in the United States," Management Science, INFORMS, vol. 63(9), pages 2776-2794, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envira:v:14:y:1982:i:10:p:1377-1388. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.