IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v38y2004i7p657-667.html
   My bibliography  Save this article

Port of destination and carrier selection for fruit exports: a multi-dimensional space-time multi-nomial probit model

Author

Listed:
  • Garrido, Rodrigo A.
  • Leva, Mabel

Abstract

This papers studies the selection of carrier and destination port for Chilean fruit exporters. This double selection is analyzed as a stochastic choice process with time and space interactions. A space-time error structure was specified within a multi-nomial probit model, considering serial correlation, spatial autocorrelation, and state dependence. The modeling approach was successfully applied to the case of grape exporters from Chile to the USA. The results validated the behavioral hypothesis for this process, i.e. there is significant state dependence, serial and spatial correlation in the choice of carrier and destination port, which should be considered when discrete choice models are used to predict export flows.

Suggested Citation

  • Garrido, Rodrigo A. & Leva, Mabel, 2004. "Port of destination and carrier selection for fruit exports: a multi-dimensional space-time multi-nomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 657-667, August.
    • Handle: RePEc:eee:transb:v:38:y:2004:i:7:p:657-667
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(03)00106-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    2. Bolduc, Denis, 1999. "A practical technique to estimate multinomial probit models in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 33(1), pages 63-79, February.
    3. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
    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. Mingzhu Yu & Chung-Yee Lee & James Jixian Wang, 2017. "The regional port competition with different terminal competition intensity," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 659-688, December.
    2. Ziegler Andreas, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(5), pages 630-652, October.
    3. Zhang, Xiunian & Lam, Jasmine Siu Lee, 2018. "Shipping mode choice in cold chain from a value-based management perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 147-167.
    4. Feo-Valero, María & Martínez-Moya, Julián, 2022. "Shippers vs. freight forwarders: Do they differ in their port choice decisions? Evidence from the Spanish ceramic tile industry," Research in Transportation Economics, Elsevier, vol. 95(C).
    5. Andreas Ziegler, 2007. "Simulated classical tests in multinomial probit models," Statistical Papers, Springer, vol. 48(4), pages 655-681, October.

    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. Ziegler, Andreas, 2001. "Simulated z-tests in multinomial probit models," ZEW Discussion Papers 01-53, ZEW - Leibniz Centre for European Economic Research.
    2. David Roodman, 2009. "Estimating Fully Observed Recursive Mixed-Process Models with cmp," Working Papers 168, Center for Global Development.
    3. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    4. GRAMMIG, Joachim & HUJER, Reinhard & SCHEIDLER, Michael, 2001. "The econometrics of airline network management," LIDAM Discussion Papers CORE 2001055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Ziegler Andreas, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(5), pages 630-652, October.
    6. Wall, Melanie M. & Liu, Xuan, 2009. "Spatial latent class analysis model for spatially distributed multivariate binary data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3057-3069, June.
    7. David Roodman, 2011. "Fitting fully observed recursive mixed-process models with cmp," Stata Journal, StataCorp LP, vol. 11(2), pages 159-206, June.
    8. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Fast variational Bayes methods for multinomial probit models," Papers 2202.12495, arXiv.org, revised Oct 2022.
    9. 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.
    10. Friederike Paetz & Winfried J. Steiner, 2017. "The benefits of incorporating utility dependencies in finite mixture probit models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 793-819, July.
    11. Heng Z. Chen & Frank Lupi & John P. Hoehn, 1999. "An Empirical Assessment of Multinomial Probit and Logit Models for Recreation Demand," Chapters, in: Joseph A. Herriges & Catherine L. Kling (ed.), Valuing Recreation and the Environment, chapter 5, pages 141-162, Edward Elgar Publishing.
    12. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    13. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    14. Michael Scheidler & Reinhard Hujer & Joachim Grammig, 2005. "Discrete choice modelling in airline network management," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 467-486.
    15. Paul Gertler & Roland Sturm & Bruce Davidson, 1994. "Information and the Demand for Supplemental Medicare Insurance," NBER Working Papers 4700, National Bureau of Economic Research, Inc.
    16. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    17. Rennings, Klaus & Ziegler, Andreas & Zwick, Thomas, 2001. "Employment changes in environmentally innovative firms," ZEW Discussion Papers 01-46, ZEW - Leibniz Centre for European Economic Research.
    18. Darla Hatton MacDonald & Mark Morrison & Mary Barnes, 2010. "Willingness to Pay and Willingness to Accept Compensation for Changes in Urban Water Customer Service Standards," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(12), pages 3145-3158, September.
    19. Langche Zeng, 2000. "A Heteroscedastic Generalized Extreme Value Discrete Choice Model," Sociological Methods & Research, , vol. 29(1), pages 118-144, August.
    20. Xingcai Zhou & Xinsheng Liu, 2008. "The Monte Carlo EM method for estimating multinomial probit latent variable models," Computational Statistics, Springer, vol. 23(2), pages 277-289, April.

    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:eee:transb:v:38:y:2004:i:7:p:657-667. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    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.