IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v6y2013icp60-73.html
   My bibliography  Save this article

Estimation efficiency of RP/SP models considering SP design and error structures

Author

Listed:
  • Sanko, Nobuhiro
  • Yamamoto, Toshiyuki

Abstract

SP attribute values are sometimes set X times or 1/X times the RP attribute values in the direction that changes respondents' RP behaviour. In estimating RP/SP models, assumptions on error structures are required: (a) RP and SP have both a common error component and independent error components (general model); (b) RP and SP have a common error component and SP only has an independent error component (SP-off-RP model); (c) RP and SP have independent error components only (independent model); and (d) RP and SP have a common error component only (double-bound model). This study simulates and examines the estimation efficiency of RP/SP models based on the D-error considering both error structures and attribute differences. Insights obtained are the following. (1) The general model offers better estimation efficiency in the neighbourhood of X=1.0. (2) For the SP-off-RP model and the independent model, the larger the variance in the error components of the SP model relative to the RP model, the larger the value of X that is required to minimise the D-error. The authors propose a method for designing an SP experiment in which the level-of-service of the SP differs from that of the RP by only a single attribute value.

Suggested Citation

  • Sanko, Nobuhiro & Yamamoto, Toshiyuki, 2013. "Estimation efficiency of RP/SP models considering SP design and error structures," Journal of choice modelling, Elsevier, vol. 6(C), pages 60-73.
    • Handle: RePEc:eee:eejocm:v:6:y:2013:i:c:p:60-73
    DOI: 10.1016/j.jocm.2013.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534513000067
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2013.04.001?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
    ---><---

    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. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
    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.
    3. Bliemer, Michiel C.J. & Rose, John M. & Hensher, David A., 2009. "Efficient stated choice experiments for estimating nested logit models," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 19-35, January.
    4. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, October.
    5. Caussade, Sebastián & Ortúzar, Juan de Dios & Rizzi, Luis I. & Hensher, David A., 2005. "Assessing the influence of design dimensions on stated choice experiment estimates," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 621-640, August.
    6. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    7. Kessels, Roselinde & Jones, Bradley & Goos, Peter & Vandebroek, Martina, 2009. "An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 279-291.
    8. Puckett, Sean M. & Hensher, David A., 2009. "Revealing the extent of process heterogeneity in choice analysis: An empirical assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(2), pages 117-126, February.
    9. Michael Hanemann & John Loomis & Barbara Kanninen, 1991. "Statistical Efficiency of Double-Bounded Dichotomous Choice Contingent Valuation," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(4), pages 1255-1263.
    10. Alberini Anna, 1995. "Optimal Designs for Discrete Choice Contingent Valuation Surveys: Single-Bound, Double-Bound, and Bivariate Models," Journal of Environmental Economics and Management, Elsevier, vol. 28(3), pages 287-306, May.
    11. Train, Kenneth & Wilson, Wesley W., 2008. "Estimation on stated-preference experiments constructed from revealed-preference choices," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 191-203, March.
    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. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.

    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. Bliemer, Michiel C.J. & Rose, John M., 2011. "Experimental design influences on stated choice outputs: An empirical study in air travel choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 63-79, January.
    2. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
    3. John Rose & Michiel Bliemer, 2013. "Sample size requirements for stated choice experiments," Transportation, Springer, vol. 40(5), pages 1021-1041, September.
    4. Danaf, Mazen & Atasoy, Bilge & de Azevedo, Carlos Lima & Ding-Mastera, Jing & Abou-Zeid, Maya & Cox, Nathaniel & Zhao, Fang & Ben-Akiva, Moshe, 2019. "Context-aware stated preferences with smartphone-based travel surveys," Journal of choice modelling, Elsevier, vol. 31(C), pages 35-50.
    5. Palhazi Cuervo, Daniel & Kessels, Roselinde & Goos, Peter & Sörensen, Kenneth, 2016. "An integrated algorithm for the optimal design of stated choice experiments with partial profiles," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 648-669.
    6. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
    7. Joan L. Walker & Yanqiao Wang & Mikkel Thorhauge & Moshe Ben-Akiva, 2018. "D-efficient or deficient? A robustness analysis of stated choice experimental designs," Theory and Decision, Springer, vol. 84(2), pages 215-238, March.
    8. van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.
    9. González, Rosa Marina & Román, Concepción & Ortúzar, Juan de Dios, 2019. "Preferences for sustainable mobility in natural areas: The case of Teide National Park," Journal of Transport Geography, Elsevier, vol. 76(C), pages 42-51.
    10. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
    11. Richard Yao & Riccardo Scarpa & John Rose & James Turner, 2015. "Experimental Design Criteria and Their Behavioural Efficiency: An Evaluation in the Field," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(3), pages 433-455, November.
    12. Esther W. de Bekker‐Grob & Mandy Ryan & Karen Gerard, 2012. "Discrete choice experiments in health economics: a review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 145-172, February.
    13. Kerr, Geoffrey N. & Sharp, Basil M.H., 2010. "Choice experiment adaptive design benefits: a case study," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 54(4), pages 1-14.
    14. Haghani, Milad & Sarvi, Majid, 2018. "Hypothetical bias and decision-rule effect in modelling discrete directional choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 361-388.
    15. Greiner, Romy & Bliemer, Michiel & Ballweg, Julie, 2014. "Design considerations of a choice experiment to estimate likely participation by north Australian pastoralists in contractual biodiversity conservation," Journal of choice modelling, Elsevier, vol. 10(C), pages 34-45.
    16. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers 2102.02945, arXiv.org.
    17. Kessels, Roselinde, 2016. "Homogeneous versus heterogeneous designs for stated choice experiments: Ain't homogeneous designs all bad?," Journal of choice modelling, Elsevier, vol. 21(C), pages 2-9.
    18. Chaniotakis, Emmanouil & Pel, Adam J., 2015. "Drivers’ parking location choice under uncertain parking availability and search times: A stated preference experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 228-239.
    19. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
    20. Weibo Li & Maria Kamargianni, 2020. "Steering short-term demand for car-sharing: a mode choice and policy impact analysis by trip distance," Transportation, Springer, vol. 47(5), pages 2233-2265, October.

    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:eejocm:v:6:y:2013:i:c:p:60-73. 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.journals.elsevier.com/journal-of-choice-modelling .

    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.