IDEAS home Printed from https://ideas.repec.org/a/ags/joaaec/100521.html
   My bibliography  Save this article

An Improved Method for Calibrating Purchase Intentions in Stated Preference Demand Models

Author

Listed:
  • Davies, Stephen P.
  • Loomis, John B.

Abstract

The Orbit demand model allows the magnitude of the calibration to stated purchase intentions to vary based on the magnitude of the stated quantities. Using an empirical example of stated trips, we find that the extent of calibration varies substantially with less correction needed at small stated trips (–25%) but larger corrections at higher quantities of stated visits (–48%). We extend the Orbit model to calculate consumer surplus per stated trip of $26. Combining the calibrations in stated trips and value per trip, the Orbit model provides estimates of annual benefits from 60% to 111% less than the count data model.

Suggested Citation

  • Davies, Stephen P. & Loomis, John B., 2010. "An Improved Method for Calibrating Purchase Intentions in Stated Preference Demand Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 42(4), pages 1-15, November.
    • Handle: RePEc:ags:joaaec:100521
    DOI: 10.22004/ag.econ.100521
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/100521/files/jaae317.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.100521?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    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. Crastes dit Sourd, Romain & Zawojska, Ewa & Mahieu, Pierre-Alexandre & Louviere, Jordan, 2018. "Mitigating strategic misrepresentation of values in open-ended stated preference surveys by using negative reinforcement," Journal of choice modelling, Elsevier, vol. 28(C), pages 153-166.
    2. Loomis, John B., 2014. "2013 WAEA Keynote Address: Strategies for Overcoming Hypothetical Bias in Stated Preference Surveys," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(1), pages 1-13, April.
    3. Helga Fehr-Duda & Robin Schimmelpfennig, 2018. "Wider die Zahlengläubigkeit: Sind Befragungsergebnisse eine gute Grundlage für wirtschaftspolitische Entscheidungen?," ECON - Working Papers 297, Department of Economics - University of Zurich, revised Dec 2018.
    4. Fifer, Simon & Rose, John M., 2016. "Can you ever be certain? Reducing hypothetical bias in stated choice experiments via respondent reported choice certaintyAuthor-Name: Beck, Matthew J," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 149-167.
    5. Ewa Zawojska & Pierre-Alexandre Mahieu & Romain Crastes & Jordan Louviere, 2016. "On a way to overcome strategic overbidding in open-ended stated preference surveys: A recoding approach," Working Papers 2016-34, Faculty of Economic Sciences, University of Warsaw.

    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. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    2. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    3. Subal Khumbhakar, 2006. "Specification and estimation of nonstandard profit functions," Empirical Economics, Springer, vol. 31(1), pages 243-260, March.
    4. Holtkamp, A.M. & Brummer, B., 2018. "Environmental efficiency of smallholder rubber production," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277518, International Association of Agricultural Economists.
    5. Giovanni Calice & Levent Kutlu & Ming Zeng, 2021. "Understanding US firm efficiency and its asset pricing implications," Empirical Economics, Springer, vol. 60(2), pages 803-827, February.
    6. Liu, Rui & Lopez Barrera, Emiliano, 2024. "Socioeconomic Drivers of Food Waste Over Time: A Comparative Evaluation of Panel Stochastic Frontier Models for Indirect Quantification in Chinese Households," 2024 Annual Meeting, July 28-30, New Orleans, LA 343852, Agricultural and Applied Economics Association.
    7. Jacob A. Bikker & Barbara Casu & Claudia Girardone & Mohamed E Chaffai & Michel Dietsch & Antonio Colangelo & Robert Inklaar & Marco Colagiovanni & Martin Czurda & Roger Hartmann & Charles-Henri Di Ma, 2009. "Productivity in the Financial Services Sector," SUERF Studies, SUERF - The European Money and Finance Forum, number 2009/4 edited by Morten Balling & Ernest Gnan & Frank Lierman & Jean-Pierre Schoder, May.
    8. Christine Amsler & Artem Prokhorov & Peter Schmidt, 2014. "Using Copulas to Model Time Dependence in Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 497-522, August.
    9. Markose Chekol Zewdie & Michele Moretti & Daregot Berihun Tenessa & Zemen Ayalew Ayele & Jan Nyssen & Enyew Adgo Tsegaye & Amare Sewnet Minale & Steven Van Passel, 2021. "Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach," Land, MDPI, vol. 10(3), pages 1-17, March.
    10. Nathan D. DeLay & Nathanael M. Thompson & James R. Mintert, 2022. "Precision agriculture technology adoption and technical efficiency," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 195-219, February.
    11. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    12. Yaguo Deng & Helena Veiga & Michael P. Wiper, 2019. "Efficiency evaluation of hotel chains: a Spanish case study," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(2), pages 115-139, June.
    13. Andrew Street, 2003. "How much confidence should we place in efficiency estimates?," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 895-907, November.
    14. Deng, Yaguo, 2016. "Efficiency evaluation of Spanish hotel chains," DES - Working Papers. Statistics and Econometrics. WS 23897, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. KimMarie McGoldrick & Lisa Voeks, 2005. "“We Got Game!â€," Journal of Sports Economics, , vol. 6(1), pages 5-23, February.
    16. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    17. Bernardina Algieri & Antonio à lvarez, 2023. "Assessing the ability of regions to attract foreign tourists: The case of Italy," Tourism Economics, , vol. 29(3), pages 788-811, May.
    18. Egon Žižmond & Matjaž Novak, 2006. "Impact of Price-Deregulation on Market Outcomes - The Case of Chimney Sweep Services in Slovenia," Prague Economic Papers, Prague University of Economics and Business, vol. 2006(4), pages 350-363.
    19. José Luis Bonifaz & Reyk Itakura, 2014. "An analysis of inefficiency of big urban water utilities in Latin-America," Working Papers 14-13, Centro de Investigación, Universidad del Pacífico.
    20. Pablo Argüelles & Luis Orea, 2021. "Managing power supply interruptions: a bottom-up spatial (frontier) model with an application to a Spanish electricity network," Empirical Economics, Springer, vol. 60(6), pages 2867-2896, June.

    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:ags:joaaec:100521. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/saeaaea.html .

    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.