IDEAS home Printed from https://ideas.repec.org/a/cup/jagaec/v42y2010i04p679-693_00.html
   My bibliography  Save this article

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

Author

Listed:
  • Davies, Stephen
  • Loomis, John

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 & Loomis, John, 2010. "An Improved Method for Calibrating Purchase Intentions in Stated Preference Demand Models," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 42(4), pages 679-693, November.
    • Handle: RePEc:cup:jagaec:v:42:y:2010:i:04:p:679-693_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1074070800003886/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2021. "How does public spending affect technical efficiency? Some evidence from 15 European countries," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 108-130, January.
    9. Subal Kumbhakar & Efthymios Tsionas, 2008. "Scale and efficiency measurement using a semiparametric stochastic frontier model: evidence from the U.S. commercial banks," Empirical Economics, Springer, vol. 34(3), pages 585-602, June.
    10. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    11. Tran, Kien C. & Tsionas, Mike G. & Prokhorov, Artem B., 2023. "Semiparametric estimation of spatial autoregressive smooth-coefficient panel stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1189-1199.
    12. Maruyama, Eduardo & Schollard, Phoebe, 2021. "Geographic prioritization of agricultural investments: Prioritization of agricultural and nutrition investments," 2021 Conference, August 17-31, 2021, Virtual 315292, International Association of Agricultural Economists.
    13. Hung-Jen Wang, 2002. "Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model," Journal of Productivity Analysis, Springer, vol. 18(3), pages 241-253, November.
    14. Diego A. Restrepo-Tobón & Subal C. Kumbhakar, 2013. "Profit efficiency of U.S. commercial banks: a decomposition," Documentos de Trabajo de Valor Público 10939, Universidad EAFIT.
    15. Fumitoshi Mizutani & Eri Nakamura, 2017. "How do governance factors affect inefficiency? Stochastic frontier analysis of public utility firms in Japan," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 44(3), pages 267-289, September.
    16. Walter, Matthias & Cullmann, Astrid & von Hirschhausen, Christian & Wand, Robert & Zschille, Michael, 2009. "Quo vadis efficiency analysis of water distribution? A comparative literature review," Utilities Policy, Elsevier, vol. 17(3-4), pages 225-232, September.
    17. Castiglione, Concetta & Infante, Davide & Zieba, Marta, 2023. "Public support for performing arts. Efficiency and productivity gains in eleven European countries," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    18. Getu Hailu & B. James Deaton, 2016. "Agglomeration Effects in Ontario’s Dairy Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 1055-1073.
    19. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
    20. Millimet, Daniel L. & Parmeter, Christopher F., 2022. "Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable," Political Analysis, Cambridge University Press, vol. 30(1), pages 66-88, January.

    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:cup:jagaec:v:42:y:2010:i:04:p:679-693_00. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/aae .

    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.