IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/6053.html
   My bibliography  Save this paper

When are Supply and Demand Determined Recursively Rather than Simultaneously? Another look at the Fulton Fish Market Data

Author

Listed:
  • Graddy, Kathryn
  • Kennedy, Peter E

Abstract

When a supply and demand model is recursive, with errors uncorrelated across the two equations, ordinary least squares (OLS) is the recommended estimation procedure. Supply to a daily fish market is determined by the previous night?s catch, so this would appear to be a good example of a recursive market. Despite this, data from the Fulton fish market are treated in the literature, without explanation, as coming from a simultaneous-equations market. We provide the missing explanation: inventory changes, influenced by current price, affect daily supply. Instrumental variable estimates using the full data set differ very little from OLS estimates using only observations with little inventory change, providing strong support for our explanation. Finally, we note that because of inventory changes, estimates of supply price elasticities in high-frequency markets must be interpreted with care.

Suggested Citation

  • Graddy, Kathryn & Kennedy, Peter E, 2007. "When are Supply and Demand Determined Recursively Rather than Simultaneously? Another look at the Fulton Fish Market Data," CEPR Discussion Papers 6053, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:6053
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP6053
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kennedy, Peter E, 2002. "Sinning in the Basement: What Are the Rules? The Ten Commandments of Applied Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 16(4), pages 569-589, September.
    2. Daniel B. Suits, 1955. "An Econometric Model of the Watermelon Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 37(2), pages 237-251.
    3. Hausman, Jerry A., 1983. "Specification and estimation of simultaneous equation models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 7, pages 391-448, Elsevier.
    4. Joshua D. Angrist & Kathryn Graddy & Guido W. Imbens, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(3), pages 499-527.
    5. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    6. Peter E. Kennedy, 2002. "Sinning in the Basement: What are the Rules? The Ten Commandments of Applied Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 16(4), pages 569-589, September.
    7. Kathryn Graddy, 1995. "Testing for Imperfect Competition at the Fulton Fish Market," RAND Journal of Economics, The RAND Corporation, vol. 26(1), pages 75-92, Spring.
    8. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    Full references (including those not matched with items on IDEAS)

    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. Kathryn Graddy, 2006. "Markets: The Fulton Fish Market," Journal of Economic Perspectives, American Economic Association, vol. 20(2), pages 207-220, Spring.
    2. Graddy, Kathryn & Hall, George, 2011. "A dynamic model of price discrimination and inventory management at the Fulton Fish Market," Journal of Economic Behavior & Organization, Elsevier, vol. 80(1), pages 6-19.
    3. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    4. George Halkos & Kyriaki Tsilika, 2015. "Programming Identification Criteria in Simultaneous Equation Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 157-170, June.
    5. Christopher L. Gilbert & Duo Qin, 2005. "The First Fifty Years of Modern Econometrics," Working Papers 544, Queen Mary University of London, School of Economics and Finance.
    6. Thomas Mayer, 2006. "The Empirical Significance of Econometric Models," Working Papers 620, University of California, Davis, Department of Economics.
    7. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    8. Michael O'Connor Keefe & James Tate & Henk Berkman, 2013. "Is the relationship between investment and conditional cash flow volatility ambiguous, asymmetric or both?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 913-947, December.
    9. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    10. Teresa Briz & Andreas C. Drichoutis & Rodolfo M. Nayga, Jr, 2014. "Detecting false positives in experimental auctions: A case study of projection bias in food consumption," Working Papers 2014-4, Agricultural University of Athens, Department Of Agricultural Economics.
    11. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    12. Kiviet, Jan, 2019. "Instrument-free inference under confined regressor endogeneity; derivations and applications," MPRA Paper 96839, University Library of Munich, Germany.
    13. Christopher L. Gilbert & Duo Qin, 2005. "The First Fifty Years of Modern Econometrics," Working Papers 544, Queen Mary University of London, School of Economics and Finance.
    14. Duo Qin, 2019. "Let’s take the bias out of econometrics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 26(2), pages 81-98, April.
    15. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    16. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    17. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute of Labor Economics (IZA).
    18. Marek Gruszczyñski, 2018. "Good Practices in Empirical Corporate Finance and Accounting Research," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 2(10), pages 45-51, December.
    19. Judith A. Clarke & Nilanjana Roy & Marsha J. Courchane, 2006. "On the Robustness of Racial Disrcimination Findings in Motgage Lending Studies," Econometrics Working Papers 0604, Department of Economics, University of Victoria.
    20. Eduardo Loría & Raúl Tirado, 2022. "Sacrifice rate and labour precariousness in Mexico, 2005Q1-2019Q4," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 41(87), pages 427-456, December.

    More about this item

    Keywords

    Demand; Estimation; Fish; Fulton market; Inventories; Simultaneous equations;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • L6 - Industrial Organization - - Industry Studies: Manufacturing

    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:cpr:ceprdp:6053. 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: the person in charge (email available below). General contact details of provider: https://www.cepr.org .

    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.