IDEAS home Printed from https://ideas.repec.org/a/ags/nareaj/159265.html
   My bibliography  Save this article

Forecasting Ex-Vessel Prices for Hard Blue Crabs in the Chesapeake Bay Region: Individual and Composite Methods

Author

Listed:
  • Hudson, Michael A.
  • Capps, Oral, Jr.

Abstract

Given the relative importance of the Chesapeake Bay hard blue crab fishery to the U.S. blue crab fishery , this paper analyzes ex-vessel prices for hard blue crabs landed in this region. The purpose is to evaluate alternative methods of forecasting ex-vessel prices for hard blue crabs in the Bay; both individual methods (trend extrapolation, econometric, and time-series) and composite methods. Examining the mean squared errors for the individual methods, the time-series model performs the best, with the econometric model slightly better than the trend extrapolation model. None of the composite methods outperforms the time-series model, although in some cases the differences are slight. Nevertheless, the time-series trend extrapolation composite outperforms all other models in identifying turning points. Generally speaking, it would appear that ex-vessel prices for hard blue crabs possess strong time dependencies , and consequently, better forecasts occur with time-series models than with econometric models.

Suggested Citation

  • Hudson, Michael A. & Capps, Oral, Jr., 1984. "Forecasting Ex-Vessel Prices for Hard Blue Crabs in the Chesapeake Bay Region: Individual and Composite Methods," Journal of the Northeastern Agricultural Economics Council, Northeastern Agricultural and Resource Economics Association, vol. 13(1), pages 1-7, April.
    • Handle: RePEc:ags:nareaj:159265
    DOI: 10.22004/ag.econ.159265
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/159265/files/Forecasting%20ex-vessel.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.159265?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
    ---><---

    References listed on IDEAS

    as
    1. Prochaska, Fred J., 1978. "Prices, Marketing Margins, And Structural Change In The King Mackerel Marketing System," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 10(1), pages 1-5, July.
    2. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    3. Prochaska, Fred J., 1978. "Prices, Marketing Margins, and Structural Change in the King Mackerel Marketing System," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 10(1), pages 105-109, July.
    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. Allen, P. Geoffrey, 1984. "A Note On Forecasting With Econometric Models," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 13(2), pages 1-4, 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. Hudson, Michael A. & Capps, Oral, Jr., 1984. "Forecasting Ex-Vessel Prices for Hard Blue Crabs in the Chesapeake Bay Region: Individual and Composite Methods," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 0(Number 1), pages 1-7, April.
    2. Garcia, Philip & Hudson, Michael A. & Waller, Mark L., 1988. "The Pricing Efficiency of Agricultural Futures Markets: An Analysis of Previous Research Results," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 20(1), pages 119-130, July.
    3. Polopolus, Leo C. & Wershow, James S., 1978. "The Incidence, Nature, And Implications Of Price-Fixing Litigation In U.S. Food Industries," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 10(1), pages 1-7, July.
    4. Hoffman, Linwood A. & Beachler, Michael, 2001. "Evaluating The Use Of Futures Prices To Forecast The Farm Level U.S. Corn Price," 2001 Annual meeting, August 5-8, Chicago, IL 20612, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    6. Covey, Ted & Bessler, David A., 1991. "The Role of Futures in Daily Forward Pricing," 1991 Annual Meeting, August 4-7, Manhattan, Kansas 271282, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    7. Henry Bryant & Michael Haigh, 2004. "Bid-ask spreads in commodity futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 923-936.
    8. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
    9. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    10. Pär Stockhammar & Pär Österholm, 2018. "Do inflation expectations granger cause inflation?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(2), pages 403-431, August.
    11. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    12. Imad Moosa & Kelly Burns, 2014. "Error correction modelling and dynamic specifications as a conduit to outperforming the random walk in exchange rate forecasting," Applied Economics, Taylor & Francis Journals, vol. 46(25), pages 3107-3118, September.
    13. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Causality and predictability in distribution: The ethanol–food price relation revisited," Energy Economics, Elsevier, vol. 42(C), pages 152-160.
    14. Zapata, Hector O. & Gil, Jose M., 1999. "Cointegration and causality in international agricultural economics research," Agricultural Economics, Blackwell, vol. 20(1), pages 1-9, January.
    15. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
    16. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    17. West, Kenneth D & McCracken, Michael W, 1998. "Regression-Based Tests of Predictive Ability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-840, November.
    18. Fullerton, Thomas M. & Kelley, Brian W., 2008. "El Paso Housing Sector Econometric Forecast Accuracy," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 385-402, April.
    19. Francisco F. R. Ramos, 1996. "Forecasting market shares using VAR and BVAR models: A comparison of their forecasting performance," Econometrics 9601003, University Library of Munich, Germany.
    20. Benedetto Molinari & Francesco Turino, 2018. "Advertising and Aggregate Consumption: A Bayesian DSGE Assessment," Economic Journal, Royal Economic Society, vol. 128(613), pages 2106-2130, August.

    More about this item

    Keywords

    Agribusiness; Demand and Price Analysis;

    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:ags:nareaj:159265. 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/nareaea.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.