IDEAS home Printed from https://ideas.repec.org/p/ags/aaea03/22208.html
   My bibliography  Save this paper

Do Economic Restrictions Improve Forecasts?

Author

Listed:
  • Murphy, Elizabeth A.
  • Norwood, F. Bailey
  • Wohlgenant, Michael K.

Abstract

A previous study showed that imposing economic restrictions improves the forecasting ability of food demand systems, thus warranting their use even when rejected in-sample. This study attempts to determine whether this is due solely to the fact that restrictions improve degrees of freedom. Results indicate that restrictions improve forecasting ability even when not derived from economic theory, but theoretical restrictions forecast best.

Suggested Citation

  • Murphy, Elizabeth A. & Norwood, F. Bailey & Wohlgenant, Michael K., 2003. "Do Economic Restrictions Improve Forecasts?," 2003 Annual meeting, July 27-30, Montreal, Canada 22208, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea03:22208
    DOI: 10.22004/ag.econ.22208
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/22208/files/sp03mu01.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.22208?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. 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.
    2. Terry L. Kastens & Gary W. Brester, 1996. "Model Selection and Forecasting Ability of Theory-Constrained Food Demand Systems," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 301-312.
    3. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-1291, November.
    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. Andrew Muhammad & Richard L. Kilmer, 2008. "The impact of EU export subsidy reductions on U.S. dairy exports," Agribusiness, John Wiley & Sons, Ltd., vol. 24(4), pages 557-574.

    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. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 383-406, November.
    2. Norwood, F. Bailey & Lusk, Jayson L. & Brorsen, B. Wade, 2004. "Model Selection for Discrete Dependent Variables: Better Statistics for Better Steaks," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(3), pages 1-16, December.
    3. Schumacher, Sara K. & Marsh, Thomas L., 2002. "Economies Of Scale In The Greenhouse Floriculture Industry," 2002 Annual Meeting, July 28-31, 2002, Long Beach, California 36577, Western Agricultural Economics Association.
    4. Lusk, Jayson L. & Norwood, F. Bailey & Brorsen, B. Wade, 2004. "Forecasting Limited Dependent Variables: Better Statistics For Better Steaks," 2004 Annual Meeting, February 14-18, 2004, Tulsa, Oklahoma 34612, Southern Agricultural Economics Association.
    5. Norwood, F. Bailey & Roberts, Matthew C. & Lusk, Jayson L., 2002. "How Are Crop Yields Distributed?," 2002 Annual meeting, July 28-31, Long Beach, CA 19733, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. 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.
    7. Xiaosheng Mu & Luciano Pomatto & Philipp Strack & Omer Tamuz, 2021. "From Blackwell Dominance in Large Samples to Rényi Divergences and Back Again," Econometrica, Econometric Society, vol. 89(1), pages 475-506, January.
    8. 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).
    9. Henry Bryant & Michael Haigh, 2004. "Bid-ask spreads in commodity futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 14(13), pages 923-936.
    10. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
    11. 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.
    12. 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.
    13. 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.
    14. repec:ebl:ecbull:v:5:y:2007:i:16:p:1-14 is not listed on IDEAS
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    20. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
    21. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Demand and Price Analysis;

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:aaea03:22208. 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/aaeaaea.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.