IDEAS home Printed from https://ideas.repec.org/a/ags/jlaare/276505.html

Evaluating the USDA’s Net Farm Income Forecast

Author

Listed:
  • Kuethe, Todd H.
  • Hubbs, Todd
  • Sanders, Dwight R.

Abstract

The USDA produces four forecasts of net farm income for each year; these forecasts are closely monitored by decision makers across the agricultural sector. However, little is known about the performance of these forecasts. Traditional forecast evaluation tests suggest that between 1975 and 2016, the long-horizon forecasts systematically under-predicted realized values. In addition, the shorter-horizon forecast revisions overreact to new information. The findings suggest that forecast users should adjust their expectations and that the USDA may want to consider other forecast approaches to supplement current procedures.

Suggested Citation

  • Kuethe, Todd H. & Hubbs, Todd & Sanders, Dwight R., 2018. "Evaluating the USDA’s Net Farm Income Forecast," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(3), September.
    • Handle: RePEc:ags:jlaare:276505
    DOI: 10.22004/ag.econ.276505
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/276505/files/JARE%2C43.3%2CSeptember2018%2C%239%2CKuethe%2C457-468.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.276505?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. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    2. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    3. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-127, June.
    4. Bailey, DeeVon & Brorsen, B. Wade, 1998. "Trends In The Accuracy Of Usda Production Forecasts For Beef And Pork," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 23(2), pages 1-11, December.
    5. Olga Isengildina & Scott H. Irwin & Darrel L. Good, 2006. "Are Revisions to USDA Crop Production Forecasts Smoothed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 1091-1104.
    6. H. Bakhshi & G. Kapetanios & T. Yates, 2005. "Rational expectations and fixed-event forecasts: An application to UK inflation," Empirical Economics, Springer, vol. 30(3), pages 539-553, October.
    7. Lucier, Gary & Chesley, Agnes & Ahearn, Mary Clare, 1986. "Farm Income Data: A Historical Perspective," Statistical Bulletin 154593, United States Department of Agriculture, Economic Research Service.
    8. Messina, Jeffrey D. & Sinclair, Tara M. & Stekler, Herman, 2015. "What can we learn from revisions to the Greenbook forecasts?," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 54-62.
    9. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    10. Jinzhi Xiao & Chad E. Hart & Sergio H. Lence, 2017. "USDA Forecasts Of Crop Ending Stocks: How Well Have They Performed?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(2), pages 220-241.
    11. Sanders, Dwight R. & Manfredo, Mark R., 2002. "Usda Production Forecasts For Pork, Beef, And Broilers: An Evaluation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(01), pages 1-14, July.
    12. repec:ags:jrapmc:122314 is not listed on IDEAS
    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. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    2. Olga Isengildina‐Massa & Berna Karali & Todd H. Kuethe & Ani L. Katchova, 2021. "Joint Evaluation of the System of USDA's Farm Income Forecasts," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(3), pages 1140-1160, September.
    3. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    4. MacDonald, Stephen & Ash, Mark & Cooke, Bryce, 2017. "The Evolution of Inefficiency in USDA’s Forecasts of U.S. and World Soybean Markets," MPRA Paper 87545, University Library of Munich, Germany.
    5. Tianyang Zhang & Ziran Li, 2022. "Can a rational expectation storage model explain the USDA ending grain stocks forecast errors?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(3), pages 313-337, March.
    6. Dwight R. Sanders & Mark R. Manfredo, 2008. "Multiple horizons and information in USDA production forecasts," Agribusiness, John Wiley & Sons, Ltd., vol. 24(1), pages 55-66.
    7. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    8. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    9. Barbara Rossi, 2021. "Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them," Journal of Economic Literature, American Economic Association, vol. 59(4), pages 1135-1190, December.
    10. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.
    11. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    12. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    13. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, The Center for Economic Research.
    14. repec:spo:wpecon:info:hdl:2441/f4rshpf3v1umfa09lat09b1bg is not listed on IDEAS
    15. Sanders, Dwight R. & Manfredo, Mark R., 2003. "USDA Livestock Price Forecasts: A Comprehensive Evaluation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(2), pages 1-19, August.
    16. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage combination from a real-time dataset," CSEF Working Papers 274, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    17. Chiu Adrian & Wieladek Tomasz, 2013. "Is the “Great Recession” really so different from the past?," The B.E. Journal of Macroeconomics, De Gruyter, vol. 13(1), pages 1037-1084, October.
    18. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    19. Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.
    20. Michael P Clements, 2014. "Assessing the Evidence of Macro- Forecaster Herding: Forecasts of Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-12, Henley Business School, University of Reading.
    21. Kexin Ding & Ani L. Katchova, 2024. "Testing the optimality of USDA's WASDE forecasts under unknown loss," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 846-865, October.

    More about this item

    Keywords

    ;
    ;

    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:jlaare:276505. 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/waeaaea.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.