IDEAS home Printed from https://ideas.repec.org/p/ags/aaea22/322281.html
   My bibliography  Save this paper

County-level USDA Crop Progress and Condition data, machine learning, and commodity market surprises

Author

Listed:
  • Cao, An N.Q.
  • Gebrekidan, Bisrat Haile
  • Heckelei, Thomas
  • Robe, Michel A.

Abstract

No abstract is available for this item.

Suggested Citation

  • Cao, An N.Q. & Gebrekidan, Bisrat Haile & Heckelei, Thomas & Robe, Michel A., 2022. "County-level USDA Crop Progress and Condition data, machine learning, and commodity market surprises," 2024 Annual Meeting, July 28-30, New Orleans, LA 322281, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea22:322281
    DOI: 10.22004/ag.econ.322281
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/322281/files/23000.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.322281?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. Irwin, Scott & Good, Darrel, . "Opening Up the Black Box: More on the USDA Corn Yield Forecasting Methodology," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 6.
    2. John Kruse & Darnell B. Smith, 1994. "Yield Estimation Throughout the Growing Season," Center for Agricultural and Rural Development (CARD) Publications 94-tr29, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    3. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, November.
    4. Sherrick, Bruce, . "Understanding the Implied Volatility (IV) Factor in Crop Insurance," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 5.
    5. An N. Q. Cao & Michel A. Robe, 2022. "Market uncertainty and sentiment around USDA announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(2), pages 250-275, February.
    6. Kruse, John & Smith, Darnell, 1994. "Yield Estimation Throughout the Growing Season," Staff General Research Papers Archive 768, Iowa State University, Department of Economics.
    7. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    8. Dorfmann, Jeffrey & Karali, Berna, 2015. "A Nonparametric Search for Information Effects from USDA Reports," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(01), pages 1-20.
    9. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    10. Westcott, Paul C. & Jewison, Michael, 2013. "Weather Effects on Expected Corn and Soybean Yields," Agricultural Outlook Forum 2013 146846, United States Department of Agriculture, Agricultural Outlook Forum.
    11. John Kruse & Darnell B. Smith, 1994. "Yield Estimation Throughout the Growing Season," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 94-tr29, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    12. Boudoukh, Jacob & Richardson, Matthew & Shen, YuQing (Jeff) & Whitelaw, Robert F., 2007. "Do asset prices reflect fundamentals? Freshly squeezed evidence from the OJ market," Journal of Financial Economics, Elsevier, vol. 83(2), pages 397-412, February.
    13. Roll, Richard, 1984. "Orange Juice and Weather," American Economic Review, American Economic Association, vol. 74(5), pages 861-880, December.
    14. Irwin, Scott & Hubbs, Todd, . "Measuring the Accuracy of Forecasting Corn and Soybean Yield with Good and Excellent Crop Condition Ratings," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 8.
    15. Hugo Storm & Kathy Baylis & Thomas Heckelei, 2020. "Machine learning in agricultural and applied economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 849-892.
    16. Stanley C. Stevens, 1991. "Evidence for a weather persistence effect on the corn, wheat, and soybean growing season price dynamics," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 11(1), pages 81-88, February.
    17. Irwin, Scott & Good, Darrel, . "How Should We Use Within-Season Crop Condition Ratings for Corn and Soybeans?," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 7.
    18. Fama, Eugene F, et al, 1969. "The Adjustment of Stock Prices to New Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(1), pages 1-21, February.
    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. Li, Jiarui & Irwin, Scott H. & Hubbs, Todd, 2025. "Does Complexity Pay? Forecasting Corn and Soybean Yields Using Crop Condition Ratings," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Preprint), July.
    2. Berna Karali & Scott H. Irwin & Olga Isengildina‐Massa, 2020. "Supply Fundamentals and Grain Futures Price Movements," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 548-568, March.
    3. Nicholas Jorgensen & Matthew Diersen, 2014. "Forecasting Corn and Sotbean Yields with Crop Conditions," Issue Briefs 2014547, South Dakota State University, Department of Economics.
    4. Lehecka, Georg V., 2013. "The Reaction of Corn and Soybean Futures Markets to USDA Crop Progress and Condition Information," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 142491, Southern Agricultural Economics Association.
    5. Lehecka, Georg V., 2014. "The Value of USDA Crop Progress and Condition Information: Reactions of Corn and Soybean Futures Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(01), pages 1-18, April.
    6. Tannura, Michael A. & Irwin, Scott H. & Good, Darrel L., 2008. "Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt," Marketing and Outlook Research Reports 37501, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    7. Mathias S. Kruttli & Brigitte Roth Tran & Sumudu W. Watugala, 2025. "Pricing Poseidon: Extreme Weather Uncertainty and Firm Return Dynamics," Journal of Finance, American Finance Association, vol. 80(2), pages 783-832, April.
    8. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    9. An N. Q. Cao & Michel A. Robe, 2022. "Market uncertainty and sentiment around USDA announcements," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(2), pages 250-275, February.
    10. Robert F. Engle & Martin Klint Hansen & Asger Lunde, 2012. "And Now, The Rest of the News: Volatility and Firm Specific News Arrival," CREATES Research Papers 2012-56, Department of Economics and Business Economics, Aarhus University.
    11. Jin, Xuejun & Chen, Cheng & Yang, Xiaolan, 2024. "The effect of international media news on the global stock market," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 50-69.
    12. Baur, Robert Frederick, 1992. "Overreaction in futures markets," ISU General Staff Papers 1992010108000010973, Iowa State University, Department of Economics.
    13. Chou, Pin-Huang & Hsieh, Chia-Hsun & Shen, Carl Hsin-Han, 2016. "What explains the orange juice puzzle: Sentiment, smart money, or fundamentals?," Journal of Financial Markets, Elsevier, vol. 29(C), pages 47-65.
    14. Komáromi, György, 2002. "A hatékony piacok elméletének elméleti és gyakorlati relevanciája [The theoretical and practical relevance of the theory of efficient markets]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 377-395.
    15. Nobel Prize Committee, 2013. "Understanding Asset Prices," Nobel Prize in Economics documents 2013-1, Nobel Prize Committee.
    16. Nicolas Merener, 2016. "Concentrated Production and Conditional Heavy Tails in Commodity Returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(1), pages 46-65, January.
    17. Yang, Shanxiang & Liu, Zhechen & Wang, Xinjie, 2020. "News sentiment, credit spreads, and information asymmetry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    18. Barbara Rossi, 2021. "Identifying and estimating the effects of unconventional monetary policy: How to do it and what have we learned?," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 1-32.
    19. Calomiris, Charles W. & Mamaysky, Harry, 2019. "How news and its context drive risk and returns around the world," Journal of Financial Economics, Elsevier, vol. 133(2), pages 299-336.
    20. Barry K. Goodwin & Matthew T. Holt & Jeffrey P. Prestemon, 2021. "Semi-parametric models of spatial market integration," Empirical Economics, Springer, vol. 61(5), pages 2335-2361, November.

    More about this item

    Keywords

    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:aaea22:322281. 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.