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Combining machine learning and market integration to improve maize price predictions in sub-Saharan Africa

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  • Anderson, Patrese
  • Baylis, Kathy
  • Davenport, Frank
  • Shukla, Shraddhanand

Abstract

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Suggested Citation

  • Anderson, Patrese & Baylis, Kathy & Davenport, Frank & Shukla, Shraddhanand, 2023. "Combining machine learning and market integration to improve maize price predictions in sub-Saharan Africa," 2023 Annual Meeting, July 23-25, Washington D.C. 335809, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea23:335809
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    File URL: https://ageconsearch.umn.edu/record/335809/files/27017.pdf
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    References listed on IDEAS

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    1. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
    2. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    3. Abdulai, Awudu, 2000. "Spatial price transmission and asymmetry in the Ghanaian maize market," Journal of Development Economics, Elsevier, vol. 63(2), pages 327-349, December.
    4. John Baffes & Bruce Gardner, 2003. "The transmission of world commodity prices to domestic markets under policy reforms in developing countries," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 6(3), pages 159-180.
    5. John Baffes & Varun Kshirsagar & Donald Mitchell, 2019. "What Drives Local Food Prices? Evidence from the Tanzanian Maize Market," The World Bank Economic Review, World Bank, vol. 33(1), pages 160-184.
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