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ENSO and Soybean Prices: Correlation without Causality

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  • Letson, David
  • McCullough, B.D.

Abstract

In this paper we seek to characterize the robustness of the ENSO/soybean price relationship and to determine whether it has practical economic content. If such a meaningful relationship exists, the implications could be profound for commodity traders and for public sector investments in climate forecasting capabilities. Also, the validity of economic evaluations of climate impacts and climate forecasts based on ENSO-price independence would come into question. Our findings suggest a relationship between interannual climate and soybean prices, although we are not able to attribute the relationship to ENSO or to say that ENSO is economically important.

Suggested Citation

  • Letson, David & McCullough, B.D., 2001. "ENSO and Soybean Prices: Correlation without Causality," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 33(3), pages 513-521, December.
    • Handle: RePEc:cup:jagaec:v:33:y:2001:i:03:p:513-521_02
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    References listed on IDEAS

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    1. Granger, C. W. J., 1988. "Causality, cointegration, and control," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 551-559.
    2. Geweke, John, 1984. "Inference and causality in economic time series models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 19, pages 1101-1144, Elsevier.
    3. McCullough, B. D., 1997. "An analysis of stock market transactions data," The Quarterly Review of Economics and Finance, Elsevier, vol. 37(4), pages 887-903.
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    Citations

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    Cited by:

    1. Massimo Peri, 2017. "Climate variability and the volatility of global maize and soybean prices," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(4), pages 673-683, August.
    2. Ubilava, David & holt, Matt, 2013. "El Ni~no southern oscillation and its effects on world vegetable oil prices: assessing asymmetries using smooth transition models," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 57(2), pages 1-25.
    3. Peri, Massimo, 2015. "Cliamte Variability and Agricultural Price volatility: the case of corn and soybeans," 2015 Conference, August 9-14, 2015, Milan, Italy 212623, International Association of Agricultural Economists.
    4. Thanarak Laosuthi & David D. Selover, 2007. "Does El Nino Affect Business Cycles," Eastern Economic Journal, Eastern Economic Association, vol. 33(1), pages 21-42, Winter.
    5. David Ubilava, 2012. "El Niño, La Niña, and world coffee price dynamics," Agricultural Economics, International Association of Agricultural Economists, vol. 43(1), pages 17-26, January.
    6. Marie, Kimberly & Solis, Daniel & Thomas, Michael & Alvarez, Sergio, 2016. "Does Climate Variability Affect the Financial Sustainability of Farmers in Florida? A Causality Analysis," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229596, Southern Agricultural Economics Association.
    7. Lotanna E. Emediegwu, 2024. "Assessing the Asymmetric Effect of Global Climate Anomalies on Food Prices: Evidence from Local Prices," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(10), pages 2743-2772, October.
    8. Emediegwu, Lotanna Ernest, 2023. "Assessing the (a)symmetric effect of global climate anomalies on food prices: Evidence from local prices," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334555, Agricultural Economics Society - AES.
    9. Fujin Yi & Mengfei Zhou & Yu Yvette Zhang, 2020. "Value of Incorporating ENSO Forecast in Crop Insurance Programs," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 439-457, March.
    10. Bert, Federico E. & Satorre, Emilio H. & Toranzo, Fernando Ruiz & Podesta, Guillermo P., 2006. "Climatic information and decision-making in maize crop production systems of the Argentinean Pampas," Agricultural Systems, Elsevier, vol. 88(2-3), pages 180-204, June.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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