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El Niño Southern Oscillation and Primary Agricultural Commodity Prices: Causal Inferences from Smooth Transition Models

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  • Ubilava, David

Abstract

Global climate anomalies affect world economies and primary commodity prices. One of the more pronounced climate anomalies is El Niño Southern Oscillation (ENSO). In this study I examine the relationship between ENSO and world commodity prices using monthly time series of the sea-surface temperature anomalies in the Nino 3.4 region, and real prices of thirty primary agricultural commodities. I apply smooth transition auoregressive (STAR) modelling techniques to assess causal inferences that could potentially be camouflaged in the linear setting. I illustrate dynamics of ENSO and commodity price behavior using generalized impulse-response functions.

Suggested Citation

  • Ubilava, David, 2013. "El Niño Southern Oscillation and Primary Agricultural Commodity Prices: Causal Inferences from Smooth Transition Models," 2013 Conference (57th), February 5-8, 2013, Sydney, Australia 152202, Australian Agricultural and Resource Economics Society.
    • Handle: RePEc:ags:aare13:152202
    DOI: 10.22004/ag.econ.152202
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    References listed on IDEAS

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    1. Allan D. Brunner, 2002. "El Niño and World Primary Commodity Prices: Warm Water or Hot Air?," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 176-183, February.
    2. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    3. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    4. Berry, Brian J.L. & Okulicz-Kozaryn, Adam, 2008. "Are there ENSO signals in the macroeconomy," Ecological Economics, Elsevier, vol. 64(3), pages 625-633, January.
    5. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    6. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
    7. Joseph V. Balagtas & Matthew T. Holt, 2009. "The Commodity Terms of Trade, Unit Roots, and Nonlinear Alternatives: A Smooth Transition Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(1), pages 87-105.
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    9. David Ubilava, 2012. "Modeling Nonlinearities in the U.S. Soybean‐to‐Corn Price Ratio: A Smooth Transition Autoregression Approach," Agribusiness, John Wiley & Sons, Ltd., vol. 28(1), pages 29-41, January.
    10. Skalin, Joakim & Teräsvirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(2), pages 202-241, April.
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    Cited by:

    1. Davinson Stev Abril‐Salcedo & Luis Fernando Melo‐Velandia & Daniel Parra‐Amado, 2020. "Nonlinear relationship between the weather phenomenon El niño and Colombian food prices," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1059-1086, October.

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    Environmental Economics and Policy; Research and Development/Tech Change/Emerging Technologies; Research Methods/ Statistical Methods;
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