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A nonlinear time series model of El Niño

Author

Listed:
  • Hall, Anthony D.

    (School of Finance and Economics, University of Technology, Sydney)

  • Skalin, Joakim

    (Dept for Economic Affairs, Ministry of Finance)

  • Teräsvirta, Timo

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

A smooth transition autoregressive model is estimated for the Southern Oscillation Index, an index commonly used as a measure of El Niño events. Using standard measures there is no indication of nonstationarity in the index. A logistic smooth transition autoregressive model describes the most turbulent periods in the data (these correspond to El Niño events) better than a linear autoregressive model. The estimated nonlinear model passes a battery of diagnostic tests. A generalised impulse response function indicates local instability, but as deterministic extrapolation from the estimated model converges, the nonlinear model may still be useful for forecasting the El Niño Southern Oscillation a few months ahead.

Suggested Citation

  • Hall, Anthony D. & Skalin, Joakim & Teräsvirta, Timo, 1998. "A nonlinear time series model of El Niño," SSE/EFI Working Paper Series in Economics and Finance 263, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0263
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    Citations

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    Cited by:

    1. Jesse B. Tack & David Ubilava, 2015. "Climate and agricultural risk: measuring the effect of ENSO on U.S. crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 46(2), pages 245-257, March.
    2. Smith, Sarah C. & Ubilava, David, 2017. "The El Niño Southern Oscillation and Economic Growth in the Developing World," Working Papers 2017-11, University of Sydney, School of Economics, revised May 2017.
    3. LanFen Chu & Michael McAleer & Chi-Chung Chen, 2009. "How Volatile is ENSO?," CIRJE F-Series CIRJE-F-635, CIRJE, Faculty of Economics, University of Tokyo.
    4. David Ubilava, 2018. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 239-263.
    5. Li, Yushu & Shukur, Ghazi, 2009. "Testing for Unit Root against LSTAR Model: Wavelet Improvement under GARCH Distortion," CAFO Working Papers 2009:6, Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics.
    6. Davinson Stev Abril Salcedo & Luis Fernando Melo Velandia & Daniel Parra Amado, 2015. "Impactos de los fenómenos climáticos sobre el precio de los alimentos en Colombia," Borradores de Economia 13648, Banco de la Republica.
    7. Andreas Röthig & Carl Chiarella, 2007. "Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(8), pages 719-737, August.
    8. 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.
    9. Lan-Fen Chu & Michael McAleer & Chi-Chung Chen, 2012. "How Volatile is ENSO for Global Greenhouse Gas Emissions and the Global Economy?," Journal of Reviews on Global Economics, Lifescience Global, vol. 1, pages 1-12.
    10. Balagtas, Joseph Valdes & Holt, Matthew T., 2006. "Unit Roots, TV-STARs, and the Commodity Terms of Trade: A Further Assessment of the Prebisch-Singer Hypothesis," 2006 Annual meeting, July 23-26, Long Beach, CA 21405, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
    12. 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.
    13. 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.
    14. 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.
    15. Ubilava, David & Helmers, Claes Gustav, 2011. "The ENSO Impact on Predicting World Cocoa Prices," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103528, Agricultural and Applied Economics Association.
    16. Po-Chin Wu & Chia-Jui Chang, 2017. "Nonlinear impacts of debt ratio and term spread on inward FDI performance persistence," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(3), pages 369-388, December.
    17. Yue-Jun Zhang & Ting Yao & Zi-Yi Wang, 2015. "The bubble process of international crude oil futures prices: empirical evidence from the STAR model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 109-125.
    18. 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.
    19. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    20. Jesse Tack & David Ubilava, 2013. "The effect of El Niño Southern Oscillation on U.S. corn production and downside risk," Climatic Change, Springer, vol. 121(4), pages 689-700, December.

    More about this item

    Keywords

    Smooth transition autoregression; Nonlinearity; Time series model; El Niño; Southern Oscillation;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

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