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Forecasting ENSO with a smooth transition autoregressive model


  • Ubilava, David
  • Helmers, C Gustav


This study examines the benets of nonlinear time series modelling to improve forecast accuracy of the El Nino Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially regime-dependent dynamics of sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, resulting in superior out-of-sample forecast performance of STAR over the linear autoregressive models. The advantage of nonlinear models is especially apparent in the short- and intermediate-term forecasts. These results are of interest to researchers and policy makers in the elds of climate dynamics, agricultural production, and environmental management.

Suggested Citation

  • Ubilava, David & Helmers, C Gustav, 2012. "Forecasting ENSO with a smooth transition autoregressive model," MPRA Paper 36890, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:36890

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    References listed on IDEAS

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    4. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
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    7. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    9. Sarantis, Nicholas, 1999. "Modeling non-linearities in real effective exchange rates," Journal of International Money and Finance, Elsevier, vol. 18(1), pages 27-45, January.
    10. Swanson, Norman R., 1998. "Money and output viewed through a rolling window," Journal of Monetary Economics, Elsevier, vol. 41(3), pages 455-474, May.
    11. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    13. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
    14. 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.
    15. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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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. 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.
    3. Iván Cárdenas-Gallo & Raha Akhavan-Tabatabaei & Mauricio Sánchez-Silva & Emilio Bastidas-Arteaga, 2016. "A Markov regime-switching framework to forecast El Niño Southern Oscillation patterns," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 829-843, March.
    4. repec:eee:wdevel:v:96:y:2017:i:c:p:490-502 is not listed on IDEAS
    5. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    6. 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.
    7. Ubilava, David, 2016. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," Working Papers 2016-10, University of Sydney, School of Economics.
    8. David Ubilava, 2014. "El Niño Southern Oscillation and the fishmeal–soya bean meal price ratio: regime-dependent dynamics revisited," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 41(4), pages 583-604.

    More about this item


    El Nino Southern Oscillation; Out-of-Sample Forecasting; Smooth Transition Autoregression;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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