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Forecasting ENSO with a smooth transition autoregressive model

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  • Ubilava, David
  • Helmers, C Gustav

Abstract

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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    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. Ubilava, David, 2017. "The ENSO Effect and Asymmetries in Wheat Price Dynamics," World Development, Elsevier, vol. 96(C), pages 490-502.
    5. 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.
    6. Tianhao Wang & Yingcun Xia, 2015. "Whittle Likelihood Estimation of Nonlinear Autoregressive Models With Moving Average Residuals," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1083-1099, September.
    7. 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.
    8. Villoria, Nelson B. & Delgado, Michael, 2017. "Worldwide Crop Supply Responses to El Niño Southern Oscillation," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258564, Agricultural and Applied Economics Association.
    9. 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.
    10. 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.

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    More about this item

    Keywords

    El Nino Southern Oscillation; Out-of-Sample Forecasting; Smooth Transition Autoregression;
    All these keywords.

    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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