Forecasting ENSO with a smooth transition autoregressive model |
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| Affiliation: | 1. Department of Agricultural and Resource Economics, The University of Sydney, R.D. Watt Building, Science Road, NSW 2006, Australia;2. Hutson School of Agriculture, Murray State University, 213 South Oakley Applied Science, Murray, KY 42071, USA;1. Robotic Research Laboratory, The Center of Excellence for Mechatronics, Department of Mechatronics, School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran;2. Neurosciences Research Center, Tabriz University of Medical Sciences, Tabriz, Iran;1. School of Economics, Shanghai University of Finance and Economics, China;2. Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai, China;3. Department of Economics, The Ohio State University, 475 Arps Hall, 1945 N. High Street, Columbus, OH 43210, USA;1. Department of Chemical Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates;2. Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates;3. Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates;4. Department of Computer Science and Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates;5. Department of Mechanical Engineering, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates;6. Mechanical Engineering and Design, Aston University, School of Engineering and Applied Science, Aston Triangle, Birmingham B4 7ET, UK |
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| Abstract: | This study examines the benefits of nonlinear time series modelling to improve forecast accuracy of the El Niño Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially smooth regime-dependent dynamics of the sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, which results in the superior out-of-sample forecast performance of STAR over the linear autoregressive models. The advantage of nonlinear models is especially apparent in short- and intermediate-term forecasts. These results are of interest to researchers and policy makers in the fields of climate dynamics, agricultural production, and environmental management. |
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