Forecasting ENSO with a smooth transition autoregressive model

Authors:
David Ubilava;C. Gustav Helmers
Affiliations:
Department of Agricultural and Resource Economics, The University of Sydney, R.D. Watt Building, Science Road, NSW 2006, Australia;Hutson School of Agriculture, Murray State University, 213 South Oakley Applied Science, Murray, KY 42071, USA
Venue:
Environmental Modelling & Software
Year:
2013

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Abstract

This study examines the benefits 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 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.