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Forecasting ENSO with a smooth transition autoregressive model

David Ubilava () and C Gustav Helmers

MPRA Paper from University Library of Munich, Germany

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.

Keywords: El Nino Southern Oscillation; Out-of-Sample Forecasting; Smooth Transition Autoregression (search for similar items in EconPapers)
JEL-codes: C53 C22 Q54 (search for similar items in EconPapers)
Date: 2012-01
New Economics Papers: this item is included in nep-env and nep-for
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