Forecasting ENSO with a smooth transition autoregressive model
David Ubilava () and
C Gustav Helmers
MPRA Paper from University Library of Munich, Germany
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)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:36890
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