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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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 36890.

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Date of creation: Jan 2012
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Handle: RePEc:pra:mprapa:36890

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Keywords: El Nino Southern Oscillation; Out-of-Sample Forecasting; Smooth Transition Autoregression;

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Cited by:
  1. 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.

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