Speaker
Description
The El Niño/Southern Oscillation (ENSO) phenomenon is one of the most important sources of interannual climate variability. This paper focuses on the prediction of the sea surface temperatures in the four Niño regions, which represent the oceanic manifestation of ENSO. The series are the components of a large-dimensional dynamic system constituted by 15 time series that include the atmospheric component (sea level pressure in Darwin and Tahiti), trade and zonal winds anomalies, as well as series related to the ocean heat content. We propose a prediction method based on a novel regularized multivariate Durbin-Levinson algorithm, which performs the projection of the series into its past, avoiding the curse of dimensionality. The regularization concerns both the lag and the cross-sectional dimensions: we taper and threshold the partial canonical correlations computed on a mixture sample cross-covariance matrix that shrinks the traditional estimator towards a seemingly unrelated system. Using a rolling forecast experiment, we show that the cyclical properties of the series are essential for multi-step ahead forecasting and the use of cross--sectional information leads to significant forecasting gains with respect to traditional vector autoregressive modelling.
45 min plus 5 min discussion
