Long-term forecasting of El Niño events via dynamic factor simulations

Mengheng Li, Siem Jan Koopman*, Rutger Lit, Desislava Petrova

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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We propose a new forecasting procedure which particularly explores opportunities for improving the precision of medium and long-term forecasts of the Niño3.4 time series that is linked with the well-known El Niño phenomenon. This important climatic time series is subject to an intricate dynamic structure and is interrelated to other climatological variables. The procedure consists of three steps. First, a univariate time series model is considered for producing prediction errors. Second, signal paths of the prediction errors are simulated via a dynamic factor model for the errors and explanatory variables. From these simulated errors, ensemble time series for Niño3.4 are constructed. Third, forecasts are generated from the ensemble time series and their sample average is our final forecast. As part of these dynamic factor simulations, we also obtain the forecast of the El Niño event which is a categorical variable. We present empirical evidence that our procedure can be superior in its forecasting performance when compared to other econometric forecasting methods.

Original languageEnglish
Pages (from-to)46-66
Number of pages21
JournalJournal of Econometrics
Issue number1
Early online date31 Jul 2019
Publication statusPublished - Jan 2020


Koopman acknowledges support from CREATES, Center for Research in Econometric Analysis of Time Series (DNRF78) at Aarhus University, Denmark , and funded by the Danish National Research Foundation . Appendix A

FundersFunder number
Center for Research in Econometric Analysis of Time SeriesDNRF78
Aarhus Universitet
Danmarks Grundforskningsfond


    • Climate econometrics
    • Dynamic models
    • Factor models
    • Kalman filter
    • Long-term forecast
    • Multivariate time series
    • Simulation smoothing
    • Unobserved components


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