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

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

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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
JournalJournal of Econometrics
DOIs
Publication statusAccepted/In press - 1 Jan 2019

Fingerprint

Dynamic factor
Simulation
Long-term forecasting
Prediction error
Long-term forecast
Empirical evidence
Econometric forecasting
Forecasting method
Forecasting performance
Categorical variables
Dynamic factor model
Time series models

Keywords

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

Cite this

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title = "Long-term forecasting of El Ni{\~n}o events via dynamic factor simulations",
abstract = "We propose a new forecasting procedure which particularly explores opportunities for improving the precision of medium and long-term forecasts of the Ni{\~n}o3.4 time series that is linked with the well-known El Ni{\~n}o 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 Ni{\~n}o3.4 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 Ni{\~n}o 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.",
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Long-term forecasting of El Niño events via dynamic factor simulations. / Li, Mengheng; Koopman, Siem Jan; Lit, Rutger; Petrova, Desislava.

In: Journal of Econometrics, 01.01.2019.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - Li, Mengheng

AU - Koopman, Siem Jan

AU - Lit, Rutger

AU - Petrova, Desislava

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N2 - 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.

AB - 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.

KW - Climate econometrics

KW - Dynamic models

KW - Factor models

KW - Kalman filter

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KW - Multivariate time series

KW - Simulation smoothing

KW - Unobserved components

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