TY - JOUR
T1 - Long-term forecasting of El Niño events via dynamic factor simulations
AU - Li, Mengheng
AU - Koopman, Siem Jan
AU - Lit, Rutger
AU - Petrova, Desislava
PY - 2020/1
Y1 - 2020/1
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
KW - Long-term forecast
KW - Multivariate time series
KW - Simulation smoothing
KW - Unobserved components
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U2 - 10.1016/j.jeconom.2019.05.004
DO - 10.1016/j.jeconom.2019.05.004
M3 - Article
AN - SCOPUS:85069903803
VL - 214
SP - 46
EP - 66
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -