Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy

Francisco Cribari-Neto, Vinícius T. Scher and Fábio M. Bayer

International Journal of Forecasting, 2023, vol. 39, issue 1, 98-109

Abstract: We evaluate the accuracy of model selection and associated short-run forecasts using beta autoregressive moving average (βARMA) models, which are tailored for modeling and forecasting time series that assume values in the standard unit interval, (0,1), such as rates, proportions, and concentration indices. Different model selection strategies are considered, including one that uses data resampling. Simulation evidence on the frequency of correct model selection favors the bootstrap-based approach. Model selection based on information criteria outperforms that based on forecasting accuracy measures. A forecasting analysis of the proportion of stored hydroelectric energy in South Brazil is presented and discussed. The empirical evidence shows that model selection based on data resampling typically leads to more accurate out-of-sample forecasts.

Keywords: βARMA model; Bootstrap; Forecasting; Information criterion; Model selection; Stored hydroelectric energy (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:1:p:98-109

DOI: 10.1016/j.ijforecast.2021.09.004

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