Updating ARMA predictions for temporal aggregates

Yue Fang () and Sergio G. Koreisha
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Sergio G. Koreisha: University of Oregon, USA, Postal: University of Oregon, USA

Journal of Forecasting, 2004, vol. 23, issue 4, 275-296

Abstract: This article develops and extends previous investigations on the temporal aggregation of ARMA predications. Given a basic ARMA model for disaggregated data, two sets of predictors may be constructed for future temporal aggregates: predictions based on models utilizing aggregated data or on models constructed from disaggregated data for which forecasts are updated as soon as the new information becomes available. We show that considerable gains in efficiency based on mean-square-error-type criteria can be obtained for short-term predications when using models based on updated disaggregated data. However, as the prediction horizon increases, the gain in using updated disaggregated data diminishes substantially. In addition to theoretical results associated with forecast efficiency of ARMA models, we also illustrate our findings with two well-known time series. Copyright © 2004 John Wiley & Sons, Ltd.

Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:23:y:2004:i:4:p:275-296

DOI: 10.1002/for.913

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