期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 197, 期 1, 页码 203-213出版社
ELSEVIER
DOI: 10.1016/j.ejor.2008.05.017
关键词
Forecasting; Decision support; Info-gaps; Robustness; Model uncertainty
资金
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [751173] Funding Source: National Science Foundation
We consider forecasting in systems whose underlying laws are uncertain. while contextual information suggests that future system properties will differ from the past, We consider linear discrete-time systems, and use a non-probabilistic info-gap model to represent uncertainty in the future transition matrix. The forecaster desires the average forecast of a specific state variable to be within a specified interval around the correct value. Traditionally, forecasting uses a model with optimal fidelity to historical data. However, since structural changes are anticipated, this is a poor strategy. Our first theorem asserts the existence, and indicates the construction, of forecasting models with sub-optimal-fidelity to historical data which ate more robust to model error than the historically optimal model. Our second theorem identifies conditions in which the probability of forecast success increases with increasing robustness to model error. The proposed methodology identifies reliable forecasting models for systems whose trajectories evolve with Knightian uncertainty for structural change over time. We consider various examples, including forecasting European Central Bank interest rates following 9/11. (C) 2008 Elsevier B.V. All rights reserved.
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