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Adaptive forecasting in the presence of recent and ongoing structural change

Liudas Giraitis (), George Kapetanios () and Simon Price ()
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Liudas Giraitis: Queen Mary University of London
George Kapetanios: Queen Mary University of London

No 490, Bank of England working papers from Bank of England

Abstract: We consider time series forecasting in the presence of ongoing structural change where both the time-series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially weighted moving averages, known to be robust to historical structural change, are found also to be useful in the presence of ongoing structural change in the forecast period. A crucial issue is how to select the degree of downweighting, usually defined by an arbitrary tuning parameter. We make this choice data-dependent by minimising forecast mean square error, and provide a detailed theoretical analysis of our proposal. Monte Carlo results illustrate the methods. We examine their performance on 97 US macro series. Forecasts using data-based tuning of the data discount rate are shown to perform well.

Keywords: Recent and ongoing structural change; forecast combination; robust forecasts (search for similar items in EconPapers)
JEL-codes: C10 C59 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-for
Date: 2014-03-28
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Related works:
Journal Article: Adaptive forecasting in the presence of recent and ongoing structural change (2013) Downloads
Working Paper: Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change (2012) Downloads
Working Paper: Adaptive Forecasting in the Presence of Recent and Ongoing Structural Change (2012) Downloads
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