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Adaptive forecasting in the presence of recent and ongoing structural change

Author

Listed:
  • Liudas Giraitis

    (Queen Mary University of London)

  • George Kapetanios

    (Queen Mary University of London)

  • Simon Price

    (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.

Suggested Citation

  • Liudas Giraitis & George Kapetanios & Simon Price, 2014. "Adaptive forecasting in the presence of recent and ongoing structural change," Bank of England working papers 490, Bank of England.
    • Handle: RePEc:boe:boeewp:0490
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Recent and ongoing structural change; forecast combination; robust forecasts;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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