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Policymaking in Periods of Structural Changes and Structural Breaks: Rolling Windows Revisited

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  • Nikolaos Giannellis
  • Stephen G. Hall
  • Georgios P. Kouretas
  • George S. Tavlas
  • Yongli Wang

Abstract

Early studies that used rolling windows found it to be a useful forecasting technique. These studies were, by‐and‐large, based on pre‐2000 data, which were nonstationary. Subsequent work, based on stationary data from the mid‐1990s to 2020, has not been able to confirm that finding. However, this latter result may reflect the fact that there was relatively little structural instability between the mid‐1990s and 2020: The data had become stationary. Following the series of shocks of the early 2020s, this is no longer the case because the shocks produced nonstationarity in the macroeconomic data, such as inflation. Consequently, rolling windows may again be a sensible way forward. The present study assesses this conjecture.

Suggested Citation

  • Nikolaos Giannellis & Stephen G. Hall & Georgios P. Kouretas & George S. Tavlas & Yongli Wang, 2025. "Policymaking in Periods of Structural Changes and Structural Breaks: Rolling Windows Revisited," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(3), pages 851-855, April.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:3:p:851-855
    DOI: 10.1002/for.3269
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    References listed on IDEAS

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    1. Stephen G. Hall & George S. Tavlas & Yongli Wang, 2023. "Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 514-529, April.
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