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Comparing predictive ability in the presence of instability over a very short time

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  • Fabrizio Iacone
  • Luca Rossini
  • Andrea Viselli

Abstract

SummaryWe consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case because they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss non-parametric approaches that are more suitable to detect such situations. We illustrate these results in a Monte Carlo exercise and in a comparison of the nowcast of the quarterly US nominal GDP from the Survey of Professional Forecasters against a naive benchmark of no growth, over a period that includes the GDP instability brought by the COVID-19 crisis. We recommend that forecasters do not pool the sample, but exclude the short periods of high local instability from the evaluation exercise.

Suggested Citation

  • Fabrizio Iacone & Luca Rossini & Andrea Viselli, 2026. "Comparing predictive ability in the presence of instability over a very short time," The Econometrics Journal, Royal Economic Society, vol. 29(1), pages 143-166.
    • Handle: RePEc:oup:emjrnl:v:29:y:2026:i:1:p:143-166.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaf018
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