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

Fabrizio Iacone, Luca Rossini and Andrea Viselli

The Econometrics Journal, 2026, vol. 29, issue 1, 143-166

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.

Keywords: Forecast evaluation; local diagnostics; structural instability test; change point; SPF (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:29:y:2026:i:1:p:143-166.

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