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

Fabrizio Iacone, Luca Rossini, Andrea Viselli

arXiv 20 May 2024 · Econometrics · publishedEconometrics Journal (2025)

arXiv:2405.11954 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We 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, as 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 and propose approaches that are more suitable to detect such situations, such as nonparametric methods (S test or MAX procedure). We illustrate these results in different Monte Carlo exercises and in evaluating the nowcast of the quarterly US nominal GDP from the Survey of Professional Forecasters (SPF) against a naive benchmark of no growth, over the period that includes the GDP instability brought by the Covid-19 crisis. We recommend that the forecaster should not pool the sample, but exclude the short periods of high local instability from the evaluation exercise.

Citation extraction

21
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Giacomini, R. and B. Rossi (2010) Forecast comparisons in unstable environments1.000113100%
2Andrews, D. W (2003) End-of-sample instability tests0.95825588%
3Giacomini, R. and H. White (2006) Tests of conditional predictive ability0.9507386%
4Diebold, F. X. and R. S. Mariano (1995) Comparing Predictive Accuracy0.92843100%
5Harvey, D. I., S. J. Leybourne, R. Sollis, and A. R. Taylor (2021) Real-time detection of regimes of predictability in the US equity premium0.87472100%
6Leadbetter, M. R., G. Lindgren, and H. Rootzén (1983) Extremes and Related Properties of Random Sequences and Processes0.58531100%
7White, H (2000) Asymptotic Theory for Econometricians0.5112250%
8Ferreira, H. and M. Scotto (2002) On the asymptotic location of high values of a stationary sequence0.51121100%
9Timmermann, A (2008) Elusive return predictability0.51121100%
10Coroneo, L. and F. Iacone (2020) Comparing predictive accuracy in small samples using fixed-smoothing asymptotics0.40511100%

Showing the top 10 of 21 scored citations.