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Robust Tests of Forecast Accuracy for Factor‐Augmented Regressions With an Application to the Novel EA‐MD‐QD Dataset

Bibliographic Data

ID21653451
AuthorsAlessandro Morico (University of Bologna Bologna Italy), Ovidijus Stauskas (0000-0002-5326-8794, BI Norwegian Business School Oslo Norway)
Year2026
Publication date2026-04-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.70056
OpenAlexW7154627954
LanguageEN
References cited53

We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural breaks in the loadings. Simulations reveal good local power properties of our tests. We apply them to the novel EA‐MD‐QD dataset by Barigozzi et al. (2024b)—which covers the Euro Area as a whole and its primary member countries—and show that factors offer predictive power

Predictive power · Regression · Time series · Financial Risk and Volatility Modeling · Italy: Economic History and Contemporary Issues · Monetary Policy and Economic Impact

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