What does this research mean for the field?

Four newly developed tests of equal predictive accuracy for factor-augmented regressions exhibit robust theoretical properties—including asymptotic normality and invariance to structural breaks—and demonstrate that macroeconomic factors offer significant predictive power for the Euro Area. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to develop robust tests for evaluating predictive accuracy in factor-augmented regressions.

April 18, 2026

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

Puntos clave

The research aims to develop robust tests for evaluating predictive accuracy in factor-augmented regressions.
Developed four novel tests for equal predictive accuracy and encompassing
Estimated factors using cross-section averages of grouped series
Conducted simulations to assess power properties of the tests
Applied tests to the EA-MD-QD dataset covering Euro Area countries
Tests demonstrate good local power properties
Factors are shown to possess predictive power in the EA-MD-QD dataset

Resumen

ABSTRACT 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.

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