New Tests of Equal Forecast Accuracy for Factor-Augmented Regressions with Weaker Loadings

2024/09/30 by Luca Margaritella, Margaritella, Luca, Ovidijus Stauskas +1
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Econometrics (econ.EM) #FOS: Economics and business #Forecasting Techniques and Applications

paper · pdf · doi:10.48550/arxiv.2409.20415

openalex publication_date 2024/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We provide the theoretical foundation for the recent tests of equal forecast accuracy and encompassing by Pitarakis (2023) and Pitarakis (2025), when the competing forecast specification is that of a factor-augmented regression model. This should be of interest for practitioners, as there is no theory justifying the use of these simple and powerful tests in such context. In pursuit of this, we employ a novel theory to incorporate the empirically well-documented fact of homogeneously/heterogeneously weak factor loadings, and track their effect on the forecast comparison problem.

Related

• Comparing Predictive Accuracy
• Robust Tests for Factor-Augmented Regressions with an Application to the novel EA-MD-QD Dataset
• Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts
• Tests for equal forecast accuracy under heteroskedasticity
• On Testing Equal Conditional Predictive Ability Under Measurement Error