Alessandro Morico, Ovidijus Stauskas
arXiv 11 Apr 2025 · Econometrics
arXiv:2504.08455 · PDF · DOI · OpenAlex · Extracted main text
We present four novel tests of equal predictive accuracy and encompassing for out-of-sample forecasts based on factor-augmented regression. We extend the work of Pitarakis (2023a,b) to develop the inferential theory of predictive regressions with generated regressors which are estimated by using Common Correlated Effects (henceforth CCE) - a technique that utilizes cross-sectional averages of grouped series. It is particularly useful since large datasets of such structure are becoming increasingly popular. Under our framework, CCE-based tests are asymptotically normal and robust to overspecification of the number of factors, which is in stark contrast to existing methodologies in the CCE context. Our tests are highly applicable in practice as they accommodate for different predictor types (e.g., stationary and highly persistent factors), and remain invariant to the location of structural breaks in loadings. Extensive Monte Carlo simulations indicate that our tests exhibit excellent local power properties. Finally, we apply our tests to a novel EA-MD-QD dataset by Barigozzi et al. (2024b), which covers Euro Area as a whole and primary member countries. We demonstrate that CCE factors offer a substantial predictive power even under varying data persistence and structural breaks.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Barigozzi, Matteo and Lissona, Claudio and Tonni, Lorenzo (2024) Large datasets for the euro area and its member countries and the dynamic effects of the common monetary policy | 1.000 | 10 | 4 | 100% |
| 2 | Bai, Jushan and Ng, Serena (2002) Determining the number of factors in approximate factor models | 1.000 | 5 | 4 | 100% |
| 3 | Margaritella, Luca and Stauskas, Ovidijus (2024) New Tests of Equal Forecast Accuracy for Factor-Augmented Regressions with Weaker Loadings self | 0.965 | 10 | 5 | 90% |
| 4 | Clark, Todd E and McCracken, Michael W (2001) Tests of equal forecast accuracy and encompassing for nested models | 0.956 | 8 | 6 | 88% |
| 5 | Pitarakis, Jean-Yves (2023) Direct Multi-Step Forecast based Comparison of Nested Models via an Encompassing Test | 0.928 | 25 | 7 | 80% |
| 6 | Gon calves, S\'ilvia and McCracken, Michael W and Perron, Benoit (2017) Tests of equal accuracy for nested models with estimated factors | 0.928 | 4 | 3 | 100% |
| 7 | McCracken, Michael W and Ng, Serena (2016) FRED-MD: A monthly database for macroeconomic research | 0.928 | 4 | 3 | 100% |
| 8 | Pitarakis, Jean-Yves (2025) A novel approach to predictive accuracy testing in nested environments | 0.918 | 22 | 7 | 77% |
| 9 | Ahn, Seung C and Horenstein, Alex R (2013) Eigenvalue ratio test for the number of factors | 0.874 | 6 | 4 | 67% |
| 10 | Chen, Liang and Dolado, Juan J. and Gonzalo, Jes \'s (2014) Detecting big structural breaks in large factor models | 0.874 | 6 | 3 | 67% |
Showing the top 10 of 59 scored citations.