#2501. Tests of Equal Forecasting Accuracy for Nested Models with Estimated CCE Factors*

November 2026publication date
Proposal available till 30-05-2025
4 total number of authors per manuscript3000 $

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Journal’s subject area:
Social Sciences (miscellaneous);
Statistics and Probability;
Statistics, Probability and Uncertainty;
Economics and Econometrics;
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Abstract:
The article proposes new tests for equal predictive power of nested models when factorial regressions are used for forecasting. In contrast to the previous literature, unknown factors are not assessed on the basis of principal components, but using the general correlated effects (CCE) approach, which uses cross-over means of blocks of variables. This simplifies the interpretation of the evaluated factors, and the resulting tests are easy to implement and take into account the block data structure. The results show that the marginal distributions do not depend on the number of factors, but only on the number of means that are known.
Keywords:
Common correlated effects; Common factor model; Factor-augmented regression model; Forecasting

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