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Media type:
E-Article
Title:
A NOVEL APPROACH TO PREDICTIVE ACCURACY TESTING IN NESTED ENVIRONMENTS
Contributor:
Pitarakis, Jean-Yves
imprint:
Cambridge University Press (CUP), 2023
Published in:Econometric Theory
Language:
English
DOI:
10.1017/s0266466623000154
ISSN:
0266-4666;
1469-4360
Origination:
Footnote:
Description:
<jats:p>We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models, leads to nuisance parameter-free Gaussian asymptotics, and is shown to remain valid under flexible assumptions that can accommodate heteroskedasticity and the presence of mixed predictors (e.g., stationary and local to unit root). A local power analysis also establishes their ability to detect departures from the null in both stationary and persistent settings. Simulations calibrated to common economic and financial applications indicate that our methods have strong power with good size control across commonly encountered sample sizes.</jats:p>