A new self-normalized forecast comparison test

This study develops a novel self-normalized Diebold–Mariano (DM) test for evaluating equal forecast accuracy. The proposed test offers several distinct advantages: it avoids bandwidth selection and bypasses direct estimation of long-run variances, both of which are typically required in conventional...

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Vydané v:	Economics letters Ročník 256; s. 112646
Hlavní autori:	Li, Haiqi, Zhang, Ni, Zhou, Jin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2025
Predmet:	Diebold–Mariano test Functional central limit theorem Predictive accuracy Self-normalization C22 Predictive accuracy C12 Diebold–Mariano test Functional central limit theorem Self-normalization
ISSN:	0165-1765
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Shrnutí:	This study develops a novel self-normalized Diebold–Mariano (DM) test for evaluating equal forecast accuracy. The proposed test offers several distinct advantages: it avoids bandwidth selection and bypasses direct estimation of long-run variances, both of which are typically required in conventional forecast accuracy testing approaches. Under relatively mild regularity conditions, we show that the asymptotic null distribution of the self-normalized DM test statistics is pivotal, with corresponding critical values tabulated through simulations. Comprehensive Monte Carlo simulations confirm that our self-normalized DM test has superior finite-sample performances compared to the original and the existing modified DM tests. •Proposes a novel self-normalized DM test for evaluating equal forecast accuracy.•Derives pivotal asymptotic null distribution with tabulated critical values.•Outperforms original and the existing modified DM tests in simulations.
ISSN:	0165-1765
DOI:	10.1016/j.econlet.2025.112646