A new self-normalized forecast comparison test

Haiqi Li, Ni Zhang and Jin Zhou

Economics Letters, 2025, vol. 256, issue C

Abstract: 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.

Keywords: Diebold–Mariano test; Functional central limit theorem; Predictive accuracy; Self-normalization (search for similar items in EconPapers)
JEL-codes: C12 C22 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:256:y:2025:i:c:s0165176525004835

DOI: 10.1016/j.econlet.2025.112646

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