A new self-normalized forecast comparison test - Li, Haiqi; Zhang, Ni; Zhou, Jin (Economics letters, 2025)
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.