Abstract
This study proposes a modified Diebold–Mariano (DM) test for equal forecast accuracy with clustered dependence. A novel consistent long-run variance estimator is developed to account for the clustered dependence. The modified DM test statistic asymptotically follows a normal distribution. The moving block bootstrap is employed to improve the size and power performance of the newly proposed test. A Monte Carlo simulation shows that the modified DM test has a better finite sample performance than the conventional DM test.
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