Testing for equal predictive accuracy with strong dependence
分析了损失差分存在自相关时Diebold-Mariano检验的性质，发现依赖增强会降低检验功效，甚至导致无功效和错误拒绝原假设，提醒应用该检验前需考虑依赖特征。
We analyse the properties of the Diebold and Mariano (1995) test in the presence of autocorrelation in the loss differential. We show that the power of the Diebold and Mariano (1995) test decreases as the dependence increases, making it more difficult to obtain statistically significant evidence of superior predictive ability against less accurate benchmarks. We also find that, after a certain threshold, the test has no power, and the correct null hypothesis is spuriously rejected. These results caution us to seriously consider the loss differential’s dependence properties before applying the Diebold and Mariano (1995) test.