A Nonparametric Distribution-Free Test for Serial Independence of Errors
提出一种检验位置尺度模型中不可观测误差序列独立性的方法，基于残差的经验过程构造检验统计量，渐近无分布且能检测任意滞后的成对依赖，蒙特卡洛模拟验证了理论结果。
In this article, we propose a test for the serial independence of unobservable errors in location-scale models. We consider a Hoeffding–Blum–Kiefer–Rosenblat type empirical process applied to residuals, and show that under certain conditions it converges weakly to the same limit as the process based on true errors. We then consider a generalized spectral test applied to estimated residuals, and get a test that is asymptotically distribution-free and powerful against any type of pairwise dependence at all lags. Some Monte Carlo simulations validate our theoretical findings.