ADAPTIVE LONG MEMORY TESTING UNDER HETEROSKEDASTICITY
针对存在未知形式异方差的ARFIMA模型，提出基于非参数方差估计的加权得分检验，该检验在正态性下渐近有效，蒙特卡洛实验显示其比常规未加权检验有更大检验功效。
This paper considers adaptive hypothesis testing for the fractional differencing parameter in a parametric ARFIMA model with unconditional heteroskedasticity of unknown form. A weighted score test based on a nonparametric variance estimator is proposed and shown to be asymptotically equivalent, under the null and local alternatives, to the Neyman-Rao effective score test constructed under Gaussianity and known variance process. The proposed test is therefore asymptotically efficient under Gaussianity. The finite sample properties of the test are investigated in a Monte Carlo experiment and shown to provide potentially large power gains over the usual unweighted long memory test.