ADAPTIVE LONG MEMORY TESTING UNDER HETEROSKEDASTICITY
D Harris, H Kew
Econometric Theory | Cambridge University Press (CUP) | Published : 2017
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.
Awarded by Australian Research Council Discovery Grant
We are grateful to the co-editor and two referees for very helpful suggestions and comments that led to significant improvement on an earlier version of this paper. This research is supported by the Australian Research Council Discovery Grant DP1094010. Address correspondence to David Harris, Department of Econometrics and Business Statistics, Monash University, E-mail: email@example.com