Abstract
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.
Original language | English |
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Pages (from-to) | 755-778 |
Number of pages | 24 |
Journal | Econometric Theory |
Volume | 33 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Jun 2017 |