ADAPTIVE LONG MEMORY TESTING UNDER HETEROSKEDASTICITY

David Harris and Hsein Kew

Econometric Theory, 2017, vol. 33, issue 3, 755-778

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:33:y:2017:i:03:p:755-778_00

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