Adaptive long memory testing under heteroskedasticity

David Harris, Hsein Kew

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

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 languageEnglish
Pages (from-to)755-778
Number of pages24
JournalEconometric Theory
Volume33
Issue number3
DOIs
Publication statusPublished - 1 Jun 2017

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