Giraitis, L. and Robinson, P.M. (2003) Edgeworth expansions for semiparametric Whittle estimation of long memory. Annals of Statistics, 31 (4). pp. 1325-1375. ISSN 0090-5364
Abstract
The semiparametric local Whittle or Gaussian estimate of the long memory parameter is known to have especially nice limiting distributional properties, being asymptotically normal with a limiting variance that is completely known. However in moderate samples the normal approximation may not be very good, so we consider a refined, Edgeworth, approximation, for both a tapered estimate, and the original untapered one. For the tapered estimate, our higher-order correction involves two terms, one of order 1/√m (where m is the bandwidth number in the estimation), the other a bias term, which increases in m; depending on the relative magnitude of the terms, one or the other may dominate, or they may balance. For the untapered estimate we obtain an expansion in which, for m increasing fast enough, the correction consists only of a bias term. We discuss applications of our expansions to improved statistical inference and bandwidth choice. We assume Gaussianity, but in other respects our assumptions seem mild.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | York RAE Import |
Date Deposited: | 28 Aug 2009 10:43 |
Last Modified: | 28 Aug 2009 10:43 |
Published Version: | http://dx.doi.org/10.1214/aos/1059655915 |
Status: | Published |
Publisher: | Institute of Mathematical Statistics |
Identification Number: | 10.1214/aos/1059655915 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:5614 |