Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory
AbstractThe 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/vm (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.
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Bibliographic InfoPaper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2002/438.
Date of creation: Sep 2002
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Edgeworth expansion; long memory; semiparametric estimation.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-11-03 (All new papers)
- NEP-ECM-2003-11-03 (Econometrics)
- NEP-ETS-2003-11-03 (Econometric Time Series)
- NEP-MFD-2003-11-03 (Microfinance)
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