Giraitis, Liudas and Robinson, Peter (2002) Edgeworth expansions for semiparametric Whittle estimation of long memory. Econometrics; EM/2002/438 (EM/02/438). Suntory and Toyota International Centres for Economics and Related Disciplines, London.
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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.
| Item Type: | Monograph (Discussion Paper) |
|---|---|
| Official URL: | http://sticerd.lse.ac.uk |
| Additional Information: | © 2002 the authors |
| Divisions: | Economics STICERD |
| Subjects: | H Social Sciences > HB Economic Theory |
| JEL classification: | C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C21 - Cross-Sectional Models; Spatial Models; Treatment Effect Models |
| Date Deposited: | 27 Apr 2007 |
| Last Modified: | 11 Dec 2024 18:31 |
| URI: | http://eprints.lse.ac.uk/id/eprint/2130 |
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