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Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory

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  • Liudas Giraitis
  • Peter M Robinson

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/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.

Suggested Citation

  • Liudas Giraitis & Peter M Robinson, 2002. "Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory," STICERD - Econometrics Paper Series 438, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:438
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    References listed on IDEAS

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    1. Donald W. K. Andrews & Patrik Guggenberger, 2003. "A Bias--Reduced Log--Periodogram Regression Estimator for the Long--Memory Parameter," Econometrica, Econometric Society, vol. 71(2), pages 675-712, March.
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    11. Liudas Giraitis & Peter M Robinson & Alexander Samarov, 2000. "Adaptive Semiparametric Estimation of the Memory Parameter - (Now published with revised title, Adaptive Rate-Optimal Estimation of the Memory Parameter, in Journal of Multivariate Analysis, 72 (2000)," STICERD - Econometrics Paper Series 379, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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