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Why Aggregate Long Memory Time Series?

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Author Info

  • Leonardo Rocha Souza

Abstract

This article shows that, for large samples, temporally aggregating a true long memory time series (in order to get an improved estimator) may make little or no sense, as the practitioner can get virtually the same estimates as those from the aggregated series by choosing the appropriate bandwidths on the original one, provided some fairly general conditions apply. Besides, the practitioner has a wider choice of bandwidths than she has of aggregating levels. However, these results apply only to two specific and commonly used estimators, and do not apply to the aggregation procedure undertaken to compute the realized volatility. Also, aggregating a time series in order to test true versus spurious long memory (as in Ohanissian et al., 2008) is a relevant issue, particularly regarding stochastic and/or realized volatility, as many nonlinear processes display spurious long memory where the above result does not apply.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930701873408
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 27 (2008)
Issue (Month): 1-3 ()
Pages: 298-316

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Handle: RePEc:taf:emetrv:v:27:y:2008:i:1-3:p:298-316

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Related research

Keywords: Bandwidth; Long memory; Spectrum; Temporal aggregation;

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Cited by:
  1. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print peer-00815563, HAL.
  2. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory and Fractional Integration in High Frequency Data on the US Dollar / British Pound Spot Exchange Rate," Discussion Papers of DIW Berlin 1294, DIW Berlin, German Institute for Economic Research.

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