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Recent developments in bootstrap methods for dependent data

Author

Listed:
  • Giuseppe Cavaliere
  • Dimitris N. Politis
  • Anders Rahbek
  • Karl B. Gregory
  • Soumendra N. Lahiri
  • Daniel J. Nordman

Abstract

type="main" xml:id="jtsa12117-abs-0001"> Unlike with independent data, smoothed bootstraps have received little consideration for time series, although data smoothing within resampling can improve bootstrap approximations, especially when target distributions depend on smooth population quantities (e.g., marginal densities). For approximating a broad class statistics formulated through statistical functionals (e.g., LL-estimators, and sample quantiles), we propose a smooth bootstrap by modifying a state-of-the-art (extended) tapered block bootstrap (TBB). Our treatment shows that the smooth TBB applies to time series inference cases not formally established with other TBB versions. Simulations also indicate that smoothing enhances the block bootstrap.

Suggested Citation

  • Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Karl B. Gregory & Soumendra N. Lahiri & Daniel J. Nordman, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 442-461, May.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:3:p:442-461
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    File URL: http://hdl.handle.net/10.1111/jtsa.12117
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    References listed on IDEAS

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    Cited by:

    1. Germán Aneiros & Paula Raña & Philippe Vieu & Juan Vilar, 2018. "Bootstrap in semi-functional partial linear regression under dependence," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 659-679, September.

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