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The Block-block Bootstrap: Improved Asymptotic Refinements

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Abstract

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join-point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block-block bootstrap. The asymptotic refinements of the block-block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.

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File URL: http://cowles.econ.yale.edu/P/cd/d13b/d1370.pdf
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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1370.

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Length: 36 pages
Date of creation: May 2002
Date of revision:
Publication status: Published in Econometrica (May 2004), 72(3): 673-700
Handle: RePEc:cwl:cwldpp:1370

Note: CFP 1091.
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

Related research

Keywords: Asymptotics; Block bootstrap; Block statistics; Edgeworth expansion; Extremum estimator; Generalized method of moments estimator; Maximum likelihood estimator; t statistic; Test of over-identifying restrictions;

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Cited by:
  1. Bunzel, Helle & Iglesias, Emma M., 2006. "Testing for Breaks Using Alternating Observations," Staff General Research Papers 12694, Iowa State University, Department of Economics.
  2. Lu Ji & Tong Li, 2008. "Multi-round procurement auctions with secret reserve prices: theory and evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(7), pages 897-923.
  3. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.
  4. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers 200423, Rutgers University, Department of Economics.
  5. Patrick Richard, 2007. "ARMA Sieve bootstrap unit root tests," Cahiers de recherche 07-05, Departement d'Economique de la Faculte d'administration à l'Universite de Sherbrooke, revised Jul 2009.
  6. Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Long-Run Regressions: Theory and Application to US Asset Markets," Finance 0410018, EconWPA.
  7. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages S2-S18, 09.
  8. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics.
  9. Ayadi, Mohamed A. & Kryzanowski, Lawrence, 2005. "Portfolio performance measurement using APM-free kernel models," Journal of Banking & Finance, Elsevier, vol. 29(3), pages 623-659, March.
  10. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
  11. D. S. Poskitt, 2008. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 224-250, 03.

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