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The Block-block Bootstrap: Improved Asymptotic Refinements

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Author Info
Donald W.K. Andrews () (Cowles Foundation)

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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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Publisher Info
Paper provided by Cowles Foundation, 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

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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;

Other versions of this item:

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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  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. [Downloadable!]
  3. Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Long-Run Regressions: Theory and Application to US Asset Markets," Finance 0410018, EconWPA. [Downloadable!]
  4. James G. MacKinnon, 2006. "Bootstrap Methods in Econometrics," Working Papers 1028, Queen's University, Department of Economics. [Downloadable!]
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  5. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics. [Downloadable!]
  6. Patrick Richard, 2007. "Sieve bootstrap unit root tests," Cahiers de recherche 07-05, Departement d'Economique de la Faculte d'administration à l'Universite de Sherbrooke. [Downloadable!]
  7. Bunzel, Helle & Iglesias, Emma M., 2006. "Testing for Breaks Using Alternating Observations," Staff General Research Papers 12694, Iowa State University, Department of Economics. [Downloadable!]
  8. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers 200423, Rutgers University, Department of Economics. [Downloadable!]
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  9. 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. [Downloadable!]
  10. D. S. Poskitt, 2006. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes," Monash Econometrics and Business Statistics Working Papers 12/06, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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