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the Block-Block Bootstrap: Improved Asymptotic Refinements

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  • Donald W. K. Andrews

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. Copyright The Econometric Society 2004.

Suggested Citation

  • Donald W. K. Andrews, 2004. "the Block-Block Bootstrap: Improved Asymptotic Refinements," Econometrica, Econometric Society, vol. 72(3), pages 673-700, May.
  • Handle: RePEc:ecm:emetrp:v:72:y:2004:i:3:p:673-700
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    File URL: http://hdl.handle.net/10.1111/j.1468-0262.2004.00509.x
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    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Testing Many Moment Inequalities," CeMMAP working papers CWP65/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Andrey Rafalson, 2012. "Bootstrap inference about integrated volatility (in Russian)," Quantile, Quantile, issue 10, pages 91-108, December.
    3. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    4. 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.
    5. Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Long-Run Regressions: Theory and Application to US Asset Markets," Finance 0410018, University Library of Munich, Germany.
    6. Reiner Franke & Frank Westerhoff, 2016. "Why a simple herding model may generate the stylized facts of daily returns: explanation and estimation," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 11(1), pages 1-34, April.
    7. 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.
    8. 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.
    9. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.
    10. 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, March.
    11. 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.
    12. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages 2-18, September.
    13. Bunzel, Helle & Iglesias, Emma M., 2006. "Testing for Breaks Using Alternating Observations," Staff General Research Papers Archive 12694, Iowa State University, Department of Economics.
    14. Patrick Richard, 2007. "ARMA Sieve bootstrap unit root tests," Cahiers de recherche 07-05, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke, revised Jul 2009.
    15. Li, Jing, 2006. "The block bootstrap test of Hausman's exogeneity in the presence of serial correlation," Economics Letters, Elsevier, vol. 91(1), pages 76-82, April.
    16. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics.
    17. Radu T. Pruna & Maria Polukarov & Nicholas R. Jennings, 2016. "A new structural stochastic volatility model of asset pricing and its stylized facts," Papers 1604.08824, arXiv.org.
    18. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    19. repec:bla:jtsera:v:38:y:2017:i:3:p:479-504 is not listed on IDEAS

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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