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On The Jackknife-After-Bootstrap Method For Dependent Data And Its Consistency Properties

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  • Lahiri, S.N.

Abstract

Motivated by Efron (1992, Journal of the Royal Statistical Society, Series B 54, 83–111), this paper proposes a version of the moving block jackknife as a method of estimating standard errors of block-bootstrap estimators under dependence. As in the case of independent and identically distributed (i.i.d.) observations, the proposed method merely regroups the values of a statistic from different bootstrap replicates to produce an estimate of its standard error. Consistency of the resulting jackknife standard error estimator is proved for block-bootstrap estimators of the bias and the variance of a large class of statistics. Consistency of Efron's method is also established in similar problems for i.i.d. data.

Suggested Citation

  • Lahiri, S.N., 2002. "On The Jackknife-After-Bootstrap Method For Dependent Data And Its Consistency Properties," Econometric Theory, Cambridge University Press, vol. 18(1), pages 79-98, February.
    • Handle: RePEc:cup:etheor:v:18:y:2002:i:01:p:79-98_18
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    Cited by:

    1. Andrés Alonso & Daniel Peña & Juan Romo, 2003. "Resampling time series using missing values techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 765-796, December.
    2. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    3. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2004. "A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1," NBER Working Papers 10447, National Bureau of Economic Research, Inc.
    4. Brière, Marie & Simar, Léopold & Szafarz, Ariane & Vanhems, Anne, 2023. "Sensitivity to measurement errors of the distance to the efficient frontier," LIDAM Discussion Papers ISBA 2023017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Ufuk Beyaztas & Beste H. Beyaztas, 2019. "On Jackknife-After-Bootstrap Method for Dependent Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1613-1632, April.
    6. Dragan Radulović, 2009. "Another look at the disjoint blocks bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 195-212, May.

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