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On Bootstrap Coverage Probability with Dependent Data

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  • Janis J. Zvingelis

    (University of Iowa)

Abstract

This paper establishes the optimal bootstrap block lengths for coverage probabilities when the bootstrap is applied to covariance stationary ergodic dependent data. It is shown that the block lengths that minimize the error in coverage probabilities of one- and two-sided block bootstrap confidence intervals of normalized and studentized smooth functions of sample averages are proportional to $n^{1/4}$. The minimum error rates in coverage probabilities of one- and two-sided block bootstrap confidence intervals are of order O($n^{-3/2}$) and O($n^{-5/4}$), respectively, for normalized and studentized statistics. This constitutes a refinement over the asymptotic confidence intervals.

Suggested Citation

  • Janis J. Zvingelis, 2000. "On Bootstrap Coverage Probability with Dependent Data," Econometric Society World Congress 2000 Contributed Papers 1231, Econometric Society.
    • Handle: RePEc:ecm:wc2000:1231
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    References listed on IDEAS

    as
    1. Lahiri, Soumendra Nath, 1996. "On Edgeworth Expansion and Moving Block Bootstrap for StudentizedM-Estimators in Multiple Linear Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 56(1), pages 42-59, January.
    2. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
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