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On Jackknife-After-Bootstrap Method for Dependent Data

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Abstract

In this paper, we adapt sufficient and ordered non-overlapping block bootsrap methods into jackknife-after-bootstrap (JaB) algorithm to estimate the standard error of a statistic where observations form a stationary sequence. We also extend the JaB algorithm to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illustrated by an extensive simulation study and they are applied to S&P 500 stock index data. Our findings reveal that the proposed algorithm often exhibits improved performance and, is computationally more efficient compared to conventional JaB method.

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Acknowledgements

We thank two anonymous referees for careful reading of the paper and valuable suggestions and comments, which have helped us produce a significantly better paper. We are also grateful to the Editor for offering the opportunity to publish our work.

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Authors and Affiliations

  1. Department of Statistics, Bartin University, Bartin, Turkey

    Ufuk Beyaztas & Beste H. Beyaztas

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  1. Ufuk Beyaztas
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  2. Beste H. Beyaztas
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Correspondence to Ufuk Beyaztas.

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Beyaztas, U., Beyaztas, B.H. On Jackknife-After-Bootstrap Method for Dependent Data. Comput Econ 53, 1613–1632 (2019). https://doi.org/10.1007/s10614-018-9827-4

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