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Adjusted blockwise empirical likelihood for long memory time series models

Author

Listed:
  • Feifan Jiang

    (Nanjing University)

  • Lihong Wang

    (Nanjing University)

Abstract

In this paper, we introduce an adjusted blockwise empirical likelihood (ABEL) method for long memory time series models. By dividing time series into blocks and by adding an appropriate adjustment term, we construct the ABEL ratio and the confidence interval for the mean of the process. Under mild conditions, we show that Wilks’ theorem still holds for the ABEL ratio by choosing a specific block correction factor. The Monte Carlo simulation studies are reported to assess the finite sample performance of the proposed ABEL method.

Suggested Citation

  • Feifan Jiang & Lihong Wang, 2018. "Adjusted blockwise empirical likelihood for long memory time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 319-332, June.
    • Handle: RePEc:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0403-1
    DOI: 10.1007/s10260-017-0403-1
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    References listed on IDEAS

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