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Empirical likelihood confidence intervals for the mean of a long-range dependent process

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Listed:
  • Nordman, Dan Nordman
  • Sibbertsen, Philipp
  • Lahiri, Soumendra N.

Abstract

This paper considers blockwise empirical likelihood for real-valued linear time processes which may exhibit either short- or long-range dependence. Empirical likelihood approaches intended for weakly dependent time series can fail in the presence of strong dependence. However, a modified blockwise method is proposed for confidence interval estimation of the process mean, which is valid for various dependence structures including long-range dependence. The finite-sample performance of the method is evaluated through a simulation study and compared to other confidence interval procedures involving subsampling or normal approximations.

Suggested Citation

  • Nordman, Dan Nordman & Sibbertsen, Philipp & Lahiri, Soumendra N., 2005. "Empirical likelihood confidence intervals for the mean of a long-range dependent process," Hannover Economic Papers (HEP) dp-327, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-327
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    References listed on IDEAS

    as
    1. Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 405-413, December.
    2. Clifford M. Hurvich & Rohit Deo & Julia Brodsky, 1998. "The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 19-46, January.
    3. Nordman, Daniel J. & Lahiri, Soumendra N., 2005. "Validity Of The Sampling Window Method For Long-Range Dependent Linear Processes," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1087-1111, December.
    4. Donald W. K. Andrews & Yixiao Sun, 2004. "Adaptive Local Polynomial Whittle Estimation of Long-range Dependence," Econometrica, Econometric Society, vol. 72(2), pages 569-614, March.
    5. Davidson, James & Sibbertsen, Philipp, 2009. "Tests of bias in log-periodogram regression," Economics Letters, Elsevier, vol. 102(2), pages 83-86, February.
    6. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    Citations

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    Cited by:

    1. Gong, Yun & Peng, Liang & Qi, Yongcheng, 2010. "Smoothed jackknife empirical likelihood method for ROC curve," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1520-1531, July.
    2. Wu, Rongning & Cao, Jiguo, 2011. "Blockwise empirical likelihood for time series of counts," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 661-673, March.
    3. Gianfranco Adimari & Annamaria Guolo, 2010. "A note on the asymptotic behaviour of empirical likelihood statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 463-476, November.
    4. 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.
    5. Chioneso S. Marange & Yongsong Qin & Raymond T. Chiruka & Jesca M. Batidzirai, 2023. "A Blockwise Empirical Likelihood Test for Gaussianity in Stationary Autoregressive Processes," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    6. Li, Minqiang & Peng, Liang & Qi, Yongcheng, 2011. "Reduce computation in profile empirical likelihood method," MPRA Paper 33744, University Library of Munich, Germany.

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    More about this item

    Keywords

    blocking; confidence interval; empirical likelihood; FARIMA; long-range dependence;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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