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

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Author Info
Nordman, Dan Nordman
Sibbertsen, Philipp
Lahiri, Soumendra N.

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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.

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Publisher Info
Paper provided by Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover with number dp-327.

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Length: 26 pages
Date of creation: Nov 2005
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Handle: RePEc:han:dpaper:dp-327

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Related research
Keywords: blocking; confidence interval; empirical likelihood; FARIMA; long-range dependence;

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. 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(06), pages 1087-1111, December. [Downloadable!]
  2. 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, 03. [Downloadable!] (restricted)
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  3. Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 405-413, December. [Downloadable!] (restricted)
  4. Davidson, James & Sibbertsen, Philipp, 2005. "Tests of Bias in Log-Periodogram Regression," Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Universität Hannover dp-317, Universität Hannover, Wirtschaftswissenschaftliche Fakultät. [Downloadable!]
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