Empirical likelihood confidence intervals for the mean of a long-range dependent process
AbstractThis 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 with other confidence interval procedures involving subsampling or normal approximations. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Journal of Time Series Analysis.
Volume (Year): 28 (2007)
Issue (Month): 4 (07)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782
Other versions of this item:
- 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), Leibniz UniversitÃ¤t Hannover, Wirtschaftswissenschaftliche FakultÃ¤t dp-327, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- 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 &bull Diffusion Processes
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, Elsevier, vol. 18(5), pages 405-413, December.
- Donald W. K. Andrews & Yixiao Sun, 2004.
"Adaptive Local Polynomial Whittle Estimation of Long-range Dependence,"
Econometrica, Econometric Society,
Econometric Society, vol. 72(2), pages 569-614, 03.
- Donald W.K. Andrews & Yixiao Sun, 2002. "Adaptive Local Polynomial Whittle Estimation of Long-range Dependence," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1384, Cowles Foundation for Research in Economics, Yale University.
- ANDREWS, DONALD W & Sun, Yixiao X, 2002. "Adaptive Local Polynomial Whittle Estimation of Long-Range Dependence," University of California at San Diego, Economics Working Paper Series, Department of Economics, UC San Diego qt9wt048tt, Department of Economics, UC San Diego.
- Davidson, James & Sibbertsen, Philipp, 2005.
"Tests of Bias in Log-Periodogram Regression,"
Hannover Economic Papers (HEP), Leibniz UniversitÃ¤t Hannover, Wirtschaftswissenschaftliche FakultÃ¤t
dp-317, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
- James Davidson & Philipp Sibbertsen, 2008. "Tests of Bias in Log-Periodogram Regression," Discussion Papers, Exeter University, Department of Economics 0805, Exeter University, Department of Economics.
- Nordman, Daniel J. & Lahiri, Soumendra N., 2005. "Validity Of The Sampling Window Method For Long-Range Dependent Linear Processes," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(06), pages 1087-1111, December.
- Wu, Rongning & Cao, Jiguo, 2011. "Blockwise empirical likelihood for time series of counts," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 102(3), pages 661-673, March.
- Gianfranco Adimari & Annamaria Guolo, 2010. "A note on the asymptotic behaviour of empirical likelihood statistics," Statistical Methods and Applications, Springer, Springer, vol. 19(4), pages 463-476, November.
- Gong, Yun & Peng, Liang & Qi, Yongcheng, 2010. "Smoothed jackknife empirical likelihood method for ROC curve," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 101(6), pages 1520-1531, July.
- 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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