Empirical Likelihood Confidence Intervals for the Mean of a Long-Range Dependent Process

24 Pages Posted: 18 Jun 2007

See all articles by Daniel Nordman

Daniel Nordman

Iowa State University - Department of Statistics

Soumendra N. Lahiri

Iowa State University - Department of Statistics; North Carolina State University

Philipp Sibbertsen

University of Hannover

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 with other confidence interval procedures involving subsampling or normal approximations.

Suggested Citation

Nordman, Daniel and Lahiri, Soumendra N. and Sibbertsen, Philipp, Empirical Likelihood Confidence Intervals for the Mean of a Long-Range Dependent Process. Journal of Time Series Analysis, Vol. 28, No. 4, pp. 576-599, July 2007. Available at SSRN: https://ssrn.com/abstract=994033 or http://dx.doi.org/10.1111/j.1467-9892.2006.00526.x

Daniel Nordman (Contact Author)

Iowa State University - Department of Statistics ( email )

Osborn Dr
Ames, IA 50011
United States

Soumendra N. Lahiri

Iowa State University - Department of Statistics ( email )

Osborn Dr
Ames, IA 50011
United States

North Carolina State University ( email )

Hillsborough Street
Raleigh, NC 27695
United States

Philipp Sibbertsen

University of Hannover ( email )

Welfengarten 1
D-30167 Hannover, 30167
Germany

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