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VALIDITY OF THE SAMPLING WINDOW METHOD FOR LONG-RANGE DEPENDENT LINEAR PROCESSES

Published online by Cambridge University Press:  23 September 2005

Daniel J. Nordman  and
Soumendra N. Lahiri
Daniel J. Nordman
Affiliation:
Iowa State University
Soumendra N. Lahiri
Affiliation:
Iowa State University
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Abstract

The sampling window method of Hall, Jing, and Lahiri (1998, Statistica Sinica 8, 1189–1204) is known to consistently estimate the distribution of the sample mean for a class of long-range dependent processes, generated by transformations of Gaussian time series. This paper shows that the same nonparametric subsampling method is also valid for an entirely different category of long-range dependent series that are linear with possibly non-Gaussian innovations. For these strongly dependent time processes, subsampling confidence intervals allow inference on the process mean without knowledge of the underlying innovation distribution or the long-memory parameter. The finite-sample coverage accuracy of the subsampling method is examined through a numerical study.The authors thank two referees for comments and suggestions that greatly improved an earlier draft of the paper. This research was partially supported by U.S. National Science Foundation grants DMS 00-72571 and DMS 03-06574 and by the Deutsche Forschungsgemeinschaft (SFB 475).

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Type
Research Article
Information
Econometric Theory , Volume 21 , Issue 6 , December 2005 , pp. 1087 - 1111
Copyright
© 2005 Cambridge University Press

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