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.
| Original language | English |
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| Pages (from-to) | 1087-1111 |
| Number of pages | 25 |
| Journal | Econometric Theory |
| Volume | 21 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 2005 |