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Abstract Details
Activity Number:
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273
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #303521 |
Title:
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Subsampling Inference for the Autocovariances of Heavy-Tailed Long-Memory Time Series
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Author(s):
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Tucker McElroy*+
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Companies:
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U.S. Census Bureau
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Address:
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, , ,
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Keywords:
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heavy tails ;
linear time series ;
self-normalization ;
subsampling ;
confidence intervals
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Abstract:
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We provide a self-normalization for the sample autocovariances (acvs) and autocorrelations (acs) of a linear, long-memory time series with innovations that have either finite 4th moment or are heavy-tailed with tail index between 2 and 4. The asymptotic distribution of the sample acv has three rates of convergence that depend on the interplay between the memory parameter d and the tail index alpha, and which consequently lead to three different limiting distributions; for the sample ac the limit distribution only depends on d. We introduce a self-normalized sample acv statistic, which is computable without knowledge of alpha or d (or their relationship), and which converges to a nondegenerate distribution. We also treat self-normalization of the acs. The sampling distributions can then be approximated nonparametrically by subsampling, as the corresponding asymptotic distribution is still parameter-dependent. The subsampling-based confidence intervals for the process acvs and acs are shown to have good empirical coverage rates in a simulation study. The impact of block size on the coverage is assessed.
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Authors who are presenting talks have a * after their name.
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