A unified approach to self-normalized block sampling

Shuyang Bai, Murad S. Taqqu and Ting Zhang

Stochastic Processes and their Applications, 2016, vol. 126, issue 8, 2465-2493

Abstract: The inference procedure for the mean of a stationary time series is usually quite different under various model assumptions because the partial sum process behaves differently depending on whether the time series is short or long-range dependent, or whether it has a light or heavy-tailed marginal distribution. In the current paper, we develop an asymptotic theory for the self-normalized block sampling, and prove that the corresponding block sampling method can provide a unified inference approach for the aforementioned different situations in the sense that it does not require the a priori estimation of auxiliary parameters. Monte Carlo simulations are presented to illustrate its finite-sample performance. The R function implementing the method is available from the authors.

Keywords: Time series; Subsampling; Block sampling; Sampling window; Self-normalization; Heavy tails; Long-range dependence; Long memory (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spa.2016.02.007

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