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Subsampling the Mean of Heavy-tailed Dependent Observations

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Author Info
Piotr Kokoszka
Michael Wolf
Abstract

We establish the validity of subsampling confidence intervals for the mean of a dependent series with heavy-tailed marginal distributions. Using point process theory, we study both linear and nonlinear GARCH-like time series models. We propose a data-dependent method for the optimal block size selection and investigate its performance by means of a simulation study.

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File URL: http://www.econ.upf.edu/docs/papers/downloads/600.pdf
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Publisher Info
Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 600.

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Date of creation: Feb 2002
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Handle: RePEc:upf:upfgen:600

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Related research
Keywords: Heavy tails; linear time series; subsampling;

Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Carrasco, Marine & Chen, Xiaohong, 2002. "Mixing And Moment Properties Of Various Garch And Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 18(01), pages 17-39, February. [Downloadable!]
  2. McElroy, Tucker & Politis, Dimitris N., 2002. "Robust Inference For The Mean In The Presence Of Serial Correlation And Heavy-Tailed Distributions," Econometric Theory, Cambridge University Press, vol. 18(05), pages 1019-1039, October. [Downloadable!]
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This page was last updated on 2009-11-27.

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