Subsampling the Mean of Heavy-Tailed Dependent Observations

UPF Economics and Business Working Paper No. 600

Posted: 4 Oct 2002

See all articles by Piotr Kokoszka

Piotr Kokoszka

Utah State University - Department of Mathematics & Statistics

Michael Wolf

University of Zurich - Department of Economics

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.

Keywords: Heavy tails, linear time series, subsampling

JEL Classification: C10, C14, C32

Suggested Citation

Kokoszka, Piotr and Wolf, Michael, Subsampling the Mean of Heavy-Tailed Dependent Observations. UPF Economics and Business Working Paper No. 600, Available at SSRN: https://ssrn.com/abstract=311579

Piotr Kokoszka

Utah State University - Department of Mathematics & Statistics ( email )

3900 Old Main Hill
Logan, UT 84322-3530
United States
435-797-0746 (Phone)
435-797-1822 (Fax)

HOME PAGE: http://www.math.usu.edu/~piotr/

Michael Wolf (Contact Author)

University of Zurich - Department of Economics ( email )

Wilfriedstrasse 6
Zurich, 8032
Switzerland

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