Dependent wild bootstrap for the empirical process

Paul Doukhan, Gabriel Lang, Anne Leucht, Michael H. Neumann

In this paper, we propose a model-free bootstrap method for the empirical process under absolute regularity. More precisely, consistency of an adapted version of the so-called dependent wild bootstrap, that was introduced by Shao (2010) and is very easy to implement, is proved under minimal conditions on the tuning parameter of the procedure. We apply our results to construct confidence intervals for unknown parameters and to approximate critical values for statistical tests. A simulation study shows that our method is competitive to standard block bootstrap methods in finite samples.

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Year of publication:	Januar 2014
Authors:	Doukhan, Paul ; Lang, Gabriel ; Leucht, Anne ; Neumann, Michael H.
Publisher:	Mannheim : Universität Mannheim, Department of Economics
Subject:	Absolute regularity \| bootstrap \| empirical process \| time series \| V -statistics \| quantiles \| Kolmogorov-Smirnov test \| Bootstrap-Verfahren \| Bootstrap approach \| Zeitreihenanalyse \| Time series analysis \| Theorie \| Theory \| Statistischer Test \| Statistical test