The Wild Bootstrap for Multilevel Models

Lucia Modugno and Simone Giannerini

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 22, 4812-4825

Abstract: In this paper, we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modified version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence, it is a valuable tool for robust inference in a multilevel framework. We assess the finite size performances of the schemes through a Monte Carlo study. The results show that for big sample sizes it always pays off to adopt an agnostic approach as the wild bootstrap outperforms other techniques.

Date: 2015
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DOI: 10.1080/03610926.2013.802807

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