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arXiv · 1508.05713

The wild bootstrap for multilevel models

Lucia Modugno·Simone Giannerini
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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.

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Lucia Modugno, Simone Giannerini. 2015-08-24. The wild bootstrap for multilevel models. https://arxiv.org/abs/1508.05713

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