The wild bootstrap for multilevel models
Methodology
2015-08-25 v1
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.
Keywords
Cite
@article{arxiv.1508.05713,
title = {The wild bootstrap for multilevel models},
author = {Lucia Modugno and Simone Giannerini},
journal= {arXiv preprint arXiv:1508.05713},
year = {2015}
}