The wild bootstrap for multilevel models

TL;DR

This paper evaluates and improves bootstrap methods for multilevel models, introducing a modified wild bootstrap that enhances robustness and performs well with large samples in hierarchical data analysis.

Contribution

It proposes a modified wild bootstrap procedure tailored for hierarchical data structures, improving robustness in multilevel model inference.

Findings

Wild bootstrap outperforms other methods with large samples.

Modified procedure is robust to heteroscedasticity.

Agnostic approach benefits large-sample multilevel analysis.

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.