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The Wild Bootstrap for Multilevel Models

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  • Lucia Modugno
  • Simone Giannerini

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. "The Wild Bootstrap for Multilevel Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(22), pages 4812-4825, November.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:22:p:4812-4825
    DOI: 10.1080/03610926.2013.802807
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    Cited by:

    1. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.

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