Wild Bootstrap Inference for Linear Regressions with Many Covariates
Econometrics
2025-06-27 v1
Abstract
We propose a simple modification to the wild bootstrap procedure and establish its asymptotic validity for linear regression models with many covariates and heteroskedastic errors. Monte Carlo simulations show that the modified wild bootstrap has excellent finite sample performance compared with alternative methods that are based on standard normal critical values, especially when the sample size is small and/or the number of controls is of the same order of magnitude as the sample size.
Cite
@article{arxiv.2506.20972,
title = {Wild Bootstrap Inference for Linear Regressions with Many Covariates},
author = {Wenze Li},
journal= {arXiv preprint arXiv:2506.20972},
year = {2025}
}