Testing Heteroskedasticity in High-Dimensional Linear Regression
Statistics Theory
2022-11-01 v1 Statistics Theory
Abstract
We propose a new testing procedure of heteroskedasticity in high-dimensional linear regression, where the number of covariates can be larger than the sample size. Our testing procedure is based on residuals of the Lasso. We demonstrate that our test statistic has asymptotic normality under the null hypothesis of homoskedasticity. Simulation results show that the proposed testing procedure obtains accurate empirical sizes and powers. We also present results of real economic data applications.
Cite
@article{arxiv.2210.17339,
title = {Testing Heteroskedasticity in High-Dimensional Linear Regression},
author = {Akira Shinkyu},
journal= {arXiv preprint arXiv:2210.17339},
year = {2022}
}