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.

翻译：我们提议在高维线性回归中采用新的半衰期试验程序,使共变体的数目大于样本的大小。我们的试验程序以激光索的残留物为基础。我们证明,我们的试验统计在同源性无效假设下具有无症状的正常性。模拟结果显示,拟议的试验程序获得了准确的经验大小和力量。我们还介绍了实际经济数据应用的结果。