We construct and study a test to detect possible change points in the regression parameters of a linear model when the model errors and covariates may exhibit heteroscedasticity Being based on a new trimming scheme for the CUSUM process introduced in Horváth et al (2020), this test is particularly well suited to detect changes that might occur near the endpoints of the sample A complete asymptotic theory for the test is developed under the null hypothesis of no change in the regression parameter, and consistency of the test is also established in the presence of a parameter change Monte Carlo simulations show that our test is comparable to existing methods when the errors are homoscedastic In contrast, existing methods developed for homoscedastic data are demonstrated to be ill-sized and poorly performing in the presence of heteroscedasticity, while the proposed test continues to perform well in heteroscedastic environments These results are further demonstrated in a study of the linear connection between the price of crude oil and the U S dollar, and in detecting changes points in asset pricing models surrounding the COVID-19 pandemic © 2020 Board of the Foundation of the Scandinavian Journal of Statistics

中文翻译：

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检测具有异方差误差的线性模型的早期或晚期变化

我们构建并研究了一个测试，以在模型误差和协变量可能表现出异方差时检测线性模型的回归参数中可能的变化点 基于 Horváth 等人 (2020) 中引入的 CUSUM 过程的新修整方案，该测试特别适合检测可能发生在样本端点附近的变化 在回归参数没有变化的原假设下开发了测试的完整渐近理论，并且测试的一致性也在存在的情况下建立参数变化 Monte Carlo 模拟表明，当误差是同方差时，我们的测试与现有方法相当 相比之下，为同方差数据开发的现有方法被证明在存在异方差的情况下规模不佳且性能不佳，虽然提议的测试在异方差环境中继续表现良好 这些结果在对原油价格和美元之间的线性关系的研究中得到进一步证明，并在检测围绕 COVID-19 大流行的资产定价模型中的变化点 © 2020 年斯堪的纳维亚统计杂志基金会董事会