Testing for structural changes in linear regressions with time-varying variance

Erhua Zhang and Jilin Wu

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 20, 4889-4918

Abstract: This article proposes a nonparametric test for structural changes in linear regression models that allows for serial correlation, autoregressive conditional heteroskedasticity and time-varying variance in error terms. The test requires no trimming of the boundary region near the end points of the sample period, and requires no prior information on the alternative, what it requires is the transformed OLS residuals under the null hypothesis. We show that the test has a limiting standard normal distribution under the null hypothesis, and is powerful against single break, multiple breaks and smooth structural changes. The Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes.

Date: 2020
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DOI: 10.1080/03610926.2019.1609038

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