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Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model

Pierre Perron () and Yohei Yamamoto

Econometrics, 2019, vol. 7, issue 2, 1-11

Abstract: In empirical applications based on linear regression models, structural changes often occur in both the error variance and regression coefficients, possibly at different dates. A commonly applied method is to first test for changes in the coefficients (or in the error variance) and, conditional on the break dates found, test for changes in the variance (or in the coefficients). In this note, we provide evidence that such procedures have poor finite sample properties when the changes in the first step are not correctly accounted for. In doing so, we show that testing for changes in the coefficients (or in the variance) ignoring changes in the variance (or in the coefficients) induces size distortions and loss of power. Our results illustrate a need for a joint approach to test for structural changes in both the coefficients and the variance of the errors. We provide some evidence that the procedures suggested by Perron et al. (2019) provide tests with good size and power.

Keywords: structural change; variance shifts; CUSQ tests; hypothesis testing; sup-LR test (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Pitfalls of Two Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model (2019) Downloads
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