Pitfalls of Two Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model
Pierre Perron () and
No 2019-01, Discussion Papers from Graduate School of Economics, Hitotsubashi University
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 the test 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, Yamamoto and Zhou (2019) provide tests with good size and power.
Keywords: structural change; variance shifts; CUSUM of squares tests; hypothesis testing; Sup-LR test (search for similar items in EconPapers)
JEL-codes: C12 C38 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-bec, nep-ecm, nep-ets and nep-ore
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Journal Article: Pitfalls of Two-Step Testing for Changes in the Error Variance and Coefficients of a Linear Regression Model (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hit:econdp:2019-01
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