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Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements. (English) Zbl 1510.62346

Summary: This paper investigates how classical measurement error and additive outliers (AO) influence tests for structural change based on \(F\)-statistics. We derive theoretically the impact of general additive disturbances in the regressors on the asymptotic distribution of these tests for structural change. The small sample properties in the case of classical measurement error and AO are investigated via Monte Carlo simulations, revealing that sizes are biased upwards and that powers are reduced. Two-wavelet-based denoising methods are used to reduce these distortions. We show that these two methods can significantly improve the performance of structural break tests.

MSC:

62M07 Non-Markovian processes: hypothesis testing
62C12 Empirical decision procedures; empirical Bayes procedures
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)

Software:

wmtsa; EBayesThresh
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Full Text: DOI Link

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