Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements

Simon Reese () and Yushu Li
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Simon Reese: Department of Economics, Lund University, Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund, Sweden, http://www.nek.lu.se/en/contact

No 2013:36, Working Papers from Lund University, Department of Economics

Abstract: This paper investigates how classical measurement error and additive outliers 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 additive outliers 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.

Keywords: Structural breaks; measurement error; additive outliers; wavelet transform; empirical Bayes thresholding (search for similar items in EconPapers)
JEL-codes: C11 C12 C15 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2013-10-11
New Economics Papers: this item is included in nep-ecm
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Published as Reese, Simon and Yushu Li, 'Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements' in Journal of Statistical Computation and Simulation, 2015, pages 3468-3479.

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