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Department of Economics, Lund University Working Papers, Department of Economics, Lund University

No 2013:36:
Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements

Simon Reese () and Yushu Li ()

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; (follow links to similar papers)

JEL-Codes: C11; C12; C15; (follow links to similar papers)

17 pages, October 11, 2013

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This paper is published as:
Reese, Simon and Yushu Li, (2015), 'Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements', Journal of Statistical Computation and Simulation, Vol. 85, No. 17, pages 3468-3479



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