Scandinavian Working Papers in Economics

Working Papers,
Lund University, Department of Economics

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

Simon Reese () and Yushu Li ()
Additional contact information
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
Yushu Li: Department of Business and Management Science, Norwegian School of Economics, Postal: NHH, Helleveien 30, NO-5045 Bergen, Norway

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

JEL-codes: C11; C12; C15

17 pages, October 11, 2013

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WP13_36.pdf PDF-file

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Published as
Simon Reese 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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