Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements

Abstract

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.

Keywords:

• structural breaks
• measurement error
• additive outlier
• wavelet transform
• empirical Bayes thresholding

JEL classification:

• C11
• C12
• C15

Acknowledgement

Yushu Li gratefully acknowledges funding from Swedish Research Council (project number 421-2009-2663). The authors would like to thank David Edgerton and one anonymous referee for helpful comments.

Notes

1. The sample sizes are chosen to be suitable for the application of the wavelet transform.[24]