Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260092 
Year of Publication: 
2013
Series/Report no.: 
Working Paper No. 2013:36
Publisher: 
Lund University, School of Economics and Management, Department of Economics, Lund
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.
Subjects: 
Structural breaks
measurement error
additive outliers
wavelet transform
empirical Bayes thresholding
JEL: 
C11
C12
C15
Document Type: 
Working Paper

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