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

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Author

Summary, in English

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.

Publishing year

2015

Language

English

Pages

3468-3479

Publication/Series

Journal of Statistical Computation and Simulation

Volume

85

Issue

17

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Economics

Keywords

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

Status

Published

ISBN/ISSN/Other

  • ISSN: 1563-5163