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Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements

Author

Listed:
  • Reese, Simon

    (Department of Economics, Lund University)

  • Li, Yushu

    (Department of Business and Management Science, Norwegian School of Economics)

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.

Suggested Citation

  • Reese, Simon & Li, Yushu, 2013. "Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements," Working Papers 2013:36, Lund University, Department of Economics.
    • Handle: RePEc:hhs:lunewp:2013_036
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    References listed on IDEAS

    as
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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