Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements
AbstractThis 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.
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Bibliographic InfoPaper provided by Lund University, Department of Economics in its series Working Papers with number 2013:36.
Length: 17 pages
Date of creation: 11 Oct 2013
Date of revision:
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Structural breaks; measurement error; additive outliers; wavelet transform; empirical Bayes thresholding;
Find related papers by 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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