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

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Listed author(s):
  • Reese, Simon

    ()

    (Department of Economics, Lund University)

  • Li, Yushu

    ()

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

Registered author(s):

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.

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File URL: http://project.nek.lu.se/publications/workpap/papers/WP13_36.pdf
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Paper provided by Lund University, Department of Economics in its series Working Papers with number 2013:36.

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Length: 17 pages
Date of creation: 11 Oct 2013
Publication status: Published as Reese, Simon and Yushu Li, 'Testing for Structural Breaks in the Presence of Data Perturbations: Impacts and Wavelet Based Improvements' in Journal of Statistical Computation and Simulation, 2015, pages 3468-3479.
Handle: RePEc:hhs:lunewp:2013_036
Contact details of provider: Postal:
Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden

Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en

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Keywords: 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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  2. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
  3. Kim, Dukpa & Perron, Pierre, 2009. "Assessing the relative power of structural break tests using a framework based on the approximate Bahadur slope," Journal of Econometrics, Elsevier, vol. 149(1), pages 26-51, April.
  4. Paulo M. M. Rodrigues & Antonio Rubia, 2011. "The Effects of Additive Outliers and Measurement Errors when Testing for Structural Breaks in Variance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(4), pages 449-468, 08.
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  7. Hansen, Bruce E., 1992. "Testing for parameter instability in linear models," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 517-533, August.
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  9. Crowley, Patrick M., 2005. "An intuitive guide to wavelets for economists," Research Discussion Papers 1/2005, Bank of Finland.
  10. Davidson, Russell, 2009. "Econometric Theory and Methods: International Edition," OUP Catalogue, Oxford University Press, number 9780195391053, April.
  11. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
  12. van Bergeijk, Peter A G, 1995. "The Accuracy of International Economic Observations," Bulletin of Economic Research, Wiley Blackwell, vol. 47(1), pages 1-20, January.
  13. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
  14. Johnstone, Iain & Silverman, Bernard W., 2005. "EbayesThresh: R Programs for Empirical Bayes Thresholding," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i08).
  15. John G. Cragg, 1994. "Making Good Inferences from Bad Data," Canadian Journal of Economics, Canadian Economics Association, vol. 27(4), pages 776-800, November.
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