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

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  • Reese, Simon

    ()
    (Department of Economics, Lund University)

  • Li, Yushu

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

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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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Bibliographic Info

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
Date of revision:
Handle: RePEc:hhs:lunewp:2013_036

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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;

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  1. Davidson, Russell, 2009. "Econometric Theory and Methods: International Edition," OUP Catalogue, Oxford University Press, number 9780195391053, September.
  2. Schennach, Susanne M., 2004. "Exponential specifications and measurement error," Economics Letters, Elsevier, vol. 85(1), pages 85-91, October.
  3. 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.
  4. Crowley, Patrick, 2005. "An intuitive guide to wavelets for economists," Research Discussion Papers 1/2005, Bank of Finland.
  5. John G. Cragg, 1994. "Making Good Inferences from Bad Data," Canadian Journal of Economics, Canadian Economics Association, vol. 27(4), pages 776-800, November.
  6. Iain Johnstone & Bernard W. Silverman, . "EbayesThresh: R Programs for Empirical Bayes Thresholding," Journal of Statistical Software, American Statistical Association, vol. 12(i08).
  7. Nathan S. Balke & Thomas B. Fomby, 1991. "Large shocks, small shocks, and economic fluctuations: outliers in macroeconomic times series," Research Paper 9101, Federal Reserve Bank of Dallas.
  8. Paulo M.M. Rodrigues & Antonio Rubia, 2010. "The Effects of Additive Outliers and Measurement Errors when Testing for Structural Breaks in Variance," Working Papers w201011, Banco de Portugal, Economics and Research Department.
  9. Hansen, Bruce E., 1992. "Testing for parameter instability in linear models," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 517-533, August.
  10. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  11. 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.
  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. Ramazan Gencay & Nikola Gradojevic, 2009. "Errors-in-Variables Estimation with No Instruments," Working Paper Series 30_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  14. Christoph Schleicher, 2002. "An Introduction to Wavelets for Economists," Working Papers 02-3, Bank of Canada.
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