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Tests for structural break in quantile regressions

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  • Marilena Furno

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Abstract

The paper compares the existing tests for parameter instability in quantile regression. One is based on the estimated objective function and the other on the gradient. Their definition determines their characteristics and helpfulness. The former allows to check if the impact of a break on the entire equation changes across quantiles while a modified version of the latter verifies if the break affects only some coefficients or all of them and helps locating the break point. In addition the paper presents a Lagrange multiplier test for structural break. The advantage of the LM test is in the ease of implementation, since it simply requires the estimation of an auxiliary regression. An example shows the characteristics of each test. A Monte Carlo study concludes the analysis. Copyright Springer-Verlag 2012

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File URL: http://hdl.handle.net/10.1007/s10182-012-0188-3
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Bibliographic Info

Article provided by Springer in its journal AStA Advances in Statistical Analysis.

Volume (Year): 96 (2012)
Issue (Month): 4 (October)
Pages: 493-515

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Handle: RePEc:spr:alstar:v:96:y:2012:i:4:p:493-515

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Related research

Keywords: Structural break; Quantile regression; Test;

References

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  1. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.
  2. So, Beong Soo & Shin, Dong Wan, 2001. "An invariant sign test for random walks based on recursive median adjustment," Journal of Econometrics, Elsevier, vol. 102(2), pages 197-229, June.
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  4. Donald W.K. Andrews, 1990. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Cowles Foundation Discussion Papers 943, Cowles Foundation for Research in Economics, Yale University.
  5. Zhongjun Qu & Tatsushi Oka, 2010. "Estimating structural changes in regression quantiles," Boston University - Department of Economics - Working Papers Series WP2010-052, Boston University - Department of Economics.
  6. Zhao, Quanshui, 2001. "Asymptotically Efficient Median Regression In The Presence Of Heteroskedasticity Of Unknown Form," Econometric Theory, Cambridge University Press, vol. 17(04), pages 765-784, August.
  7. Bai, Jushan, 1995. "Least Absolute Deviation Estimation of a Shift," Econometric Theory, Cambridge University Press, vol. 11(03), pages 403-436, June.
  8. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, April.
  9. He X. & Zhu L-X., 2003. "A Lack-of-Fit Test for Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1013-1022, January.
  10. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
  11. Weiss, Andrew A., 1990. "Least absolute error estimation in the presence of serial correlation," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 127-158.
  12. Gagliardini, Patrick & Trojani, Fabio & Urga, Giovanni, 2005. "Robust GMM tests for structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 139-182.
  13. Marilena Furno, 2008. "Quantile regressions analysis of the Italian school system," Working Papers 2008-06, Universita' di Cassino, Dipartimento di Scienze Economiche.
  14. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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