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Quantile regression estimates and the analysis of structural breaks

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  • Marilena Furno

Abstract

The paper considers a test for structural breaks based on quantile regressions instead of OLS estimates. Besides granting robustness, this allows us to verify the impact of a break in more than one point of the conditional distribution. The quantile regression test is then repeatedly implemented as a diagnostic tool to uncover partial or spurious breaks. The test is also implemented to measure the contribution of each explanatory variable to the instability of the regression coefficients, thus finding which one of the different possible sources of breaks linked to the nature of the explanatory variables is the most effective. A real data example of exchange rates shows the presence of a time-driven break, but only at the lower quartile, while the analysis of the explanatory variable excludes its involvement in the break. Since the asymptotic distribution of the OLS test for structural change depends on i.i.d. normal errors and on the exogeneity of the explanatory variables, a Monte Carlo study analyses the behavior of OLS and quantile regression tests for structural changes with lagged endogenous variables, non-normal errors, spurious or partial breaks, and misspecification.

Suggested Citation

  • Marilena Furno, 2014. "Quantile regression estimates and the analysis of structural breaks," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2185-2192, December.
    • Handle: RePEc:taf:quantf:v:14:y:2014:i:12:p:2185-2192
    DOI: 10.1080/14697688.2011.653387
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    Cited by:

    1. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    2. Harry J. Turtle & Chengping Zhang, 2015. "Structural breaks and portfolio performance in global equity markets," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 909-922, June.

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