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Tests for structural break in quantile regressions

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  • Marilena Furno

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

Suggested Citation

  • Marilena Furno, 2012. "Tests for structural break in quantile regressions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 493-515, October.
    • Handle: RePEc:spr:alstar:v:96:y:2012:i:4:p:493-515
    DOI: 10.1007/s10182-012-0188-3
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    Cited by:

    1. Marilena Furno, 2011. "Goodness of Fit and Misspecification in Quantile Regressions," Journal of Educational and Behavioral Statistics, , vol. 36(1), pages 105-131, February.
    2. Liu, Weiqiang, 2023. "A consistent nonparametric test for the structure change in quantile regression," Economics Letters, Elsevier, vol. 228(C).
    3. Zhou, Mi & Wang, Huixia Judy & Tang, Yanlin, 2015. "Sequential change point detection in linear quantile regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 98-103.
    4. Marilena Furno, 2021. "Cointegration tests at the quantiles," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1087-1100, January.

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