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A note on estimating the bent line quantile regression model

Author

Listed:
  • Yanyang Yan

    (Hunan University)

  • Feipeng Zhang

    (Hunan University)

  • Xiaoying Zhou

    (Hunan University)

Abstract

This paper considers a new estimating method for the bent line quantile regression model. By a simple linearization technique, the proposed method can simultaneously obtain the estimates of the regression coefficients and the change-point location. Moreover, it can be readily implemented by current software. Simulation studies demonstrate that the proposed method has good finite sample performance. Two empirical applications are also presented to illustrate the method.

Suggested Citation

  • Yanyang Yan & Feipeng Zhang & Xiaoying Zhou, 2017. "A note on estimating the bent line quantile regression model," Computational Statistics, Springer, vol. 32(2), pages 611-630, June.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:2:d:10.1007_s00180-017-0711-9
    DOI: 10.1007/s00180-017-0711-9
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    References listed on IDEAS

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    1. Wachsmuth A. & Wilkinson L. & Dallal G.E., 2003. "Galtons Bend: A Previously Undiscovered Nonlinearity in Galtons Family Stature Regression Data," The American Statistician, American Statistical Association, vol. 57, pages 190-192, August.
    2. Chenxi Li & Ying Wei & Rick Chappell & Xuming He, 2011. "Bent Line Quantile Regression with Application to an Allometric Study of Land Mammals' Speed and Mass," Biometrics, The International Biometric Society, vol. 67(1), pages 242-249, March.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Lee, Sokbae & Seo, Myung Hwan & Shin, Youngki, 2011. "Testing for Threshold Effects in Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 220-231.
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