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A note on estimating the bent line quantile regression model.

Yanyang Yan1, Feipeng Zhang1, Xiaoying Zhou1

  • 1School of Finance and Statistics, Hunan University, Changsha 410082, China.

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Summary
This summary is machine-generated.

A novel method estimates bent line quantile regression models efficiently. This approach determines both regression coefficients and change-point locations simultaneously using linearization.

Keywords:
Change-pointLinearization techniqueQuantile regression

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Area of Science:

  • Statistics
  • Econometrics
  • Data Analysis

Background:

  • Quantile regression models are essential for analyzing data with varying conditional distributions.
  • Bent line models are frequently used to capture structural breaks in data.
  • Estimating change-point locations and coefficients simultaneously presents computational challenges.

Purpose of the Study:

  • To introduce a new, efficient estimation method for the bent line quantile regression model.
  • To enable simultaneous estimation of regression coefficients and the change-point location.
  • To provide a method easily implementable with existing statistical software.

Main Methods:

  • A simple linearization technique is employed to transform the bent line quantile regression problem.
  • The proposed method integrates the estimation of coefficients and the change-point.
  • The approach is designed for straightforward application using standard statistical software.

Main Results:

  • Simulation studies confirm the proposed method's good finite sample performance.
  • The method effectively estimates both regression coefficients and the change-point location.
  • Empirical applications demonstrate the practical utility of the new estimation technique.

Conclusions:

  • The proposed linearization technique offers an effective and practical solution for bent line quantile regression.
  • This method simplifies the simultaneous estimation of key model parameters.
  • The approach shows promise for broader application in statistical modeling and data analysis.