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Change Point Detection in Panel Linear Regression Models Based on Jump Information Criterion.

Wenzhi Zhao1, Lu Fan1, Zhiming Xia2

  • 1School of Science, Xi'an Polytechnic University, Xi'an 710048, China.

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Summary

This study introduces a new Jump Information Criterion (JIC) for detecting structural breaks in panel linear regression models. The JIC method accurately identifies change points, offering a stable and efficient tool for panel data analysis.

Keywords:
change pointconvergence ratejump information criterionpanel data

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Area of Science:

  • Statistics
  • Econometrics
  • Data Analysis

Background:

  • Panel linear regression models are widely used but susceptible to structural breaks.
  • Detecting change points in these models is crucial for accurate analysis.
  • Existing methods may lack efficiency or accuracy in identifying these breaks.

Purpose of the Study:

  • To propose a novel Jump Information Criterion (JIC) for change point detection in panel linear regression.
  • To establish the theoretical consistency and convergence rate of the JIC estimator.
  • To validate the performance of JIC through simulations and real-world data.

Main Methods:

  • Reconstructing change point hypothesis testing as a parameter estimation problem.
  • Setting potential change point counts to 0 (null hypothesis) and 1 (alternative hypothesis).
  • Rigorous mathematical deductions for theoretical validation and Monte Carlo simulations for empirical testing.

Main Results:

  • The proposed JIC estimator demonstrates consistency and achieves an optimal convergence rate.
  • Simulation experiments confirm the JIC's accuracy, stability, and computational efficiency.
  • Empirical analysis on real data validates JIC as a reliable tool for structural break analysis.

Conclusions:

  • The Jump Information Criterion (JIC) provides an effective and reliable method for change point detection in panel linear regression.
  • JIC offers improved accuracy, stability, and computational efficiency compared to existing approaches.
  • This work presents a valuable new tool for researchers analyzing structural breaks in panel data.