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Grouped Change-Points Detection and Estimation in Panel Data

Author

Listed:
  • Haoran Lu

    (The School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China)

  • Dianpeng Wang

    (The School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China)

Abstract

The change-points in panel data can be obstacles for fitting models; thus, detecting change-points accurately before modeling is crucial. Extant methods often either assume that all panels share the common change-points or that grouped panels have the same unknown parameters. However, the problem of different change-points and model parameters between panels has not been solved. To deal with this problem, a novel approach is proposed here to simultaneously detect and estimate the grouped change-points precisely by employing an iterative algorithm and the penalty cost function. Some numerical experiments and case studies are utilized to demonstrate the superior performance of the proposed method in grouping the panels, and estimating the number and positions of change-points.

Suggested Citation

  • Haoran Lu & Dianpeng Wang, 2024. "Grouped Change-Points Detection and Estimation in Panel Data," Mathematics, MDPI, vol. 12(5), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:750-:d:1349815
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    References listed on IDEAS

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    1. Marc Lavielle & Eric Moulines, 2000. "Least‐squares Estimation of an Unknown Number of Shifts in a Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(1), pages 33-59, January.
    2. Jushan Bai, 1994. "Least Squares Estimation Of A Shift In Linear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 453-472, September.
    3. Holger Dette & Josua Gösmann, 2020. "A Likelihood Ratio Approach to Sequential Change Point Detection for a General Class of Parameters," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1361-1377, July.
    4. Bai, Jushan, 1997. "Estimating Multiple Breaks One at a Time," Econometric Theory, Cambridge University Press, vol. 13(3), pages 315-352, June.
    5. Davis, Richard A. & Lee, Thomas C.M. & Rodriguez-Yam, Gabriel A., 2006. "Structural Break Estimation for Nonstationary Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 223-239, March.
    6. Paul Fearnhead & Guillem Rigaill, 2019. "Changepoint Detection in the Presence of Outliers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 169-183, January.
    7. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    8. Zhuoheng Chen & Yijun Hu, 2017. "Cumulative sum estimator for change-point in panel data," Statistical Papers, Springer, vol. 58(3), pages 707-728, September.
    9. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    10. Okui, Ryo & Wang, Wendun, 2021. "Heterogeneous structural breaks in panel data models," Journal of Econometrics, Elsevier, vol. 220(2), pages 447-473.
    11. Chen, Bin & Huang, Liquan, 2018. "Nonparametric testing for smooth structural changes in panel data models," Journal of Econometrics, Elsevier, vol. 202(2), pages 245-267.
    12. Ngai Hang Chan & Chun Yip Yau & Rong-Mao Zhang, 2014. "Group LASSO for Structural Break Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 590-599, June.
    13. Lawrence Joseph & David Wolfson, 1993. "Maximum likelihood estimation in the multi-path change-point problem," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(3), pages 511-530, September.
    14. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.
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