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Common breaks in time trends for large panel data with a factor structure

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  • Dukpa Kim

Abstract

In this paper, I analyse issues related to the estimation of a common break in a large panel of time series data. Each series in the panel consists of a linear time trend and a random error. The linear time trend is subject to a break that occurs at the same date for all series. The error term is cross‐sectionally correlated through a factor structure. The break date is estimated jointly with the common factors. In particular, two break date estimators are analysed: the first is obtained as an iterative solution while the second is obtained as a global solution. The asymptotic properties of these estimators are analysed under both global and local asymptotic frameworks. These two estimators are shown to be asymptotically equivalent and to achieve a faster rate of convergence than the simple break date estimator that does not take common factors into account. The limiting distributions of the proposed break date estimators are provided so that asymptotically valid confidence intervals can be formed. Monte Carlo simulation results are provided to support the theoretical results.

Suggested Citation

  • Dukpa Kim, 2014. "Common breaks in time trends for large panel data with a factor structure," Econometrics Journal, Royal Economic Society, vol. 17(3), pages 301-337, October.
    • Handle: RePEc:wly:emjrnl:v:17:y:2014:i:3:p:301-337
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    File URL: http://hdl.handle.net/10.1111/ectj.12033
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    Cited by:

    1. Bada, O. & Kneip, A. & Liebl, D. & Mensinger, T. & Gualtieri, J. & Sickles, R.C., 2022. "A wavelet method for panel models with jump discontinuities in the parameters," Journal of Econometrics, Elsevier, vol. 226(2), pages 399-422.
    2. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    3. Badi H. Baltagi & Chihwa Kao & Long Liu, 2017. "Estimation and identification of change points in panel models with nonstationary or stationary regressors and error term," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 85-102, March.
    4. Horváth, Lajos & Rice, Gregory, 2019. "Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 138-165.
    5. Yiren Wang & Peter C B Phillips & Liangjun Su, 2023. "Panel Data Models with Time-Varying Latent Group Structures," Papers 2307.15863, arXiv.org.
    6. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.
    7. repec:gam:jecnmx:v:12:y:2024:i:2:p:11-:d:1378087 is not listed on IDEAS
    8. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    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. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
    11. Jiang, Peiyun & Kurozumi, Eiji, 2021. "A new test for common breaks in heterogeneous panel data models," Discussion paper series HIAS-E-107, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    12. Barbora Peštová & Michal Pešta, 2017. "Change Point Estimation in Panel Data without Boundary Issue," Risks, MDPI, vol. 5(1), pages 1-22, January.

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