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Identifying Latent Group Structures in Nonlinear Panels

Author

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  • Wang, Wuyi

    (School of Economics, Singapore Management University)

  • Su, Liangjun

    (School of Economics, Singapore Management University)

Abstract
We propose a procedure to identify latent group structures in nonlinear panel data models where some regression coefficients are heterogeneous across groups but homogeneous within a group and the group number and membership are unknown. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we extend the sequential binary segmentation algorithm of Bai (1997) for break detection from the time series setup to the panel data framework. We demonstrate that our method is able to identify the true latent group structures with probability approaching one and the post-classification estimators are oracle-efficient. The method has the advantage of more convenient implementation compared with some alternative methods, which is a desirable feature in nonlinear panel applications. To improve the finite sample performance, we also consider an alternative version based on the spectral decomposition of certain estimated matrix and link the group identification issue to the community detection problem in the network literature. Simulations show that our method has good finite sample performance. We apply this method to explore how individuals' portfolio choices respond to their financial status and other characteristics using the Netherlands household panel data from year 1993 to 2015, and find three latent groups.

Suggested Citation

  • Wang, Wuyi & Su, Liangjun, 2017. "Identifying Latent Group Structures in Nonlinear Panels," Economics and Statistics Working Papers 19-2017, Singapore Management University, School of Economics.
  • Handle: RePEc:ris:smuesw:2017_019
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    Citations

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    Cited by:

    1. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2024. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1026-1040, July.
    2. Leng, Xuan & Chen, Heng & Wang, Wendun, 2023. "Multi-dimensional latent group structures with heterogeneous distributions," Journal of Econometrics, Elsevier, vol. 233(1), pages 1-21.
    3. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    4. Yu, Lu & Gu, Jiaying & Volgushev, Stanislav, 2024. "Spectral clustering with variance information for group structure estimation in panel data," Journal of Econometrics, Elsevier, vol. 241(1).
    5. Wang, Yiren & Phillips, Peter C.B. & Su, Liangjun, 2024. "Panel data models with time-varying latent group structures," Journal of Econometrics, Elsevier, vol. 240(1).
    6. Bofei Xiao & Bo Lei & Wei Lan & Bin Guo, 2022. "A blockwise network autoregressive model with application for fraud detection," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1043-1065, December.
    7. Mehrabani, Ali, 2023. "Estimation and identification of latent group structures in panel data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1464-1482.
    8. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    9. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    10. Su, Liangjun & Wang, Wuyi & Xu, Xingbai, 2023. "Identifying latent group structures in spatial dynamic panels," Journal of Econometrics, Elsevier, vol. 235(2), pages 1955-1980.
    11. Chen, Elynn Y. & Fan, Jianqing & Zhu, Xuening, 2023. "Community network auto-regression for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1239-1256.
    12. Langevin, R.;, 2024. "Consistent Estimation of Finite Mixtures: An Application to Latent Group Panel Structures," Health, Econometrics and Data Group (HEDG) Working Papers 24/16, HEDG, c/o Department of Economics, University of York.

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    More about this item

    Keywords

    Binary segmentation algorithm; clustering; community detection; network; oracle estimator; panel structure model; parameter heterogeneity; singular value decomposition.;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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