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Identify latent group structures in panel data: The classifylasso command

Author

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  • Wenxin Huang

    (Shanghai Jiao Tong University)

  • Yiru Wang

    (University of Pittsburgh)

  • Lingyun Zhou

    (Tsinghua University)

Abstract

In this article, we introduce a new command, classifylasso, that implements the classifier-lasso method (Su, Shi, and Phillips, 2016, Econometrica 84: 2215–2264) to simultaneously identify and estimate unobserved parameter heterogeneity in panel-data models using penalized techniques. We document the functionality of this command, including 1) penalized least-squares estimation of group-specific coefficients and classification of unknown group membership under a certain number of groups; 2) two lasso-type estimators with robust standard errors, namely, classifier-lasso and postlasso; and 3) determination of the number of groups based on an information criterion. We further develop some postestimation commands to display and visualize the estimation results.

Suggested Citation

  • Wenxin Huang & Yiru Wang & Lingyun Zhou, 2024. "Identify latent group structures in panel data: The classifylasso command," Stata Journal, StataCorp LP, vol. 24(1), pages 46-71, March.
    • Handle: RePEc:tsj:stataj:v:24:y:2024:i:1:p:46-71
    DOI: 10.1177/1536867X241233642
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    Cited by:

    1. 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).

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