Identify latent group structures in panel data: The classifylasso command

Wenxin Huang (), Yiru Wang and Lingyun Zhou ()
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Wenxin Huang: Shanghai Jiao Tong University
Lingyun Zhou: Tsinghua University

Stata Journal, 2024, vol. 24, issue 1, 46-71

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.

Keywords: classifylasso; classifylasso postestimation; classoselect; classo- coef; classogroup; unobserved parameter heterogeneity; latent group structures; classifier-lasso; penalized least squares; classification (search for similar items in EconPapers)
Date: 2024
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http://hdl.handle.net/10.1177/1536867X241233642

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Working Paper: Identify latent group structures in panel data: The classifylasso command (2023)
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DOI: 10.1177/1536867X241233642

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