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Identify latent group structures in panel data: The classifylasso command
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DOI：10.1177/1536867X241233642.png)
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Abstract
En 中文
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:
st0739
classifylasso
classifylasso postestimation
classoselect
classocoef
classogroup
unobserved parameter heterogeneity
latent group structures
classifier-lasso
penalized least squares
classification
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Citations:
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