Identify latent group structures in panel data: The classifylasso command

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.