A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data

Abstract

New methods are discussed for estimating the population-averaged (PA) coefficients of some variables of interest in a sparse, high-dimensional generalized linear model (GLM) in an unbalanced panel-data setting with random effects. A cluster plugin for GLM lassos with unbalanced panel data is proposed. It is proven that a lasso that uses the new cluster-plugin method can outperform a lasso using the cross-sectional plugin, when the covariates have non-zero within-panel covariances. The proposed cluster plugin for GLMs extends the literature on Neyman-orthogonal moment conditions and provides estimators for the PA coefficients in sparse, high-dimensional logit, Poisson, and linear models when the data come from unbalanced panels. The results of the Monte Carlo simulations show that the implemented estimators perform well in finite samples. The simulations also show that a GLM lasso using the proposed cluster-plugin method produces more accurate covariate selection than a GLM lasso using the cross-sectional plugin method, when the covariates have non-zero within-panel covariances. Easy-to-use Stata commands are available for the proposed methods.