Empirical Bayes Estimation in Heterogeneous Coefficient Panel Models

Abstract: We develop an empirical Bayes (EB) G-modeling framework for short-panel linear models with multidimensional heterogeneity and nonparametric prior. Specifically, we allow heterogeneous intercepts, slopes, dynamics, and a non-spherical error covariance structure. We establish identification and consistency of the nonparametric maximum likelihood estimator (NPMLE) under general conditions, and provide low-level sufficient conditions for several models of empirical interest. Conditions for regret consistency of the resulting EB estimators are also established. The NPMLE is computed using a Wasserstein-Fisher-Rao gradient flow algorithm adapted to panel regressions. Using data from the Panel Study of Income Dynamics, we find that the slope coefficient for potential experience is substantially heterogeneous and negatively correlated with the random intercept, and that error variances and autoregressive coefficients vary significantly across individuals. The EB estimates reduce mean squared prediction errors relative to individual maximum likelihood estimates.