Individual Shrinkage for Random Effects

This paper develops a novel approach to random effects estimation and individual-level forecasting in micropanels, targeting individual accuracy rather than aggregate performance. The conventional shrinkage methods used in the literature, such as the James-Stein estimator and Empirical Bayes, target aggregate performance and can lead to inaccurate decisions at the individual level. We propose a complementary class of shrinkage estimators with individual weights (IW) that leverage an individual's own past history, instead of the cross-sectional dimension. This approach overcomes the "tyranny of the majority" inherent in existing methods, while relying on weaker assumptions. We discuss the theoretical optimality of IW and recommend using feasible weights determined through a Minimax Regret analysis in practice.