A James-Stein-type adjustment to bias correction in fixed effects panel models

This paper proposes a James-Stein-type (JS) adjustment to analytical bias correction in fixed effects panel models that suffer from the incidental parameters problem. We provide high-level conditions under which infeasible JS leads higher-order MSE improvement over bias-corrected estimator, and former is asymptotically equivalent latter. To obtain feasible adjustment, we propose nonparametric bootstrap procedure estimate weighting matrix for its consistency. apply two models: (1) linear autoregressive model with effects, (2) nonlinear static model. For each application, employ Monte Carlo simulations confirm theoretical results illustrate finite-sample improvements due adjustment. Finally, extension of more general class other policy are illustrated.