We introduce a new type of shrinkage estimator that is not based on asymptotic optimality, but instead learns a state-dependent shrinkage policy via supervised learning in a contextual bandit setup. The proposed estimator applies to both linear and nonlinear shrinkage and shows improved...

A group of agents with a common prior receive informative signals about an unknown state repeatedly over time. If these signals were public, agents' beliefs would be identical and commonly known. This suggests that if signals were private, then the more correlated these are, the greater the...

Financial crises appear to have long‐lasting effects, even after the crisis itself has passed. This paper offers a simple explanation based on Bayesian learning from rare events. Agents face a latent and time‐varying probability of economic disaster. When a disaster occurs, learning results...