🤖 AI Summary
This study addresses the pervasive issue of dynamic inconsistency in classical statistical decision rules based on ex ante criteria—such as minimax regret—which often become irrational to follow once data are observed. The paper provides the first systematic characterization of this problem, develops a formal framework for analyzing dynamic consistency, and axiomatically introduces two novel optimality criteria. By integrating decision theory, axiomatic reasoning, and minimax regret analysis, the proposed criteria effectively prevent post-data deviations across a range of empirical settings, thereby ensuring that decision rules remain coherent before and after information updating. This approach rectifies the behavioral discrepancies inherent in existing methodologies and enhances their practical applicability.
📝 Abstract
A large literature in econometrics proposes decision rules with optimality guarantees based on ex ante criteria, such as minimax regret. We develop a framework for analyzing the dynamic consistency of such rules and show that, in many empirically relevant settings, the researcher may wish to deviate from the interim prescription of ex ante optimal rules after observing the data realization. To address this problem, we propose and axiomatize two classes of optimality criteria that yield dynamically consistent decision rules.