ECONOMETRICS: Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits; Professor Keisuke HIRANO (The Pennsylvania State University)

Abstract

We develop asymptotic approximation results that can be applied to sequential estimation and inference problems, adaptive randomized controlled trials, and other statistical decision problems that can be cast as involving multiple decision nodes with structured and possibly endogenous information sets. Our results extend the classic Asymptotic Representation Theorem that provides a basis for efficiency bound theory and local power analysis. In adaptive settings where the decision at one stage can affect the observation of variables in later stages, we show that a limiting data environment characterizes all limit distributions attainable through the choice of the adaptive design rule and the decision rules applied to the adaptively generated data, under local alternatives. We illustrate how the theory can be applied to study the choice of adaptive rules and end-of-sample statistical inference in batched (groupwise) sequential adaptive experiments.