Robust Data-Driven Decisions Under Model Uncertainty

TL;DR

This paper introduces new data updating methods that improve decision-making robustness under uncertain data-generating processes, outperforming traditional inference techniques in various economic models.

Contribution

It develops novel, easy-to-implement updating rules that ensure decisions are robustly better across all possible data-generating processes, addressing limitations of existing inference methods.

Findings

New updating rules outperform maximum likelihood and Bayesian methods.

Rules guarantee robust decision improvements asymptotically and in finite samples.

Application to economic models shows more intuitive conclusions under ambiguity.

Abstract

When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions. That is, no matter which DGP governs the uncertainty, one can make a better decision than without using the data. I show that common inference methods, e.g., maximum likelihood and Bayesian updating cannot achieve this goal. To address, I develop new updating rules that lead to robustly better decisions either asymptotically almost surely or in finite sample with a pre-specified probability. Especially, they are easy to implement as are given by simple extensions of the standard statistical procedures in the case where the possible DGPs are all independent and identically distributed. Finally, I show that the new updating rules also lead to more intuitive conclusions in existing economic models such as asset pricing under ambiguity.