2021/04/23 by Blesch, Maximilian, Eisenhauer, Philipp
#Econometrics (econ.EM) #FOS: Economics and business #Theoretical Economics (econ.TH)
paper · doi:10.48550/arxiv.2104.12573
Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model's implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to post-decision disappointment. Using statistical decision theory, we develop a framework to explore, evaluate, and optimize robust decision rules that explicitly account for estimation uncertainty. We show how to operationalize our analysis by studying robust decisions in a stochastic dynamic investment model in which a decision-maker directly accounts for uncertainty in the model's transition dynamics.