Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282154 
Year of Publication: 
2023
Series/Report no.: 
Discussion Paper No. 463
Publisher: 
Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition, München und Berlin
Abstract: 
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.
Subjects: 
decision-making under uncertainty
robust Markov decision process
JEL: 
D81
C44
D25
Document Type: 
Working Paper

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