Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237349 
Year of Publication: 
2021
Series/Report no.: 
ECONtribute Discussion Paper No. 104
Publisher: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
Abstract: 
Economists often estimate a subset of their model parameters outside the model and let the decision-makers inside the model treat these point estimates as-if they are correct. This practice ignores model ambiguity, opens the door for misspecification of the decision problem, and leads to post-decision disappointment. We develop a framework to explore, evaluate, and optimize decision rules that explicitly account for the uncertainty in the first step estimation using statistical decision theory. We show how to operationalize our analysis by studying a stochastic dynamic investment model where the decision-makers take ambiguity about the model's transition dynamics directly into account.
Subjects: 
decision-making under uncertainty
robust Markov decision process
JEL: 
D81
C44
D25
Document Type: 
Working Paper

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