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 and opens the door for model misspecification and post-decision disappointment. We develop a framework to explore and evaluate decision rules that explicitly account for the uncertainty in the first step estimation and assess their performance in a decision-theoretic setting . We show how to operationalize our analysis by studying a stochastic dynamic investment model where the decision-maker takes ambiguity about the model's transition dynamics directly into account.
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.
R
and
,
R
model misspecification and
misspecification of the decision problem, and leads to
R
and evaluate
, evaluate, and optimize
R
and assess their performance in a decision-theoretic setting
using statistical decision theory
R
decision-maker takes
decision-makers take