  Counterfactuals in quantitative trade and spatial models are functions of the
current state of the world and the model parameters. Common practice treats the
current state of the world as perfectly observed, but there is good reason to
believe that it is measured with error. This paper provides tools for
quantifying uncertainty about counterfactuals when the current state of the
world is measured with error. I recommend an empirical Bayes approach to
uncertainty quantification, and show that it is both practical and
theoretically justified. I apply the proposed method to the settings in Adao,
Costinot, and Donaldson (2017) and Allen and Arkolakis (2022) and find
non-trivial uncertainty about counterfactuals.
