Counterfactuals in equilibrium models are functions of the current state of the world, the exogenous change variables and the model parameters. Current practice treats the current state of the world, the observed data, as perfectly measured, but there is good reason to believe that they are measured with error. The main aim of this paper is to provide tools for quantifying uncertainty about counterfactuals, when the current state of the world is measured with error. I propose two methods, a Bayesian approach and an adversarial approach. Both methods are practical and theoretically justified. I apply the two methods to the application in Adao et al. (2017) and find non-trivial uncertainty about counterfactuals.PDF: Counterfactual Sensitivity in Equilibrium Models.pdf
