Climate Disasters and Exchange Rates: Are Beliefs Keeping up with Climate Change?

There is clear scientific evidence of the shift in the probability distribution of climate-related disasters in recent decades. Is this shift reflected in the behavior of forward-looking measures of economic activity such as real exchange rates? I evaluate the role of different belief formation assumptions on the ability of the model to predict the response of real exchange rates to climate-related disasters. I consider Bayesian and backward-looking belief updates as well as static beliefs with no update or a one-time update. To do so, I construct a version of the Farhi-Gabaix (2015) framework augmented with explicit belief formation. I use two approaches to model calibration and simulate the model for 47 countries for 1964–2019 using actual data for climate-related disasters. I find that in general differences in belief formation do not have much effect on the model fit because the productivity loss component dominates the predicted response. Specifically, I find that even in recent years there is no evidence of Bayesian beliefs being a better fit for the data.