Robustness is a fledgling literature in Macro. The primary concern of robust analysis is that we don’t know the exact model of the economy and this model uncertainty has policy implications. Also, the math is neat.
Uncertainty about what the correct model is causes optimal policy to give heavy weight to worse-case scenarios.
Ellison and Sargent found a pretty neat application of robustness. The Fed staff are a bunch of academics who believe they know the true model of the economy. They use the “true” model to make forecasts and those forecasts are usually really good. The FOMC is the policy making branch of the Fed. They take the staff’s forecasts, produce their own forecasts and then set policy. It turns out the FOMC’s forecasts are worse than the staff’s. Those dumb policy makers, right?
Wrong. It turns out that because the FOMC is uncertain about the true model of the economy, they won’t take the staff’s model as the true model. The optimal response to this uncertainty would lead them to worry more about worse-cases. As Ellison and Sargent say, the FOMC “can be bad forecasters and good policymakers”.