This experimental approach in macro follows the learning to forecast literature, notably developed by the team of Cars Hommes in Amsterdam.

The model is of course very simple, to keep things as general as possible. And the outcomes are provided by the model, conditional on the heterogeneous expectations formed by participants in the lab.

The main contribution of the experimental approach consists in designing comparable treatments by varying only a single feature at a time. In our case, we have two comparable treatments, namely whether a point or a band target is disclosed to participants. We can thus evaluate to what extend communication about band or point target matters for economic outcomes.

The replicability is also of interest.

Obviously, in the real world, it is extremely difficult to have so pure data and to bench test alternative policies. ]]>

The focus of the paper is on linearized DSGE models and, more generally, on linear(ized) Rational Expectations models which feature unobserved components.

The paper does not claim that the suggested bootstrap approach can be extended to nonlinear representations of dynamic equilibrium models. At first glance, the generalization to the class of nonlinear DSGE models seems puzzling (yet not impossible at all under certain conditions). But that is surely the topic of another paper.

It’s true that the “ever closer union” is a big uphill battle. Surprisingly though, unemployment insurance is regularly singled out in European policy circles as the most viable form of fiscal risk sharing — with the supranational element supplementing rather than replacing the national systems. That’s what initially motivated the paper. Thanks for mentioning it! ]]>