We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...

The applied literature on adaptive learning has mostly focused on small, linear models, with homogenous expectations. In non-linear models heterogeneous expectations prevail and the process through which agents select (and change) a forecasting model becomes a necessary ingredient of the...