A note on the representative adaptive learning algorithm

摘要

We compare forecasts from different adaptive learning algorithms and calibrations applied to US realtime 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 accuracy and in matching (future) survey forecasts.