Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/102978
Authors: 
Berardi, Michele
Galimberti, Jaqueson K.
Year of Publication: 
2014
Series/Report no.: 
KOF Working Papers, KOF Swiss Economic Institute, ETH Zurich 356
Abstract: 
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 accuracy and in matching (future) survey forecasts.
Subjects: 
expectations
learning algorithms
forecasting
learning-to-forecast
least squares
stochastic gradient
JEL: 
C53
D83
D84
E03
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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