A note on the representative adaptive learning algorithm
Michele Berardi () and
Jaqueson Galimberti ()
Economics Letters, 2014, vol. 124, issue 1, 104-107
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.
Keywords: Expectations; Learning algorithms; Forecasting; Learning-to-forecast; Least squares; Stochastic gradient (search for similar items in EconPapers)
JEL-codes: C53 D83 D84 E37 (search for similar items in EconPapers)
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Working Paper: A Note on the Representative Adaptive Learning Algorithm (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:124:y:2014:i:1:p:104-107
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