A note on the representative adaptive learning algorithm

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.

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Year of publication:	2014
Authors:	Berardi, Michele ; Galimberti, Jaqueson K.
Published in:	Economics Letters. - Elsevier, ISSN 0165-1765. - Vol. 124.2014, 1, p. 104-107
Publisher:	Elsevier
Subject:	Expectations \| Learning algorithms \| Forecasting \| Learning-to-forecast \| Least squares \| Stochastic gradient

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Type of publication:	Article
Classification:	C53 - Forecasting and Other Model Applications ; D83 - Search, Learning, Information and Knowledge ; D84 - Expectations; Speculations ; E37 - Forecasting and Simulation
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010784969