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:	2014-04
Authors:	Galimberti, Jaqueson ; Berardi, Michele
Institutions:	KOF Swiss Economic Institute, Department of Management, Technology and Economics (D-MTEC)
Subject:	expectations \| learning algorithms \| forecasting \| learning-to-forecast \| least squares \| stochastic gradient

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Extent:	application/pdf
Series:	KOF Working papers.
Type of publication:	Book / Working Paper
Notes:	Number 14-356 19 pages
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/10010762441