A note on the representative adaptive learning algorithm

Michele Berardi; Jaqueson K. Galimberti

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.

MoreLess

Year of publication:	2014
Authors:	Berardi, Michele ; Galimberti, Jaqueson K.
Publisher:	Zürich : KOF
Subject:	expectations \| learning algorithms \| forecasting \| learning-to-forecast \| least squares \| stochastic gradient \| Theorie \| Theory \| Prognoseverfahren \| Forecasting model \| Lernprozess \| Learning process \| Rationale Erwartung \| Rational expectations \| Algorithmus \| Algorithm \| Erwartungsbildung \| Expectation formation \| Kleinste-Quadrate-Methode \| Least squares method

freely available

Full text |

More access options

doi.org

Check Google Scholar

In German libraries (KVK)

I need help

Extent:	Online-Ressource (19 S.) graph. Darst.
Series:	KOF working papers. - Zürich : [Verlag nicht ermittelbar], ZDB-ID 2107617-0. - Vol. 356
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Arbeitspapier ; Working Paper ; Graue Literatur ; Non-commercial literature
Language:	English
Notes:	Systemvoraussetzungen: Acrobat Reader
Other identifiers:	10.3929/ethz-a-010131559 [DOI] hdl:10419/102978 [Handle]
Classification:	C53 - Forecasting and Other Model Applications ; D83 - Search, Learning, Information and Knowledge ; D84 - Expectations; Speculations ; e03 ; E37 - Forecasting and Simulation
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10010344932