Least mean squares learning in self-referential linear stochastic models

Barucci, Emilio; Landi, Leonardo

doi:10.1016/s0165-1765(97)00202-4

Cited by 20 publications

(17 citation statements)

References 4 publications

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“…A computationally simpler alternative is offered by the Stochastic Gradient (SG) algorithm (Barucci and Landi, 1997;Evans and Honkapohja, 1998). We argue that the 1 previous literature has neglected the need of a realistic justification in the choice of the representative learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

A note on the representative adaptive learning algorithm

Berardi

Galimberti

2014

Economics Letters

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe 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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Section: Introductionmentioning

confidence: 99%

A note on the representative adaptive learning algorithm

Berardi

Galimberti

2014

Economics Letters

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“…3 For details on stochastic gradient learning, see Barucci and Landi (1997), Evans and Honkapohja (1998), and Evans et al (forthcoming). 4 See Bhagwati et al (1998) for a presentation of the classic Ricardian model.…”

Section: Article In Pressmentioning

confidence: 99%

“…14 For further discussion of SGL, see Barucci and Landi (1997), Evans and Honkapohja (1998), and Evans et al (forthcoming). 15 Evans and Honkapohja (2001) show how to discern stability under learning for this type of model under RLS, and Evans and Honkapohja (1998) do so for SGL.…”

Section: Article In Pressmentioning

confidence: 99%

Adaptive learning with a unit root: An application to the current account

Davies

Shea

2010

Journal of Economic Dynamics and Control

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“…An example of learning device that follows (6) is the so-called stochastic gradient learning, considered for instance in Barucci and Landi (1997) and Evans and Honkapohja (1998).…”

Section: Alternative Views On Error Learningmentioning

confidence: 99%