A Note on the Representative Adaptive Learning Algorithm
dc.contributor.author
Bernardi, Michele
dc.contributor.author
Galimberti, Jaqueson K.
dc.date.accessioned
2017-10-04T14:09:22Z
dc.date.available
2017-06-11T07:25:40Z
dc.date.available
2017-10-04T14:09:22Z
dc.date.issued
2014-04
dc.identifier.uri
http://hdl.handle.net/20.500.11850/82973
dc.identifier.doi
10.3929/ethz-a-010131559
dc.description.abstract
We compare forecasts from different adaptive learning algorithms and calibrations ap- plied 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 perfor- mance both in terms of forecasting accuracy and in matching (future) survey forecasts.
en_US
dc.format
application/pdf
dc.language.iso
en
en_US
dc.publisher
KOF Swiss Economic Institute, ETH Zurich
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
MACHINE LEARNING (ARTIFICIAL INTELLIGENCE)
en_US
dc.subject
MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ)
en_US
dc.subject
Least squares
en_US
dc.subject
Expectations
en_US
dc.subject
PROGRAMS AND ALGORITHMS FOR THE SOLUTION OF SPECIAL PROBLEMS
en_US
dc.subject
Forecasting
en_US
dc.subject
Learning algorithms
en_US
dc.subject
PROGRAMME UND ALGORITHMEN ZUR LÖSUNG SPEZIELLER PROBLEME
en_US
dc.subject
Stochastic gradient
en_US
dc.subject
Learning-to-forecast
en_US
dc.title
A Note on the Representative Adaptive Learning Algorithm
en_US
dc.type
Working Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2014
ethz.journal.title
KOF Working Papers
ethz.journal.volume
356
en_US
ethz.size
21 p.
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
en_US
ethz.identifier.nebis
010131559
ethz.publication.place
Zürich
en_US
ethz.publication.status
published
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ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03716 - Sturm, Jan-Egbert / Sturm, Jan-Egbert
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03716 - Sturm, Jan-Egbert / Sturm, Jan-Egbert
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
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ethz.date.deposited
2017-06-11T07:28:22Z
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ECOL
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ECIT
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imp59366b5a3dca425470
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eth:8530
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pub:130889
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yes
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Open access
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2017-07-13T02:48:40Z
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