Open access
Datum
2014-04Typ
- Working Paper
ETH Bibliographie
yes
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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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-a-010131559Publikationsstatus
publishedZeitschrift / Serie
KOF Working PapersBand
Verlag
KOF Swiss Economic Institute, ETH ZurichThema
MACHINE LEARNING (ARTIFICIAL INTELLIGENCE); MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ); Least squares; Expectations; PROGRAMS AND ALGORITHMS FOR THE SOLUTION OF SPECIAL PROBLEMS; Forecasting; Learning algorithms; PROGRAMME UND ALGORITHMEN ZUR LÖSUNG SPEZIELLER PROBLEME; Stochastic gradient; Learning-to-forecastOrganisationseinheit
03716 - Sturm, Jan-Egbert / Sturm, Jan-Egbert
02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
ETH Bibliographie
yes
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