A Note on the Representative Adaptive Learning Algorithm

Michele Bernardi and Jaqueson Galimberti
Authors registered in the RePEc Author Service: Michele Berardi

No 14-356, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

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.

Keywords: Expectations; Learning algorithms; Forecasting; Learning-to-forecast; Least squares; Stochastic gradient (search for similar items in EconPapers)
Pages: 21 pages
Date: 2014-04
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Journal Article: A note on the representative adaptive learning algorithm (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:kof:wpskof:14-356

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