Economics at your fingertips  

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 (10) Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
Journal Article: A note on the representative adaptive learning algorithm (2014) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in KOF Working papers from KOF Swiss Economic Institute, ETH Zurich Contact information at EDIRC.
Bibliographic data for series maintained by ().

Page updated 2021-04-08
Handle: RePEc:kof:wpskof:14-356