A note on the representative adaptive learning algorithm

Michele Berardi, Jaqueson K. Galimberti

Research output: Contribution to journalArticlepeer-review


We 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. © 2014 Elsevier B.V.
Original languageEnglish
Pages (from-to)104-107
Number of pages3
JournalEconomics Letters
Issue number1
Publication statusPublished - 2014


  • Expectations
  • Forecasting
  • Learning algorithms
  • Learning-to-forecast
  • Least squares
  • Stochastic gradient


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