Learning with bounded memory in stochastic models

Seppo Honkapohja, Kaushik Mitra

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to a rational expectations equilibrium (REE). The properties of dynamics arising from such rules are studied for standard models with steady states. If the REE in linear models is in a certain sense expectationally stable (E-stable), then the dynamics are asymptotically stationary and forecasts are unbiased, but the economy has excess volatility. We also provide similar local results for a class of nonlinear models with small noise.
Original languageEnglish
Pages (from-to)1437-1457
JournalJournal of Economic Dynamics and Control
Issue number8
Early online date16 May 2002
Publication statusPublished - Jun 2003


  • Convergence of learning
  • Stability
  • Excess volatility


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