Learning of Steady States in Nonlinear Models when Shocks Follow a Markov Chain

Seppo Honkapohja, Kaushik Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Local convergence results for adaptive learning of stochastic steady states in nonlinear models are extended to the case where the exogenous observable variables follow a finite Markov chain. The stability conditions for the corresponding nonstochastic model and its steady states yield convergence for the stochastic model when shocks are sufficiently small. The results are applied to asset pricing and to an overlapping generations model. Large shocks can destabilize learning even if the steady state is stable with small shocks. Relationship to stationary sunspot equilibria are also discussed.
Original languageEnglish
Title of host publicationInstitutions, Equilibria, and Efficiency: Essays in Honour of Birgit Grodal
EditorsChristian Schultz, Karl Vind
PublisherSpringer Verlag
ISBN (Electronic)978-3-540-28161-0
ISBN (Print)978-3-540-28160-3
Publication statusPublished - 2006

Publication series

NameStudies in Economic Theory


  • Bounded rationality
  • Recursive algorithms
  • Steady state
  • Linearization
  • Asset pricing
  • Overlapping generations


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