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

  • Seppo Honkapohja
  • Kaushik Mitra
Chapter
  • 312 Downloads
Part of the Studies in Economic Theory book series (ECON.THEORY, volume 25)

Summary

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.

Key words

Bounded rationality Recursive algorithms Steady state Linearization Asset pricing Overlapping generations 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seppo Honkapohja
    • 1
  • Kaushik Mitra
    • 2
  1. 1.Faculty of EconomicsUniversity of CambridgeCambridgeUK
  2. 2.Department of Economics, Royal HollowayUniversity of LondonEgham SurreyUK

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