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Adaptive Learning in Stochastic Nonlinear Models When Shocks Follow a Markov Chain

Author

Listed:
  • Seppo Honkapohja

    (University of Helsinki)

  • Kaushik Mitra

    (Royal Holloway College, University of London)

Abstract

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 ?nite Markov chain. The stability conditions for the corresponding nonstochastic model and its steady states yield convergence for the stochastic model when shocks are suf?ciently 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.

Suggested Citation

  • Seppo Honkapohja & Kaushik Mitra, "undated". "Adaptive Learning in Stochastic Nonlinear Models When Shocks Follow a Markov Chain," Discussion Papers 03-22, University of Copenhagen. Department of Economics, revised Apr 2003.
  • Handle: RePEc:kud:kuiedp:0322
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    File URL: http://www.econ.ku.dk/english/research/publications/wp/2003/0322.pdf/
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    More about this item

    Keywords

    bounded rationality; recursive algorithms; steady state; linearization; asset pricing; overlapping generations;

    JEL classification:

    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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