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

Seppo Honkapohja () and Kaushik Mitra ()

No 03-22, Discussion Papers from University of Copenhagen. Department of Economics

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.

Keywords: bounded rationality; recursive algorithms; steady state; linearization; asset pricing; overlapping generations (search for similar items in EconPapers)
JEL-codes: C62 C61 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge
Date: 2003-04
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