Adaptive Learning in Stochastic Nonlinear Models When Shocks Follow a Markov Chain
AbstractLocal 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.
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Bibliographic InfoPaper provided by University of Copenhagen. Department of Economics in its series Discussion Papers with number 03-22.
Length: 14 pages
Date of creation:
Date of revision: Apr 2003
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bounded rationality; recursive algorithms; steady state; linearization; asset pricing; overlapping generations;
Find related papers by JEL classification:
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- 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, and Information
This paper has been announced in the following NEP Reports:
- NEP-DGE-2003-07-04 (Dynamic General Equilibrium)
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