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

Seppo Mikko Sakari 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
View list of references

Downloads: (external link)
http://www.econ.ku.dk/Research/Publications/pink/2003/0322.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Discussion Papers from University of Copenhagen. Department of Economics
Address: Studiestraede 6, DK-1455 Copenhagen K., Denmark
Contact information at EDIRC.
Series data maintained by Henriette Aabo Hansen ().