Local Convergence of Recursive Learning to Steady States and Cycles in Stochastic Nonlinear Models - (Now published in 'Econometrica', vol.63 (1995), pp.195-206.)

George Evans and Seppo Honkapohja

STICERD - Theoretical Economics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: We examine a recursive algorithm for learning steady states and cycles in stochastic nonlinear models. Necessary and sufficient conditions for local convergence are shown to be equivalent to easily computable expectational-stability conditions. These conditions are affected by the distribution of the random shocks. For the case of small noise it is shown that stochastic cycles exist near nonstochastic ones and that a projection facility in the algorithm is not required for convergence with probability 1 to stable steady states and cycles. The results are applied to an overlapping generations model with productivity shocks.

Date: 1992-05
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cep:stitep:236

Access Statistics for this paper

More papers in STICERD - Theoretical Economics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE
Bibliographic data for series maintained by ().