Learning with Bounded Memory in Stochastic Models

Seppo Mikko Sakari Honkapohja () and Kaushik Mitra ()

Discussion Papers from Department of Economics, University of York

Abstract: Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to an rational expectations equilibrium (REE). The properties of the dynamics arising from such rules are studied for models with steady states. If in standard linear models the REE is in a certain sense expectationally stable (E-stable), then the dynamics are asymptotically stationary and forecasts are unbiased. We also provide similar local results for a class of nonlinear models with small noise and their approximations.

Keywords: Bounded memory; expectational stability; unbiased. (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 D83 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge and nep-evo
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http://www.york.ac.uk/depts/econ/documents/dp/0042.pdf (application/pdf)

Related works:
Working Paper: Learning with Bounded Memory in Stochastic Models (1999)
Working Paper: Learning with Bounded Memory in Stochastic Models (1999)
Journal Article: Learning with bounded memory in stochastic models (2003)
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Persistent link: http://EconPapers.repec.org/RePEc:yor:yorken:00/42

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