Learning with Bounded Memory in Stochastic Models

Seppo Mikko Sakari Honkapohja () and Kaushik Mitra ()

University of Helsinki, Department of Economics from Department of Economics

Abstract: Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to an REE. The properties of the dunamics 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: ECONOMETRIC MODELS; LEARNING; PRICES; BUSINESS CYCLES (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 D83 E32 E37 (search for similar items in EconPapers)
Date: 1999
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Related works:
Working Paper: Learning with Bounded Memory in Stochastic Models
Working Paper: Learning with Bounded Memory in Stochastic Models (1999)
Journal Article: Learning with bounded memory in stochastic models (2003)
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