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Learning with Bounded Memory in Stochastic Models

Kaushik Mitra () and Seppo Honkapohja ()

No 221, Computing in Economics and Finance 1999 from Society for Computational Economics

Abstract: There exists by now a sizeable literature that studies the dynamics of adaptive learning in stochastic macroeconomic models. A common starting point is to postulate that economic agents use standard econometric techniques to estimate the unknown parameters of the stochastic process of the relevant variables and forecast the future values using these estimated parameter values. A feature of learning is that, in the limit, agents are assumed to have access to an infinite amount of data. Our goal here, by contrast, is to analyze finite memory rules in stochastic economic models. We consider a wide variety of macroeconomic models, both linear and nonlinear, where agents are learning steady states. We study some basic issues here. Does the state of the economy have some invariant distribution in the long run? Is there convergence of the moments of the forecast? What is the influence of memory length on the residual variance of these forecasts? What can one say about these moments in nonlinear models? We provide answers to these questions for the models we analyze.

New Economics Papers: this item is included in nep-ets and nep-evo
Date: 1999-03-01
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Related works:
Journal Article: Learning with bounded memory in stochastic models (2003) Downloads
Working Paper: Learning with Bounded Memory in Stochastic Models (1999)
Working Paper: Learning with Bounded Memory in Stochastic Models Downloads
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More papers in Computing in Economics and Finance 1999 from Society for Computational Economics CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA. Contact information at EDIRC.
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