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Learning with Bounded Memory in Stochastic Models

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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.

Suggested Citation

  • Honkapohja, S. & Mitra, K., 1999. "Learning with Bounded Memory in Stochastic Models," University of Helsinki, Department of Economics 456, Department of Economics.
    • Handle: RePEc:fth:helsec:456
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    More about this item

    Keywords

    ECONOMETRIC MODELS ; LEARNING ; PRICES ; BUSINESS CYCLES;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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