Generalised Fading Memory Learning in a Cobweb Model: some evidence
AbstractWe develop a learning rule that generalises the well known fading memory learning in the sense that the weights attached to the available time series data are not constant and are updated in light of the prediction error(s). The underlying idea is that confidence in the available data will be low when large errors have been realized (e.g. in times of higher volatility) and vice versa. A class of functional forms compatible with this idea is analysed in the context of a standard Cobweb model with boundedly rational agents. We study the problem of convergence to the perfect foresight equilibrium (both local and global) and give conditions that ensure the coexistence of different attractors. We refer to both experimental and numerical evidence to establish the possible range of application of the generalised fading memory learning
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 272.
Date of creation: 11 Aug 2004
Date of revision:
bounded rationality; learning dynamics; fading memory; nonlinear cobweb model.;
Find related papers by JEL classification:
- C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-08-02 (All new papers)
- NEP-CMP-2004-07-26 (Computational Economics)
- NEP-FIN-2004-08-02 (Finance)
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