Learning generates Long Memory
Guillaume Chevillon () and
No WP1113, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School
We consider a prototypical representative-agent forward-looking model, and study the low frequency variability of the data when the agent's beliefs about the model are updated through linear learning algorithms. We nd that learning in this context can generate strong persistence. The degree of persistence depends on the weights agents place on past observations when they update their beliefs, and on the magnitude of the feedback from expectations to the endogenous variable. When the learning algorithm is recursive least squares, long memory arises when the coefficient on expectations is sufficiently large. In algorithms with discounting, long memory provides a very good approximation to the low-frequency variability of the data. Hence long memory arises endogenously, due to the self-referential nature of the model, without any persistence in the exogenous shocks. This is distinctly di erent from the case of rational expectations, where the memory of the endogenous variable is determined exogenously. Finally, this property of learning is used to shed light on some well-known empirical puzzles.
Keywords: Learning; Long Memory; Persistence; Present-Value Models (search for similar items in EconPapers)
Pages: 55 pages
New Economics Papers: this item is included in nep-cba, nep-dge and nep-mic
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Working Paper: Learning generates Long Memory (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:essewp:dr-11013
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