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Learning generates Long Memory

Listed author(s):
  • Guillaume Chevillon

    (ESSEC Business School - Essec Business School)

  • Sophocles Mavroeidis

    (University of Oxford (UK) - University of Oxford [Oxford])

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 find 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 different 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.

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Paper provided by HAL in its series Post-Print with number hal-00661012.

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Date of creation: Oct 2013
Publication status: Published in ESSEC Working paper. Document de recherche de l'ESSEC / ISSN : 1291-9616 WP1113 Updated October 2013. 2013, pp.49
Handle: RePEc:hal:journl:hal-00661012
Note: View the original document on HAL open archive server: https://hal-essec.archives-ouvertes.fr/hal-00661012v2
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