Learning can generate long memory
Guillaume Chevillon () and
Journal of Econometrics, 2017, vol. 198, issue 1, 1-9
We study learning dynamics in a prototypical representative-agent forward-looking model in which agents’ beliefs are updated using linear learning algorithms. We show that learning in this model can generate long memory endogenously, without any persistence in the exogenous shocks, depending 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. This is distinctly different from the case of rational expectations, where the memory of the endogenous variable is determined exogenously.
Keywords: Long memory; Recursive least squares; Decreasing gain learning; New Keynesian Phillips curve (search for similar items in EconPapers)
JEL-codes: C1 E3 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:198:y:2017:i:1:p:1-9
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