.png){kind=icon}
.png){kind=icon}
Return
Learning can generate long memory
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2017.01.001.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Long memory
Recursive least squares
Decreasing gain learning
New Keynesian Phillips curve
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W