Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/242576 
Year of Publication: 
2020
Series/Report no.: 
Economics Working Paper Series No. 2020/04
Publisher: 
Auckland University of Technology (AUT), Faculty of Business, Economics and Law, Auckland
Abstract: 
This note evaluates how adaptive learning agents weigh different pieces of information when forming expectations with a recursive least squares algorithm. The analysis is based on a new and more general non-recursive representaion of the learning algorithm, namely, a penalized weighted least squares estimator, where a penalty term accounts for the effects of the learning initials. The paper then draws behavioral implications of diferent specifications of the learning mechanism, such as the cases with decreasing-, constant-, regime-switching, and age-dependent gains. The latter is shown to imply the emergence of "dormant memories" as the agents get old.
Subjects: 
bounded rationality
expectations
adaptive learning
memory
JEL: 
E70
D83
D84
D90
E37
C32
C63
Document Type: 
Working Paper

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