Economics at your fingertips  

Information weighting under least squares adaptive learning

Jaqueson Galimberti ()

No 2020-04, Working Papers from Auckland University of Technology, Department of Economics

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.

Keywords: bounded rationality; expectations; adaptive learning; memory (search for similar items in EconPapers)
JEL-codes: E70 D83 D84 D90 E37 C32 C63 (search for similar items in EconPapers)
Date: 2020-04
New Economics Papers: this item is included in nep-age, nep-ets, nep-mac, nep-ore and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working Papers from Auckland University of Technology, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Gail Pacheco ().

Page updated 2021-04-13
Handle: RePEc:aut:wpaper:202004