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: C32 C63 D83 D84 D90 E37 E70 (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: View citations in EconPapers (1)

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Related works:
Working Paper: Initial Beliefs Uncertainty and Information Weighting in the Estimation of Models with Adaptive Learning (2021)
Working Paper: Initial beliefs uncertainty (2021)
Working Paper: Information weighting under least squares learning (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:aut:wpaper:202004

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