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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
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