Information weighting under least squares adaptive learning - Jaqueson K. Galimberti (Auckland University of Technology (AUT), Faculty of Business, Economics and Law, 2020)
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: E70; D83; D84; D90; E37; C32; C63; 330; bounded rationality; expectations; adaptive learning; memory