Least mean squares learning in self-referential linear stochastic models

摘要: Abstract We analyze Self-Referential Linear Stochastic models under bounded rationality assuming that agents update their beliefs by means of the Least Mean Squares algorithm. This learning mechanism is less complex than Recursive Ordinary and appears to be more plausible as a device for economic agents. prove convergence mechanism, conditions are different from those required learning.