Convergence of Least-Squares Learning in Environments with Hidden State Variables and Private Information
研究存在私人信息的经济环境中递归最小二乘学习方案的收敛性，证明在特定条件下系统会收敛到理性预期均衡，并应用于Townsend模型。
The authors study the convergence of recursive least-squares learning schemes in economic environments in which there is private information. The presence of private information leads to the presence of hidden state variables from the viewpoint of particular agents. By applying theorems of Lennart Ljung, the authors extend some of their earlier results to characterize conditions under which a system governed by least-squares learning will eventually converge to a rational expectations equilibrium. They apply insights from the learning results to formulate and compute the equilibrium of a version of Robert Townsend's model. Copyright 1989 by University of Chicago Press.