Least-Squares Learning and the Stability of Equilibria with Externalities
研究代表性主体使用最小二乘预测时竞争均衡的稳定性，发现低就业稳态总是不稳定，高就业稳态在特定条件下稳定，削弱了协调失灵理论对持续高失业的解释。
This paper studies the stability of competitive equilibria in a model of aggregate employment when the representative agent uses a least-squares forecasting procedure. It is shown that the Pareto inferior low employment steady state is always unstable under least-squares learning, even if it is stable under perfect foresight. The high employment steady state is stable under learning if and only if it is saddle point stable under perfect foresight. This weakens multiple equilibrium theories of coordination failure that purport to explain persistently high unemployment. The Pareto superior high employment steady state will be the focal point of individual forecasting.