GENERALIZED STOCHASTIC GRADIENT LEARNING*
研究了前瞻性模型中广义随机梯度（GSG）学习的性质，给出了GSG学习收敛到理性预期均衡的条件，这些条件与最小二乘学习的稳定性条件相关但不同。
We study the properties of the generalized stochastic gradient (GSG) learning in forward‐looking models. GSG algorithms are a natural and convenient way to model learning when agents allow for parameter drift or robustness to parameter uncertainty in their beliefs. The conditions for convergence of GSG learning to a rational expectations equilibrium are distinct from but related to the well‐known stability conditions for least squares learning.