Economic Dynamics with Learning: New Stability Results
利用统计文献中的新成果，给出了递归随机算法收敛的概率界，可用于分析随机模型中理性预期均衡在局部学习下的稳定性，并提供了多元线性模型和唯一均衡模型的例子。
Drawing upon recent contributions in the statistical literature, we present new results on the convergence of recursive, stochastic algorithms which can be applied to economic models with learning and which generalize previous results. The formal results provide probability bounds for convergence which can be used to describe the local stability under learning of rational expectations equilibria in stochastic models. Economic examples include local stability in a multivariate linear model with multiple equilibria and global convergence in a model with a unique equilibrium.