Adaptive Learning with Nonlinear Dynamics Driven by Dependent Processes
为经济主体的自适应学习算法提供了收敛性理论，将Ljung(1977)框架扩展到允许非线性运动规律和中等依赖的随机过程，并给出易于验证的收敛条件。
We provide a convergence theory for adaptive learning algorithms useful for the study of learning by economic agents. Our results extend the framework of Ljung (1977) previously utilized by Marcet-Sargent (1989a,b) and Woodford (1990), by permitting nonlinear laws of motion driven by stochastic processes that may exhibit moderate dependence, such as mixing and mixingale processes. We draw on previous work by Kushner and Clark (1978) to provide readily verifiable and/or interpretable conditions ensuring algorithm convergence, chosen for their suitability in the context of adaptive learning.