GENERALIZED STOCHASTIC GRADIENT LEARNING

被引:31
|
作者
Evans, George W.
Honkapohja, Seppo
Williams, Noah [1 ]
机构
[1] Univ Wisconsin, Dept Econ, Madison, WI 53706 USA
基金
英国经济与社会研究理事会;
关键词
MONETARY-POLICY; NASH INFLATION; STABILITY; EXPECTATIONS; CONVERGENCE; FRAMEWORK; BELIEFS; RULES; MODEL;
D O I
10.1111/j.1468-2354.2009.00578.x
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:237 / 262
页数:26
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