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

来源：

INTERNATIONAL ECONOMIC REVIEW | 2010年 / 51卷 / 01期

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

页码：237 / 262

页数：26

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