GENERALIZED STOCHASTIC GRADIENT LEARNING*
George W. Evans, Seppo Honkapohja, Noah R. Williams · International Economic Review · 2010
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.