Abstract
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.
Original language | English |
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Pages (from-to) | 237-262 |
Number of pages | 26 |
Journal | International Economic Review |
Volume | 51 |
Issue number | 1 |
DOIs | |
Publication status | Published - Feb 2010 |
Keywords
- MONETARY-POLICY
- NASH INFLATION
- STABILITY
- EXPECTATIONS
- CONVERGENCE
- FRAMEWORK
- BELIEFS
- RULES
- MODEL