Learning with bounded memory in stochastic models

✍ Scribed by Seppo Honkapohja; Kaushik Mitra

• Book ID: 104293311
• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 206 KB
• Volume: 27
• Category: Article
• ISSN: 0165-1889
• DOI: 10.1016/s0165-1889(02)00066-0

✦ Synopsis

Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to a rational expectations equilibrium (REE). The properties of dynamics arising from such rules are studied for standard models with steady states. If the REE in linear models is in a certain sense expectationally stable (E-stable), then the dynamics are asymptotically stationary and forecasts are unbiased, but the economy has excess volatility. We also provide similar local results for a class of nonlinear models with small noise.