Bayesian Model Averaging, Learning and Model Selection
27 Pages Posted: 4 Apr 2012
Date Written: March 2012
Abstract
Agents have two forecasting models, one consistent with the unique rational expectations equilibrium, another that assumes a time-varying parameter structure. When agents use Bayesian updating to choose between models in a self-referential system, we find that learning dynamics lead to selection of one of the two models. However, there are parameter regions for which the non-rational forecasting model is selected in the long-run. A key structural parameter governing outcomes measures the degree of expectations feedback in Muth's model of price determination.
Keywords: grain of truth, rational expectations equilibrium, Time-varying perceptions
JEL Classification: D83, D84, E37
Suggested Citation: Suggested Citation