DP8917 | Bayesian Model Averaging, Learning and Model Selection

Publication Date

01/03/2012

JEL Code(s)

Keyword(s)

Programme Area(s)

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.