Working Paper: CEPR ID: DP8917
Authors: George W. Evans; Seppo Honkapohja; Thomas J. Sargent; Noah Williams
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: Bayesian model averaging; learning; model selection; rational expectations equilibrium; time-varying perceptions
JEL Codes: D83; D84; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Expectations feedback (D84) | Model selection (C52) |
| Learning dynamics (C69) | Model selection (C52) |
| Expectations feedback (weak) (D84) | Convergence to REE (R13) |
| Expectations feedback (strong) (D84) | Convergence to non-REE model (C59) |
| Expectations feedback (D84) | Outcomes (I14) |