Working Paper: CEPR ID: DP11004
Authors: Raffaella Giacomini; Vasiliki Skreta; Javier Turn
Abstract: We formulate a theory of expectation updating that fits the dynamics of accuracy and disagreement in a new survey dataset where agents can update at any time while observing each other's expectations. Agents use heterogeneous models and can be inattentive but, when updating, they follow Bayes' rule and assign homogeneous weights to public information. Our empirical findings suggest that agents do not herd and, despite disagreement, they place high faith in their models, whereas during a crisis they lose this faith and undergo a paradigm shift. This simple, "micro-founded" theory could enhance the explanatory power of macroeconomic and finance models.
Keywords: Bayesian learning; disagreement; expectation formation; forecast accuracy; herding; heterogeneous agents; information rigidities
JEL Codes: D80; D83; E27; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| attention (Y60) | accuracy of forecasts (C53) |
| economic environment (P42) | reliance on forecasting models (C53) |
| economic context (E66) | models used by agents (C52) |
| agents' forecasts vary (G17) | agents do not herd (C92) |
| 2008-2009 crisis (G01) | discard initial models (C52) |
| high faith in models (C51) | accurate forecasts (normal times) (C53) |
| faith declines (Z12) | shift in forecasting behavior (C53) |