Author(s):	Raffaella Giacomini, Vasiliki Skreta, Javier Turén
Publication Date:	December 2015
Keyword(s):	Bayesian learning, Disagreement, Expectation formation, Forecast accuracy, Herding, Heterogeneous agents, Information rigidities
JEL(s):	d80, d83, e27, e37
Programme Areas:	Monetary Economics and Fluctuations
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=11004

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.