DP11004 Models, Inattention and Expectation Updates
|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.