Publication | Open Access
Heterogeneity, Inattention, and Bayesian Updates
54
Citations
21
References
2020
Year
Bayesian StatisticBayesian Decision TheoryEngineeringUncertain ReasoningBayesian InferenceCausal InferenceProbabilistic ForecastingData ScienceUncertainty QuantificationManagementPersistent DisagreementBayesian MethodsBayes ’ RuleDecision TheoryStatisticsForecastingPredictabilityReasoningBayesian UpdatesBayesian StatisticsProfessional ForecastersStatistical InferenceModel Uncertainty
We formulate a theory of expectations updating that fits the dynamics of accuracy and disagreement in a new survey of professional forecasters. We document new stylized facts, including the puzzling persistence of disagreement as uncertainty resolves. Our theory explains these facts by allowing for different channels of heterogeneity. Agents produce an initial forecast based on heterogeneous priors and are heterogeneously “inattentive.” Updaters use Bayes’ rule and interpret public information using possibly heterogeneous models. Structural estimation of our theory supports the conclusion that in normal times heterogeneous priors and inattention are enough to generate persistent disagreement, but not during the crisis. (JEL C53, D81, D83, D84, E31, E37)
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