Expectation and confusion: Evidence and theory

In this paper, we characterize a forecasting model where forecasters cannot perfectly distinguish between the two persistent components (trends and cycles) in a dynamic setting. In this model, forecasters jointly update their beliefs about the two components: noisy information about one component is used to update beliefs about the other component. We present diagnostic empirical facts on forecasting behaviors and show that these facts are consistent with our model’s predictions while contradicting those of existing models in the expectation formation literature. To validate our model, we exploit the Federal Reserve’s 2012 adoption of explicit inflation targeting as a policy shock. Structural estimation reveals that this policy change altered the underlying data-generation process, and the corresponding changes in forecasting behavior indeed align with our model’s predictions. Finally, we characterize how this rational confusion mechanism interacts with behavioral biases such as overconfidence and show that this interaction can address the well-known persistent forecast error puzzle in the literature.