Abstract:  This paper introduces information quality in a real business cycle model. Information quality relates to the idea that information obtained can inaccurately reflect the actual state of the economy. Using the Survey of Professional Forecasters, I document that forecast errors are larger during downturns, even if agents acquire more information. I then augment a rational inattention model with information search frictions that generate variable information quality. Information depends on both data abundance and information search intensity. Unlike rational inattention models, which are demand driven, I allow for time-varying data abundance, or information supply, generating fluctuations in information quality. The model delivers pro-cyclical information quality, which rationalizes puzzling evidence that information acquisition and uncertainty increase in downturns. A Bayesian estimation of the model for the US economy shows that information quality accounts for sizable fluctuations in uncertainty and output. The model also generates: (i) systematic mistakes when agents do not internalize fluctuations in information quality, (ii) variation in information processing costs, which produce higher frequency and dispersion in price changes during downturns, and (iii) production externalities, as firms do not internalize that more activity generates data abundance, which reduces uncertainty. 

Abstract: This paper examines how beliefs of tail risk events influence macroeconomic expectations in a Bayesian learning model with noisy signals. We show theoretically and empirically that the misperception of tail risk results in overreaction to first and second moment shocks, in contrast to a Gaussian model. First moment shocks cause excessive optimism and pessimism in individuals as they provide valuable information about tail risk. Second moment shocks lead to more pessimistic forecasts as higher uncertainty is linked to an increased likelihood of disasters. As signals become noisier, the response to news regarding a first moment shock becomes more pronounced. Our findings shed light on factors driving overreaction in expectations and highlight the importance of uncertainty shocks in propagating macroeconomic stability.

Abstract:  This paper examines how information frictions affect the conduct of macroprudential policy. Since macroprudential policy directly influences credit prices, it can impact beliefs when agents learn from credit spreads and experience policy misperception. We show that when agents do not accurately internalize and account for the policy effects on credit spreads, they mistakenly attribute changes in credit prices to the aggregate state. Consequently, agents erroneously update their beliefs about the aggregate state, even when the underlying aggregate state remains unchanged. Our findings suggest that instead of correcting externalities with policies, externalities can arise due to policy actions instead. The optimal policy adjustments depend on whether policy misperception and learning from credit spreads amplify or dampen the initial policy effect. To prevent sub-optimal outcomes, we highlight the importance of clear central bank communication.

*Previously circulated under the title "Learning from Prices, Credit Cycles, and Macroprudential Policies"