Monetary Policy under Data Uncertainty: Interest-Rate Smoothing from a Cross-Country Perspective

Abstract

Cross-country estimates of Taylor rules suggest that higher data uncertainty is associated with a more inertial behavior of interest rates. Data uncertainty is measured by the volatility of differences between real-time data and their revisions. Using a simple structural model with Kalman filter learning, I replicate the cross-country pattern of the inertial behavior. More inertial behavior results not because central banks gradually adjust interest rates in the face of data uncertainty, but because the central banks' inference about the true data is correlated with past interest rates. Thus, I endogenize the inertial behavior of interest rates as resulting in part from the learning process.