Optimal monetary policy revisited: does considering US real-time data change things?

The primary goal of this article is to investigate whether properly modelling real-time data and optimal real-time decision-making of a monetary planner provides new insights into monetary policy behaviour and outcomes. This article extends a variant of the asymmetric preference model suggested by Ruge-Murcia to investigate the use of real-time data available to policymakers when making their decisions and revised data which more accurately measure economic performance, but is only available much later. In our extended model, the central banker targets a weighted average of revised and real-time inflation together with a weighted average of revised and real-time output. Moreover, we allow for an asymmetric central bank response to real-time data depending on whether the unemployment rate is high or low. Our model identifies several new potential sources of inflation bias due to data revisions. Our empirical results suggest that the Federal Reserve Bank focuses on targeting revised inflation during low unemployment periods, but it weighs heavily real-time inflation during high unemployment periods. The inflation bias due to data revisions is comparable in magnitude to the bias from asymmetric central banker preferences with the bias being somewhat larger during high unemployment.