A State Space Approach To The Policymaker's Data Uncertainty Problem

The paper describes the challenges that uncertainty over the true value of key macroeconomic variables poses for policymakers and the way in which they may form and update their priors in light of a range of indicators. Speci cally, it casts the data uncertainty challenge in state space form and illustrates - in this setting - how the policymaker’s data uncertainty problem is related to any constraints that an optimising statistical agency might face in resolving its own data uncertainty challenge. The paper uses this intuition to motivate a set of identifying assumptions that might be used in the practical application of the Kalman Filter to form and update priors on the basis of a variety of indicators. In doing so, it moves beyond the simple methodology for deriving "best guesses" of the true value of economic variables outlined in Ashley, Driver, Hayes, and Je¤ery (2005)