Optimal Monetary Policy with Real-time Signal Extraction from the Bond Market

Monetary policy is conducted in an environment of uncertainty. This paper sets up a model where the central bank uses real-time data from the bond market together with standard macroeconomic indicators to estimate the current state of the economy more efficiently, while taking into account that its own actions influence what it observes. The timeliness of bond market data allows for quicker responses of monetary policy to disturbances compared to the case when the central bank has to rely solely on collected aggregate data. The information content of the term structure creates a link between the bond market and the macroeconomy that is novel to the literature. To quantify the importance of the bond market as a source of information, the model is estimated on data for the United States and Australia using Bayesian methods. The empirical exercise suggests that there is some information in the US term structure that helps the Federal Reserve to identify shocks to the economy on a timely basis. Australian bond prices seem to be less informative than their US counterparts, perhaps because Australia is a relatively small and open economy.