Recent studies show that the estimated parameters of rational expectations dynamic stochastic general equilibrium models of the business cycle are largely time-varying. This paper shows that assuming adaptive learning (rather than rational expectations) strongly reduces the estimated parameter variability of standard models (by around 75%). Moreover, the reduction in parameter variability induced by adaptive learning is much stronger for the subsets of parameters that control nominal price and wage rigidity and the subset of policy rule parameters (at 98% and 83%, respectively). Furthermore, our estimation results suggest that adaptive learning helps to explain the recent swings in the comovements between real and nominal US macroeconomic variables, but the swing in the relative weight of supply and demand shocks seems to be the most important driving force.

This paper extends the asymmetric preference model suggested by Ruge-Murcia (2003a) to investigate the use of real-time data which roughly measure the type of data available to policy makers when making their decisions and revised data which more accurately measure economic performance (Croushore, 2011). In our extended model, the central banker monitors a weighted average of revised and real-time inflation. Moreover, we allow for an asymmetric central bank focus on real-time inflation depending on whether the unemployment rate is high or low. Our model identifies a source of inflation bias due to inflation revisions. Our empirical results suggest that the Federal Reserve Bank focuses on monitoring revised inflation during low unemployment periods, but it weights real-time inflation heavily during high unemployment periods. In contrast, the Bank of England seems to focus on an equally-weighted average of real-time and revised inflation when monitoring inflation which is fairly robust over the business cycle.