Learning and monetary policy shifts

This paper estimates a dynamic stochastic equilibrium model in which monetary policy follows a nominal interest rate rule that is subject to regime switches in the target inflation rate. Two specifications are considered: agents know the current state of monetary policy (full information) and agents use Bayesian updating to infer the policy regime (learning). First, our policy regime estimates are consistent with the view that policy was marked by a shift to a high-inflation regime in the early 1970s which ended with Volcker's stabilization policy. Second, while Bayesian posterior odds favor the full-information version of the model, the fall of interest rates, actual and expected inflation in the early 1980s is better captured by the delayed response of the learning specification. Third, monetary policy shocks of up to two standard deviations essentially do not trigger the Bayesian learning mechanism. Yet due to nonlinearities, interventions that lead to small initial interest rate changes may be associated with much larger effects on output and inflation than under full information.