Benefits of Gradualism or Costs of Inaction? Monetary Policy in Times of Uncertainty

Abstract

Should monetary policy be more aggressive or more cautious when facing uncertainty on the relationship between macroeconomic variables? This paper's answer is: “it depends” on the degree of persistence of the shocks that hit the economy. The paper studies optimal monetary policy in a basic (two-equation) forward looking New-Keynesian (NK) framework with random parameters. It relaxes the assumption of full central bank information in two ways: by allowing for uncertainty on the model parameters and by assuming asymmetric information. While the private sector observes the realizations of the random process of the parameters as they occur, the central bank observes them with a one period delay. Compared to the problem with full information, the monetary authority must solve the Bayesian decision problem of minimizing the expected stream of future welfare losses integrating over its prior probability distribution of the unknown parameters. The paper proposes a general method to account for uncertainty on any subset of parameters of the model. As an application, it focuses on two cases: uncertainty on the natural rate of interest and on the slope of the Phillips curve.