详情

原文传递 Uncertainty, Learning, and Gradual Monetary Policy

题名：	Uncertainty, Learning, and Gradual Monetary Policy
作者：	Brian Sack
关键词：	learning；policy；onet；rtai；neta；ncert；etar；dual；smoothness；optimal
摘要：	This paper argues that interest-rate smoothing may be optimal when the effect of monetary policy is uncertain. A model is presented in which the Federal Reserve rationally learns about the policy multiplier by observing the reaction of the economy to recent choices of the interest rate. As a result of this learning process, the Fed faces greater uncertainty about the impact of its policy as it moves the interest rate away from its previous level. The optimal policy response to macroeconomic developments therefore involves gradual adjustment of the interest rate over a period of time during which the Fed is learning about the effect of its policy, consistent with the smoothness of interest rate movements found in estimated policy rules. The model also suggests that periods of active interest rate movements, by allowing the Fed to learn more effectively, may be followed by a more aggressive policy rule
报告类型：	科技报告