LEARNING, COMMITMENT, AND MONETARY POLICY

Abstract

This paper examines a class of interest rate rules that respond to public expectations and to lagged variables and considers varying levels of commitment that correspond to varying degrees of response to lagged output. Under commitment the policymaker adjusts the nominal rate with lagged output to impact public expectations. Within this class of rules, I provide a condition for nonexplosive and determinate solutions. Expectational stability obtains for any nonnegative response to lagged output. Simulation results show that modified commitment is best under least-squares learning. However, in the presence of parameter uncertainty and/or measurement error in the policymaker's data on public expectations, the best policy is one of partial commitment, where the response to lagged output is less than under modified commitment. The case for partial commitment is strengthened if the gain parameter in the learning mechanism is high, which can be interpreted as the use of few lags by public agents in the formation of expectations or as an indication of low credibility of the policymaker. The appointment of a conservative central banker ameliorates these concerns about modified commitment.