Communication, learning and optimal monetary policy

The second part of the thesis deals with interest rate policy under inflation targeting when there is uncertainty in the term structure of interest rates emanating from unobserved, possibly volatile, market sentiments. In situations where expectations depend on the state of the economy--the rate of inflation and the level of the output gap, the central bank faces uncertainty about the degree of persistence in aggregate demand and inflation. Interestingly, the speed of learning about the degree of persistence depends on the interest rate policy followed and the resulting variability in inflation and the output gap, where higher variability speeds up learning and improves control of inflation in the long run. The analysis shows that passive and active learning scenarios have different implications for the degree of response of the rate of interest to the state of the economy and thus for the short-run conduct of monetary policy.

Learning about the Term Structure and Optimal Rules for Inflation Targeting

In this paper we incorporate the term structure of interest rates in a standard inflation forecast targeting framework.We find that under flexible inflation targeting and uncertainty in the degree of persistence in the economy, allowing for active learning possibilities has e®ects on the optimal interest rate rule followed by the central bank.For a wide range of possible initial beliefs about the unknown parameter, the dynamically optimal rule is in general more activist, in the sense of responding aggressively to the state of the economy, than the myopic rule for small to moderate deviations of the state variable from its target.On the other hand, for large deviations, the optimal policy is less activist than the myopic and the certainty equivalence policies.Learning;Rational Expectations;Separation Principle;Term Structure of Interest Rates

Communication, Learning and Optimal Monetary Policy.

The second part of the thesis deals with interest rate policy under inflation targeting when there is uncertainty in the term structure of interest rates emanating from unobserved, possibly volatile, market sentiments. In situations where expectations depend on the state of the economy--the rate of inflation and the level of the output gap, the central bank faces uncertainty about the degree of persistence in aggregate demand and inflation. Interestingly, the speed of learning about the degree of persistence depends on the interest rate policy followed and the resulting variability in inflation and the output gap, where higher variability speeds up learning and improves control of inflation in the long run. The analysis shows that passive and active learning scenarios have different implications for the degree of response of the rate of interest to the state of the economy and thus for the short-run conduct of monetary policy.

Learning About the Term Structure and Optimal Rules for Inflation Targeting

In this paper we incorporate the term structure of interest rates in a standard inflation forecast targeting framework. We find that under flexible inflation targeting and uncertainty in the degree of persistence in the economy, allowing for active learning possibilities has effects on the optimal interest rate rule followed by the central bank. For a wide range of possible initial beliefs about the unknown parameter, the dynamically optimal rule is in general more activist, in the sense of responding aggressively to the state of the economy, than the myopic rule for small to moderate deviations of the state variable from its target. On the other hand, for large deviations, the optimal policy is less activist than the myopic and the certainty equivalence policies.Learning;Rational Expectations;Separation Principle;Term Structure of Interest Rates

Heterogeneous Information about the Term Structure of Interest rates, Least-Squares Learning and Optimal Interest Rate Rules for Inflation Forecast Targeting

In this paper we incorporate the term structure of interest rates in a standard inflation forecast targeting framework.Learning about the transmission process of monetary policy is introduced by having heterogeneous agents - i.e. the central bank and private agents - who have different information sets about the future sequence of short-term interest rates.We analyse inflation forecast targeting in two environments.One in which the central bank has perfect knowledge, in the sense that it understands and observes the process by which private sector interest rate expectations are generated, and one in which the central bank has imperfect knowledge and has to learn the private sector forecasting rule for short-term interest rates.In the case of imperfect knowledge, the central bank has to learn about private sector interest rate expectations, as the latter affect the impact of monetary policy through the expectations theory of the term structure of interest rates.Here following Evans and Honkapohja (2001), the learning scheme we investigate is that of least-squares learning (recursive OLS) using the Kalman filter.We find that optimal monetary policy under learning is a policy that separates estimation and control.Therefore, this model suggests that the practical relevance of the breakdown of the separation principle and the need for experimentation in policy may be limited.information;term structure of interest rates;least squares;optimization;inflation;forecasting;learning;rational expectations;kalman filter