The purpose of this paper is to survey and discuss inflation targeting in the context of monetary policy rules, to clarify the essential characteristics of in‡ation targeting, to compare inflation targeting to other monetary policy rules, and to draw some conclusions for the monetary policy of

The paper extends previous analysis of closed-economy inflation targeting to a small open economy with forward-looking aggregate supply and demand with some microfoundations, and with stylized realistic lags in the different transmission channels for monetary policy. The paper compares targeting of CPI and domestic inflation, strict and exible inflation targeting, and inflation-targeting reaction functions and the Taylor rule. The optimal monetary policy response to several different shocks is examined. Flexible CPI-inflation targeting stands out as successful in limiting not only the variability of CPI inflation but also the variability of the output gap and the real exchange rate. Somewhat counter to conventional wisdom, negative productivity supply shocks and positive demand shocks have similar effects on inflation and the output gap, and induce similar monetary policy responses. The model gives limited support for a so-called monetary conditions index, MCI, of the monetary-policy impact on aggregate d...

We examine to what extent variants of inflation-forecast targeting can avoid stabilization bias, incorporate history-dependence, and achieve determinacy of equilibrium, so as to reproduce a socially optimal equilibrium. We also evaluate these variants in terms of the transparency of the connection with the ultimate policy goals and the robustness to model perturbations. A suitably designed inflation-forecast targeting rule can achieve the social optimum and at the same time have a more transparent connection to policy goals and be more robust than competing instrument rules.

Inflation targeting is a monetary-policy strategy that is characterized by an announced numerical inflation target, an implementation of monetary policy that gives a major role to an inflation forecast and has been called forecast targeting, and a high degree of transparency and accountability. It was introduced in New Zealand in 1990, has been very successful in terms of stabilizing both inflation and the real economy, and has, as of 2010, been adopted by about 25 industrialized and emerging-market economies. The chapter discusses the history, macroeconomic effects, theory, practice, and future of inflation targeting.

The optimal weights on indicators in models with partial information about the state of the economy and forward-looking variables are derived and interpreted, both for equilibria under discretion and under commitment. An example of optimal monetary policy with a partially observable potential output and a forward-looking indicator is examined. The optimal response to the optimal estimate of potential output displays certainty-equivalence, whereas the optimal response to the imperfect observation of output depends on the noise in this observation.

Previous analysis of the implementation of inflation targeting is extended to monetary policy responses to different shocks, consequences of model uncertainty, effects of interest rate smoothing and stabilization, a comparison with nominal GDP targeting, and implications of forward-looking behavior. Model uncertainty, output stabilization, and interest rate stabilization or smoothing all call for a more gradual adjustment of the conditional ination forecast toward the inflation target. The conditional ination forecast is the natural intermediate target during inflation targeting.

We consider a general class of nonlinear optimal policy problems involving forward-looking constraints (such as the Euler equations that are typically present as structural equations in DSGE models), and show that it is possible, under regularity conditions that are straightforward to check, to derive a problem with linear constraints and a quadratic objective that approximates the exact problem. The LQ approximate problem is computationally simple to solve, even in the case of moderately large state spaces and flexibly parameterized disturbance processes, and its solution represents a local linear approximation to the optimal policy for the exact model in the case that stochastic disturbances are small enough. We derive the second-order conditions that must be satisfied in order for the LQ problem to have a solution, and show that these are stronger, in general, than those required for LQ problems without forwardlooking constraints. We also show how the same linear approximations to the model structural equations and quadratic approximation to the exact welfare measure can be used to correctly rank alternative simple policy rules, again in the case of small enough shocks.

We examine optimal and other monetary policies in a linear-quadratic setup with a relatively general form of model uncertainty, so-called Markov jump-linear-quadratic systems extended to include forward-looking variables and unobservable “modes. ” The form of model uncertainty our framework encompasses includes: simple i.i.d. model deviations; serially correlated model deviations; estimable regime-switching models; more complex structural uncertainty about very different models, for instance, backward- and forward-looking models; time-varying centralbank judgment about the state of model uncertainty; and so forth. We provide an algorithm for finding the optimal policy as well as solutions for arbitrary policy functions. This allows us to compute and plot consistent distribution forecasts—fan charts—of target variables and instruments. Our methods hence extend certainty equivalence and “mean forecast targeting ” to more general certainty non-equivalence and “distribution forecast targeting.” JEL Classification: E42, E52, E58

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The optimal weights on indicators in models with partial information about the state of the economy and forward-looking variables are derived and interpreted, both for equilibria under discretion and under commitment. The private sector is assumed to have more information about the state of the economy than the policymaker. Certainty equivalence holds for the optimal reaction function in state-space form: thecoefficients are independent of the degree of uncertainty about the state of the economy. However, in the case of commitment, certainty equivalence does not hold for the reaction function in integrative form: the policy instrument cannot be written as a distributed lag of past estimates of past states of the economy. Instead, optimal policy will generally depend on new estimates of past states of the economy, and in ways that depend on the information structure. Furthermore, the usual separation principle does not hold, since the estimation of the state of the economy is not independent of optimization and is, in general, quite complex. We present a general characterization of optimal filtering and control in settings of this kind, and discuss an application of our methods to the problem of the optimal use of “real-time” macroeconomic data in the conduct of monetary policy.