Inflation targeting as a monetary policy rule

The purpose of the paper is to survey and discuss inflation targeting in the context of monetary policy rules. The paper provides a general conceptual discussion of monetary policy rules, attempts to clarify the essential characteristics of inflation targeting, compares inflation targeting to the other monetary policy rules, and draws some conclusions for the monetary policy of the European system of Central Banks

Monetary Policy and Real Stabilization

Monetary policy can achieve average inflation equal to a given inflation target and, at best, a good compromise between inflation variability and output-gap variability. Monetary policy cannot completely stabilize either inflation or the output gap. Increased credibility in the form of inflation expectations anchored on the inflation target will reduce the variability of inflation and the output gap. Central banks can improve transparency and accountability by specifying not only an inflation target but also the dislike of output-gap variability relative to inflation variability. Central banks can best achieve both the long-run inflation target and the best compromise between inflation and output-gap stability by engaging in forecast targeting,' where the bank selects the feasible combination of inflation and output-gap projections that minimize the loss function and the corresponding instrument-rate plan and sets the instrument-rate accordingly. Forecast targeting implies that the instrument responds to all information that significantly affects the projections of inflation and the output gap. Therefore it cannot be expressed in terms of a simple instrument rule, like a Taylor rule. The objective of financial stability, including a well-functioning payment system, can conveniently be considered as a restriction on monetary policy that does not bind in normal times, but does bind in times of financial crises. By producing and publishing Financial Stability Reports with indicators of financial stability, the central bank can monitor the degree of financial stability and issue warnings to concerned agents and authorities in due time and this way avoid deteriorating financial stability. Forecast targeting implies that asset-price developments and potential asset-price bubbles are taken into account and responded to the extent that they are deemed to affect the projections of the target variables, inflation and the output gap. In most cases, it will be difficult to make precise judgments, though, especially to identify bubbles with reasonable certainty. The zero bound, liquidity traps and risks of deflation are serious concerns for a monetary policy aimed at low inflation. Forecast targeting with a symmetric positive inflation target keeps the risk of the zero bound, liquidity traps and deflation small. Prudent central banks may want to prepare in advance contingency plans for situations when a series of bad shocks substantially increases the risk

Monetary policy with model uncertainty: distribution forecast targeting

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. The form of model uncertainty our framework encompasses includes : simple i.i.d. model deviations; serially correlated model deviations; estimable regimeswitching models; more complex structural uncertainty about very different models, for instance, backward- and forward-looking models; time-varying central-bank 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." --Optimal policy,multiplicative uncertainty

Optimal Policy Projections

We outline a method to provide advice on optimal monetary policy while taking policymakers’ judgment into account. The method constructs optimal policy projections (OPPs) by extracting the judgment terms that allow a model, such as the Federal Reserve Board staff economic model, FRB/US, to reproduce a forecast, such as the Greenbook forecast. Given an intertemporal loss function that represents monetary policy objectives, OPPs are the projections — of target variables, instruments, and other variables of interest — that minimize that loss function for given judgment terms. The method is illustrated by revisiting the economy of early 1997 as seen in the Greenbook forecasts of February 1997 and November 1999. In both cases, we use the vintage of the FRB/US model that was in place at that time. These two particular forecasts were chosen, in part, because they were at the beginning and the peak, respectively, of the late 1990s boom period. As such, they differ markedly in their implied judgments of the state of the world in 1997 and our OPPs illustrate this difference. For a conventional loss function, our OPPs provide significantly better performance than Taylor-rule simulations.

Optimal Policy with Partial Information in a Forward-Looking Model: Certainty-Equivalence Redux

This paper proves a certainty equivalence result for optimal policy under commitment with symmetric partial information about the state of the economy in a model with forward-looking variables. This result is used in our previous paper, Indicator Variables for Optimal Policy,' which synthesizes what is known about the case of symmetric partial information, and derives useful general formulas for computation of the optimal policy response coefficients and efficient estimates of the state of the economy in the context of a fairly general forward-looking rational-expectations model. In particular, our proof takes into account that, under commitment, the policymaker can affect the future evolution of the observable variables, and thereby potentially affect the future information available.

Optimal Policy Projections

We outline a method to provide advice on optimal monetary policy while taking policymakers' judgment into account. The method constructs Optimal Policy Projections (OPPs) by extracting the judgment terms that allow a model, such as the Federal Reserve Board's FRB/US model, to reproduce a forecast, such as the Greenbook forecast. Given an intertemporal loss function that represents monetary policy objectives, OPPs are the projections - of target variables, instruments, and other variables of interest -that minimize that loss function for given judgment terms. The method is illustrated by revisiting the Greenbook forecasts of February 1997 and November 1999, in each case using the vintage of the FRB/US model that was in place at that time. These two particular forecasts were chosen, in part, because they were at the beginning and the peak, respectively, of the late 1990s boom period. As such, they differ markedly in their implied judgments of the state of the world, and our OPPs illustrate this difference. For a conventional loss function, our OPPs provide significantly better performance than Taylor-rule simulations.

Implementing Optimal Policy through Inflation-Forecast Targeting

We examine to what extent variants of inflation-forecast targeting can avoid stabilization bias, incorporate history-dependence, and achieve determinancy 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.

Monetary Policy with Judgment: Forecast Targeting

"Forecast targeting", forward-looking monetary policy that uses central-bank judgment to construct optimal policy projections of the target variables and the instrument rate, may perform substantially better than monetary policy that disregards judgment and follows a given instrument rule. This is demonstrated in a few examples for two empirical models of the U.S. economy, one forward looking and one backward looking. A complicated infinite-horizon central-bank projection model of the economy can be closely approximated by a simple finite system of linear equations, which is easily solved for the optimal policy projections. Optimal policy projections corresponding to the optimal policy under commitment in a timeless perspective can easily be constructed. The whole projection path of the instrument rate is more important than the current instrument setting. The resulting reduced-form reaction function for the current instrument rate is a very complex function of all inputs in the monetary-policy decision process, including the central bank’s judgment. It cannot be summarized as a simple reaction function such as a Taylor rule. Fortunately, it need not be made explicit.Inflation targeting; optimal monetary policy; forecasts