By providing guidance about future economic developments, central banks can affect private sector expectations and decisions. This can improve welfare by reducing private sector forecast errors, but it can also magnify the impact of noise in central bank forecasts. I employ a model of heterogeneous information to compare outcomes under opaque and transparent monetary policies. While better central bank information is always welfare improving, more central bank information may not be.
This paper develops a generalization of the formulas proposed by Kuttner (2001) and others for purposes of measuring the effects of a change in the federal funds target on Treasury yields of different maturities. The generalization avoids the need to condition on the date of the target change and allows for deviations of the effective fed funds rate from the target as well as gradual learning by market participants about the target. The paper shows that parameters estimated solely on the basis of the behavior of the fed funds and fed funds futures can account for the broad calendar regularities in the relation between fed funds futures and Treasury yields of different maturities. Although the methods are new, the conclusion is quite similar to that reported by earlier researchers"”changes in the fed funds target seem to be associated with quite large changes in Treasury yields, even for maturities of up to 10 years.
Monetary policy analysts often rely on rules of thumb, such as the Taylor rule, to describe historical monetary policy decisions and to compare current policy with historical norms. Analysis along these lines also permits evaluation of episodes where policy may have deviated from a simple rule and examination of the reasons behind such deviations. One interesting question is whether such rules of thumb should draw on policymakers' forecasts of key variables, such as inflation and unemployment, or on observed outcomes. Importantly, deviations of the policy from the prescriptions of a Taylor rule that relies on outcomes may be the result of systematic responses to information captured in policymakers' own projections. This paper investigates this proposition in the context of Federal Open Market Committee (FOMC) policy decisions over the past 20 years, using publicly available FOMC projections from the semiannual monetary policy reports to Congress (Humphrey-Hawkins reports). The results indicate that FOMC decisions can indeed be predominantly explained in terms of the FOMC's own projections rather than observed outcomes. Thus, a forecast-based rule of thumb better characterizes FOMC decisionmaking. This paper also confirms that many of the apparent deviations of the federal funds rate from an outcome-based Taylor-style rule may be considered systematic responses to information contained in FOMC projections.
This paper estimates a Bayesian vector autoregression for the U.S. economy that includes a housing sector and addresses the following questions: Can developments in the housing sector be explained on the basis of developments in real and nominal gross domestic product and interest rates? What are the effects of housing demand shocks on the economy? How does monetary policy affect the housing market? What are the implications of house price developments for the stance of monetary policy? Regarding the latter question, we implement a CÃ©spedes et al. (2006) version of a monetary conditions index.
This paper studies the design of optimal monetary policy under uncertainty using a Markov jump-linear-quadratic (MJLQ) approach. To approximate the uncertainty that policymakers face, the authors use different discrete modes in a Markov chain and take mode-dependent linear-quadratic approximations of the underlying model. This allows the authors to apply a powerful methodology with convenient solution algorithms that they have developed. They apply their methods to analyze the effects of uncertainty and potential gains from experimentation for two sources of uncertainty in the New Keynesian Phillips curve. The examples highlight that learning may have sizable effects on losses and, although it is generally beneficial, it need not always be so. The experimentation component typically has little effect and in some cases it can lead to attenuation of policy.