Search Results
Working Paper
Expectations, learning and the costs of disinflation: experiments using the FRB/US model
The costs of disinflation are explored using the Board's new sticky-price rational expectations macroeconometric model of the U.S. economy, FRB/US. The model nests both model consistent and `restricted-information rational' expectations. Monetary policy is governed by interest-rate reaction functions of which two are considered: the well-known Taylor rule and another rule that is more aggressive and richer in its specification, estimated using data for the last 15 years. Agents are required to learn of shifts of the inflation target using linear updating rules. The simulated costs of ...
Working Paper
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 ...
Working Paper
Robustifying learnability
In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought-after goals of policy design. Some contributions to the literature, including Bullard and Mitra (2001) and Evans and Honkapohja (2002), have made significant headway in establishing certain features of monetary policy rules that facilitate learning. However a treatment of policy design for learnability in worlds where agents have potentially misspecified their learning models has yet to surface. This paper ...
Working Paper
Avoiding Nash Inflation : Bayesian and Robust Responses to Model Uncertainty
We examine learning, model misspecification, and robust policy responses to misspecification in a quasi-real-time environment. The laboratory for the analysis is the Sargent (1999) explanation for the origins of inflation in the 1970s and the subsequent disinflation. Three robust policy rules are derived that differ according to the extent that misspecification is taken as a parametric phenomenon. These responses to drifting estimated parameters and apparent misspecification are compared to the certainty-equivalent case studied by Sargent. We find gains from utilizing robust approaches to ...
Discussion Paper
The Monetary Policy Response to Uncertain Inflation Persistence
This FEDS Note considers the implications of uncertainty regarding the persistence of inflation for the conduct of monetary policy.
Journal Article
Aggregate disturbances, monetary policy, and the macroeconomy: the FRB/US perspective
The FRB/US macroeconometric model of the U.S. economy was created at the Federal Reserve Board for use in policy analysis and forecasting. This article begins with an examination of the model's characterization of the monetary transmission mechanism -- the chain of relationships describing how monetary policy actions influence financial markets and, in turn, output and inflation. The quantitative nature of this mechanism is illustrated by estimates of the effect of movements in interest rates and other factors on spending in different sectors and by simulations of the effect of a change in ...
Working Paper
Simplicity versus optimality the choice of monetary policy rules when agents must learn
The monetary policy rules that are widely discussed--notably the Taylor rule--are remarkable for their simplicity. One reason for the apparent preference for simple ad hoc rules over optimal rules might be the assumption of full information maintained in the computation of an optimal rule. Arguably this makes optimal control rules less robust to model specification errors. In this paper, we drop the full-information assumption and investigate the choice of policy rules when agents must learn the rule that is in use. To do this, we conduct stochastic simulations on a small, estimated ...